Log in

Relevant bibliographies by topics / Recommender System (RS) / Journal articles

To see the other types of publications on this topic, follow the link: Recommender System (RS).

Journal articles on the topic 'Recommender System (RS)'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Recommender System (RS).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Walia, Prof Ranjanroop. "Online Recommender System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 2569–77. http://dx.doi.org/10.22214/ijraset.2021.36424.

Full text

Abstract:

As the size of the e-commerce market grows, the consequences of it are appearing throughout society.The business Environment of a company changes from a product center to a user center and introduces a recommendation system. However, the existing research has shown a limitation in deriving customized recommendation information to reflect the detailed information that users consider when purchasing a product. Therefore, the proposed system reflects the users subjective purchasing criteria in the recommendation algorithm. And conduct sentiment analysis of product review data. Finally, the final

APA, Harvard, Vancouver, ISO, and other styles

2

Lahlou, Fatima Zahra, Houda Benbrahim, and Ismail Kassou. "Review Aware Recommender System." International Journal of Distributed Artificial Intelligence 10, no. 2 (2018): 28–50. http://dx.doi.org/10.4018/ijdai.2018070102.

Full text

Abstract:

Context aware recommender systems (CARS) are recommender systems (RS) that provide recommendations according to user contexts. The first challenge for building such a system is to get the contextual information. Some works tried to get this information from reviews provided by users in addition to their ratings. However, all of these works perform important feature engineering in order to infer the context. In this article, the authors present a new CARS architecture that allows to automatically use contextual information from reviews without requiring any feature engineering. Moreover, they d

APA, Harvard, Vancouver, ISO, and other styles

3

Bajenaru, Victor, Steven Lavoie, Brett Benyo, Christopher Riker, Mitchell Colby, and James Vaccaro. "Recommender System Metaheuristic for Optimizing Decision-Making Computation." Electronics 12, no. 12 (2023): 2661. http://dx.doi.org/10.3390/electronics12122661.

Full text

Abstract:

We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set’s optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1)

APA, Harvard, Vancouver, ISO, and other styles

4

Kang, Li Ting, and Yong Wang. "Seven Factors in Evaluating Recommender System." Applied Mechanics and Materials 472 (January 2014): 443–49. http://dx.doi.org/10.4028/www.scientific.net/amm.472.443.

Full text

Abstract:

Recommender system (RS) has been evaluated in many but incomparable ways beyond accuracy and thus proposing an evaluation framework to synthesize the existing strategies seems a solution. However, few scholars did it so far. Through literature review, user interview and expert assessment, this study proposed a theoretical evaluation model of RS and then formed the assessment tool, RS Evaluation Questionnaire (RSE). The results showed that RSE was an effective tool to evaluate a recommender system, with its reliability (Cronbachs α=0.803) and validity meeting the requirements of psychometrics.

APA, Harvard, Vancouver, ISO, and other styles

5

Kumar Sahni, Dheeraj. "Recommender System (RS): Challenges, Issues & Extensions." Mapana Journal of Sciences 21, no. 1 (2022): 73–92. http://dx.doi.org/10.12723/mjs.60.6.

Full text

Abstract:

Recommendations are long chains followed from traditional life to today’s life. In everyday life, the chain of recommendation augments the social process via some physical media and digital applications. The issues and challenges of recommendation are still in the infancy due to the growth of technology. This article identifies the uncovered areas of concern and links them to novel solutions. We also provide an extensive literature with different dimension for the newbie to work with the subject. We observed the study with different taxonomy provided by the prevalent researcher of the recommen

APA, Harvard, Vancouver, ISO, and other styles

6

Vaidhehi, V., and R. Suchithra. "A Systematic Review of Recommender Systems in Education." International Journal of Engineering & Technology 7, no. 3.4 (2018): 188. http://dx.doi.org/10.14419/ijet.v7i3.4.16771.

Full text

Abstract:

Recommender system (RS)s are widely used in different walks of life. This research work is to explore the usage of RS in the field of education. This review is performed in five dimensions which includes, Purpose of RS in Education, various techniques to build RS, input parameters used in design of RS, type of students involved in design of RS and Modelling strategies for RS to represent the data. The outcome of the research work is to facilitate the efficient design of the recommender system in education which will help the students by generating the appropriate recommendations.

APA, Harvard, Vancouver, ISO, and other styles

7

Usman, Abdulgafar, Abubakar Roko, Aminu B. Muhammad, and Abba Almu. "Enhancing Personalized Book Recommender System." International Journal of Advanced Networking and Applications 14, no. 03 (2022): 5486–92. http://dx.doi.org/10.35444/ijana.2022.14311.

Full text

Abstract:

Recommender systems (Rs) are widely used to provide recommendations for a collection of items or products that may be of interest to user or a group of users. Because of its superior performance, Content-Based Filtering (CBF) is one of the approaches that are commonly utilized in real-world Rs using Time-Frequency and Inverse Document Frequency (TF-IDF) to calculate document similarities. However, it computes document similarity directly in the word-count space. We propose a user-based collaborative filtering (UBCF) method to solve the problem of limited in content analysis which leads to a lo

APA, Harvard, Vancouver, ISO, and other styles

8

Batra, Priya, Anukriti Singh, and T. S. Mahesh. "Efficient Characterization of Quantum Evolutions via a Recommender System." Quantum 5 (December 6, 2021): 598. http://dx.doi.org/10.22331/q-2021-12-06-598.

Full text

Abstract:

We demonstrate characterizing quantum evolutions via matrix factorization algorithm, a particular type of the recommender system (RS). A system undergoing a quantum evolution can be characterized in several ways. Here we choose (i) quantum correlations quantified by measures such as entropy, negativity, or discord, and (ii) state-fidelity. Using quantum registers with up to 10 qubits, we demonstrate that an RS can efficiently characterize both unitary and nonunitary evolutions. After carrying out a detailed performance analysis of the RS in two qubits, we show that it can be used to distinguis

APA, Harvard, Vancouver, ISO, and other styles

9

Yadav, Dharminder, Himani Maheshwari, and Umesh Chandra. "An Approach Towards Hotel Recommender System." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2605–12. http://dx.doi.org/10.1166/jctn.2020.8936.

Full text

Abstract:

Recommendation Systems (RS) suggest the right item to the right user. It predicts the user’s rating to an item and based on this rating RS provides the suggestion to users. In today’s world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable hotel in a given geographical area according to the user query by using library “recommenderlab” in R. This study proposed a system that gives the best hotel availabl

APA, Harvard, Vancouver, ISO, and other styles

10

Nugroho, Arseto Satriyo, Igi Ardiyanto, and Teguh Bharata Adji. "User Curiosity Factor in Determining Serendipity of Recommender System." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 3 (2021): 75. http://dx.doi.org/10.22146/ijitee.67553.

Full text

Abstract:

Recommender rystem (RS) is created to solve the problem by recommending some items among a huge selection of items that will be useful for the e-commerce users. RS prevents the users from being flooded by information that is irrelevant for them.Unlike information retrieval (IR) systems, the RS system's goal is to present information to the users that is accurate and preferably useful to them. Too much focus on accuracy in RS may lead to an overspecialization problem, which will decrease its effectiveness. Therefore, the trend in RS research is focusing beyond accuracy methods, such as serendip

APA, Harvard, Vancouver, ISO, and other styles

11

Hdioud, Ferdaous, Bouchra Frikh, Brahim Ouhbi, and Ismail Khalil. "Multi-Criteria Recommender Systems." International Journal of Mobile Computing and Multimedia Communications 8, no. 4 (2017): 20–48. http://dx.doi.org/10.4018/ijmcmc.2017100102.

Full text

Abstract:

A Recommender System (RS) works much better for users when it has more information. In Collaborative Filtering, where users' preferences are expressed as ratings, the more ratings elicited, the more accurate the recommendations. New users present a big challenge for a RS, which has to providing content fitting their preferences. Generally speaking, such problems are tackled by applying Active Learning (AL) strategies that consist on a brief interview with the new user, during which she is asked to give feedback about a set selected items. This article presents a comprehensive study of the most

APA, Harvard, Vancouver, ISO, and other styles

12

Travada, Eko. "TEKNIK POLLING DI RECOMMENDER SYSTEM COLLABORATIVE FILTERING UNTUK PEMBELAJARAN DARING." Jurnal Teknologi dan Komunikasi Pemerintahan 2, no. 1 (2020): 43–51. http://dx.doi.org/10.33701/jtkp.v2i1.2299.

Full text

Abstract:

Currently, the Recommender System (RS) is a method that is widely used to help sort out information, which is currently very large. Without a Recommender System it will be very difficult to sort out the information one by one as needed. Sorting information in a RS is not the same as searching for information, as we do a search for files on storage media by simply writing a few keywords to find the files needed. RS sorting is by looking at the magnitude of a value obtained from drawing conclusions after analyzing the available data, either the user data itself or other user data. Information se

APA, Harvard, Vancouver, ISO, and other styles

13

Geng, Bingrui, Lingling Li, Licheng Jiao, Maoguo Gong, Qing Cai, and Yue Wu. "NNIA-RS: A multi-objective optimization based recommender system." Physica A: Statistical Mechanics and its Applications 424 (April 2015): 383–97. http://dx.doi.org/10.1016/j.physa.2015.01.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Sun, Jinyang, Baisong Liu, Hao Ren, and Weiming Huang. "NCGAN:A neural adversarial collaborative filtering for recommender system." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 2915–23. http://dx.doi.org/10.3233/jifs-210123.

Full text

Abstract:

The major challenge of recommendation system (RS) based on implict feedback is to accurately model users’ preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users’ preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filteri

APA, Harvard, Vancouver, ISO, and other styles

15

Kim, Yuri, Seoyeon Oh, Chaerin Noh, Eunbeen Hong, and Seongbin Park. "Design of a Serendipity-Incorporated Recommender System." Electronics 14, no. 4 (2025): 821. https://doi.org/10.3390/electronics14040821.

Full text

Abstract:

Unexpected yet advantageous findings, often referred to as serendipitous discoveries, are becoming increasingly significant in the field of computer science. This research aims to examine the impact of factors that could potentially trigger such serendipity within a recommender system (RS) and consequently proposes a novel, serendipity-incorporated recommender system (SRS). The SRS is developed by integrating elements that could stimulate the occurrence of serendipity into an RS algorithm. These elements include interestingness, diversity, and unexpectedness. As a result, the SRS is equipped t

APA, Harvard, Vancouver, ISO, and other styles

16

Bhuskute, Tanmay, Amit Jeve, Nihal Shah, Tejas Shah, and B. A. Patil. "MediaRec: A Hybrid Media Recommender System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2723–28. http://dx.doi.org/10.22214/ijraset.2022.42927.

Full text

Abstract:

Abstract: This paper discusses about a hybrid recommendation platform for Movies, Books and Songs in one roof. A recommender system is a subgroup of information filtering systems that helps in predicting the “rating” or “Preference” that a user would give to any item. It also helps users to get media of their choice based on their experiences of self and other users in a productive and efficacious manner without wasting time in useless browsing. Previous approaches in recommender system (RS) include Content based filtering and Collaborative filtering. These approaches have a particular limitat

APA, Harvard, Vancouver, ISO, and other styles

17

Piao, Jinghua, Guozhen Zhang, Fengli Xu, et al. "Bringing Friends into the Loop of Recommender Systems: An Exploratory Study." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021): 1–26. http://dx.doi.org/10.1145/3479583.

Full text

Abstract:

The recommender system (RS), as a computer-supported information filtering system, is ubiquitous and influences what we eat, watch, or even like. In online RS, interactions between users and the system form a feedback loop: users take actions based on the recommendations provided by RS, and RS updates its recommendations accordingly. As such interactions increase, the issue of recommendation homogeneity intensifies, which significantly impairs user experience. In the face of this long-standing issue, the newly-emerging social e-commerce offers a new solution -- bringing friends' recommendation

APA, Harvard, Vancouver, ISO, and other styles

18

M. O., Omisore, and Samuel O. W. "Personalized Recommender System for Digital Libraries." International Journal of Web-Based Learning and Teaching Technologies 9, no. 1 (2014): 18–32. http://dx.doi.org/10.4018/ijwltt.2014010102.

Full text

Abstract:

The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that suit their reading abilities was developed. Content-based filtering (CBF) was used to analyze learners' reading abilities while books that are found suitable to learners are recommended with fuzzy matching techniques. The yokefellow cold-start proble

APA, Harvard, Vancouver, ISO, and other styles

19

Bin Abubakr Joolfoo, Muhammad, Radhika Dhurmoo, and Rameshwar Ashwin Jugurnauth. "Design of a Recommender System (RS) for Job Searching Using Hybrid System." Internet of Things and Cloud Computing 8, no. 3 (2020): 31. http://dx.doi.org/10.11648/j.iotcc.20200803.11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Zhao, Wayne Xin, Gaole He, Kunlin Yang, et al. "KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems." Data Intelligence 1, no. 2 (2019): 121–36. http://dx.doi.org/10.1162/dint_a_00008.

Full text

Abstract:

To develop a knowledge-aware recommender system, a key issue is how to obtain rich and structured knowledge base (KB) information for recommender system (RS) items. Existing data sets or methods either use side information from original RSs (containing very few kinds of useful information) or utilize a private KB. In this paper, we present KB4Rec v1.0, a data set linking KB information for RSs. It has linked three widely used RS data sets with two popular KBs, namely Freebase and YAGO. Based on our linked data set, we first preform qualitative analysis experiments, and then we discuss the effe

APA, Harvard, Vancouver, ISO, and other styles

21

Rabiu, Idris, Naomie Salim, Aminu Da’u, and Akram Osman. "Recommender System Based on Temporal Models: A Systematic Review." Applied Sciences 10, no. 7 (2020): 2204. http://dx.doi.org/10.3390/app10072204.

Full text

Abstract:

Over the years, the recommender systems (RS) have witnessed an increasing growth for its enormous benefits in supporting users’ needs through mapping the available products to users based on their observed interests towards items. In this setting, however, more users, items and rating data are being constantly added to the system, causing several shifts in the underlying relationship between users and items to be recommended, a problem known as concept drift or sometimes called temporal dynamics in RS. Although the traditional techniques of RS have attained significant success in providing rec

APA, Harvard, Vancouver, ISO, and other styles

22

Wilkinson, Daricia, Öznur Alkan, Q. Vera Liao, et al. "Why or Why Not? The Effect of Justification Styles on Chatbot Recommendations." ACM Transactions on Information Systems 39, no. 4 (2021): 1–21. http://dx.doi.org/10.1145/3441715.

Full text

Abstract:

Chatbots or conversational recommenders have gained increasing popularity as a new paradigm for Recommender Systems (RS). Prior work on RS showed that providing explanations can improve transparency and trust, which are critical for the adoption of RS. Their interactive and engaging nature makes conversational recommenders a natural platform to not only provide recommendations but also justify the recommendations through explanations. The recent surge of interest inexplainable AI enables diverse styles of justification, and also invites questions on how styles of justification impact user perc

APA, Harvard, Vancouver, ISO, and other styles

23

Haruna, Khalid, Aminu Musa, Zayyanu Yunusa, Yakubu Ibrahim, Fa’iz Ibrahim Jibia, and Nur Bala Rabiu. "Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes." Science in Information Technology Letters 2, no. 1 (2022): 28–42. http://dx.doi.org/10.31763/sitech.v2i1.610.

Full text

Abstract:

Recommender systems (RS) have been widely used to extract relevant and meaningful information from a vast body of data, to make appropriate suggestions to users with different preferences in various domains of applications. However, despite the success of the early recommendation systems, they suffer from two major challenges of cold start and data sparsity. Traditional RS consider an interaction between user and item (2D), neglecting contextual information such as location, until fairly recently. The contexts extend traditional RS to multi-dimension interaction and provides a useful informati

APA, Harvard, Vancouver, ISO, and other styles

24

Chopra, Akanksha Bansal, and Veer Sain Dixit. "An adaptive RNN algorithm to detect shilling attacks for online products in hybrid recommender system." Journal of Intelligent Systems 31, no. 1 (2022): 1133–49. http://dx.doi.org/10.1515/jisys-2022-1023.

Full text

Abstract:

Abstract Recommender system (RS) depends on the thoughts of numerous users to predict the favourites of potential consumers. RS is vulnerable to malicious information. Unsuitable products can be offered to the user by injecting a few unscrupulous “shilling” profiles like push and nuke attacks into the RS. Injection of these attacks results in the wrong recommendation for a product. The aim of this research is to develop a framework that can be widely utilized to make excellent recommendations for sales growth. This study uses the methodology that presents an enhanced clustering algorithm named

APA, Harvard, Vancouver, ISO, and other styles

25

Mali, Mahesh, Dhirendra Mishra, and M. Vijayalaxmi. "Benchmarking for Recommender System (MFRISE)." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 11, no. 2 (2022): 146–56. http://dx.doi.org/10.17993/3ctic.2022.112.146-156.

Full text

Abstract:

The advent of the internet age offers overwhelming choices of movies and shows to viewers which create need of comprehensive Recommendation Systems (RS). Recommendation System will suggest best content to viewers based on their choice using the methods of Information Retrieval, Data Mining and Machine Learning algorithms. The novel Multifaceted Recommendation System Engine (MFRISE) algorithm proposed in this paper will help the users to get personalized movie recommendations based on multi-clustering approach using user cluster and Movie cluster along with their interaction effect. This will a

APA, Harvard, Vancouver, ISO, and other styles

26

Esheiba, Laila, Amal Elgammal, Iman M. A. Helal, and Mohamed E. El-Sharkawi. "A Hybrid Knowledge-Based Recommender for Product-Service Systems Mass Customization." Information 12, no. 8 (2021): 296. http://dx.doi.org/10.3390/info12080296.

Full text

Abstract:

Manufacturers today compete to offer not only products, but products accompanied by services, which are referred to as product-service systems (PSSs). PSS mass customization is defined as the production of products and services to meet the needs of individual customers with near-mass-production efficiency. In the context of the PSS mass customization environment, customers are overwhelmed by a plethora of previously customized PSS variants. As a result, finding a PSS variant that is precisely aligned with the customer’s needs is a cognitive task that customers will be unable to manage effectiv

APA, Harvard, Vancouver, ISO, and other styles

27

Vijayakumar, V., Subramaniyaswamy Vairavasundaram, R. Logesh, and A. Sivapathi. "Effective Knowledge Based Recommender System for Tailored Multiple Point of Interest Recommendation." International Journal of Web Portals 11, no. 1 (2019): 1–18. http://dx.doi.org/10.4018/ijwp.2019010101.

Full text

Abstract:

With the massive growth of the internet, a new paradigm of recommender systems (RS's) is introduced in various real time applications. In the research for better RS's, especially in the travel domain, the evolution of location-based social networks have helped RS's to understand the changing interests of users. In this article, the authors present a new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs). The recommended personalized list of travel locations will be predicted by generating a heat map of already visit

APA, Harvard, Vancouver, ISO, and other styles

28

Kuanr, Madhusree, and Puspanjali Mohapatra. "Assessment Methods for Evaluation of Recommender Systems: A Survey." Foundations of Computing and Decision Sciences 46, no. 4 (2021): 393–421. http://dx.doi.org/10.2478/fcds-2021-0023.

Full text

Abstract:

Abstract The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are use

APA, Harvard, Vancouver, ISO, and other styles

29

A. Almohsen, Khadija, and Huda Al-Jobori. "Recommender Systems in Light of Big Data." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (2015): 1553. http://dx.doi.org/10.11591/ijece.v5i6.pp1553-1563.

Full text

Abstract:

The growth in the usage of the web, especially e-commerce website, has led to the development of recommender system (RS) which aims in personalizing the web content for each user and reducing the cognitive load of information on the user. However, as the world enters Big Data era and lives through the contemporary data explosion, the main goal of a RS becomes to provide millions of high quality recommendations in few seconds for the increasing number of users and items. One of the successful techniques of RSs is collaborative filtering (CF) which makes recommendations for users based on what o

APA, Harvard, Vancouver, ISO, and other styles

30

Karabila, Ikram, Nossayba Darraz, Anas El-Ansari, Nabil Alami, and Mostafa El Mallahi. "Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis." Future Internet 15, no. 7 (2023): 235. http://dx.doi.org/10.3390/fi15070235.

Full text

Abstract:

Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment

APA, Harvard, Vancouver, ISO, and other styles

31

Ammar, Abdulsalam Al-Asadi, and Nsaif Jasim Mahdi. "Cluster-based denoising autoencoders for rate prediction recommender systems." Cluster-based denoising autoencoders for rate prediction recommender systems 30, no. 3 (2023): 1805–12. https://doi.org/10.11591/ijeecs.v30.i3.pp1805-1812.

Full text

Abstract:

Recommender system (RS) is a suitable tool for filtering out items and providing the most relevant and suitable items to each user, based on their individual preferences. Deep learning algorithms achieve great success in several fields including RS. The issue with deep learning-based RS models is that, they ignore the differences of users’ preferences, and they build a model based on all the users’ rates. This paper proposed an optimized clusteringbased denoising autoencoder model (OCB-DAE) which trains multiple models instead of one, based on users’ preferences using k-means

APA, Harvard, Vancouver, ISO, and other styles

32

Selmi, Afef, Maryah Alawadh, Raghad Alotaibi, and Shrefah Alharbi. "A tag-based recommender system for tourism using collaborative filtering." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 960. https://doi.org/10.11591/ijeecs.v38.i2.pp960-974.

Full text

Abstract:

<p>Recommender systems have garnered significant attention from researchers due to their potential for delivering personalized recommendations in light of the vast amount of information available online. These systems have found applications in various domains, including financial services, movies, and research articles. Their implementation in the tourism industry is particularly promising. Travelers often face the daunting task of selecting the right tourist attractions from a plethora of options, which can consume considerable time and energy. By leveraging personalized recommendation

APA, Harvard, Vancouver, ISO, and other styles

33

Afef, Selmi Maryah Alawadh Raghad Alotaibi Shrefah Alharbi. "A tag-based recommender system for tourism using collaborative filtering." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 960–74. https://doi.org/10.11591/ijeecs.v38.i2.pp960-974.

Full text

Abstract:

Recommender systems have garnered significant attention from researchers due to their potential for delivering personalized recommendations in light of the vast amount of information available online. These systems have found applications in various domains, including financial services, movies, and research articles. Their implementation in the tourism industry is particularly promising. Travelers often face the daunting task of selecting the right tourist attractions from a plethora of options, which can consume considerable time and energy. By leveraging personalized recommendation technolo

APA, Harvard, Vancouver, ISO, and other styles

34

Al-Asadi, Ammar Abdulsalam, and Mahdi Nsaif Jasim. "Cluster-based denoising autoencoders for rate prediction recommender systems." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 3 (2023): 1805. http://dx.doi.org/10.11591/ijeecs.v30.i3.pp1805-1812.

Full text

Abstract:

Recommender system (RS) is a suitable tool for filtering out items and providing the most relevant and suitable items to each user, based on their individual preferences. Deep learning algorithms achieve great success in several fields including RS. The issue with deep learning-based RS models is that, they ignore the differences of users’ preferences, and they build a model based on all the users’ rates. This paper proposed an optimized clustering-based denoising autoencoder model (OCB-DAE) which trains multiple models instead of one, based on users’ preferences using k-means algorithm combin

APA, Harvard, Vancouver, ISO, and other styles

35

Mat Amin, Maizan, Jannifer Yep Ai Lan, Mokhairi Makhtar, and Abd Rasid Mamat. "A Decision Tree Based Recommender System for Backpackers Accommodations." International Journal of Engineering & Technology 7, no. 2.15 (2018): 45. http://dx.doi.org/10.14419/ijet.v7i2.15.11210.

Full text

Abstract:

Backpackers often travel for a longer period of time, have their own budgets and requirements on accommodations. The existing systems do not offer personalized recommendation criteria and some proposed inefficient recommender system (RS) for users. Moreover, other than information searching from websites and bloggers, only limited systems were specifically designed for backpackers’ accommodations recommender system. An observation and online survey was conducted to get the information from backpackers regarding their preferences while looking for the accommodations. Fifty (50) respondents were

APA, Harvard, Vancouver, ISO, and other styles

36

Guesmi, Mouadh, Mohamed Amine Chatti, Shoeb Joarder, et al. "Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender System." Information 14, no. 7 (2023): 401. http://dx.doi.org/10.3390/info14070401.

Full text

Abstract:

Significant attention has been paid to enhancing recommender systems (RS) with explanation facilities to help users make informed decisions and increase trust in and satisfaction with an RS. Justification and transparency represent two crucial goals in explainable recommendations. Different from transparency, which faithfully exposes the reasoning behind the recommendation mechanism, justification conveys a conceptual model that may differ from that of the underlying algorithm. An explanation is an answer to a question. In explainable recommendation, a user would want to ask questions (referre

APA, Harvard, Vancouver, ISO, and other styles

37

Mat Nawi, Rosmamalmi, Chee Xuan Yui, Shahrul Azman Mohd Noah, Noryusliza Abdullah, and Norfaradilla Wahid. "A Cross-Domain Linked Open Data-Enabled in Collaborative Group Recommender System." Journal of Advanced Research in Applied Sciences and Engineering Technology 62, no. 3 (2024): 89–101. https://doi.org/10.37934/araset.62.3.89101.

Full text

Abstract:

A new search paradigm is continuously evolving, with users' perspectives on information searching shifting from searching for information to receiving information. One of the new methods of receiving information is through recommender systems (RS). RS have proven to be successful in many traditional domains including tourism and books. The group recommender system (GRS) and individual RS challenges are triggered by the limited and incomplete number of user-item ratings. The data sparsity problem emerges because of this incompleteness. Data sparsity in a group has a negative impact on the quali

APA, Harvard, Vancouver, ISO, and other styles

38

Ojagh, Soroush, Mohammad Reza Malek, and Sara Saeedi. "A Social–Aware Recommender System Based on User’s Personal Smart Devices." ISPRS International Journal of Geo-Information 9, no. 9 (2020): 519. http://dx.doi.org/10.3390/ijgi9090519.

Full text

Abstract:

Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE

APA, Harvard, Vancouver, ISO, and other styles

39

Hornik, Jacob, Chezy Ofir, Matti Rachamim, and Sergei Graguer. "Fog Computing-Based Smart Consumer Recommender Systems." Journal of Theoretical and Applied Electronic Commerce Research 19, no. 1 (2024): 597–614. http://dx.doi.org/10.3390/jtaer19010032.

Full text

Abstract:

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent langu

APA, Harvard, Vancouver, ISO, and other styles

40

Aljukhadar, Muhammad, and Sylvain Senecal. "The Effect of Consumer-Activated Mind-Set and Product Involvement on the Compliance With Recommender System Advice." SAGE Open 11, no. 3 (2021): 215824402110315. http://dx.doi.org/10.1177/21582440211031550.

Full text

Abstract:

Whereas the research gauging the effectiveness of e-commerce recommender systems (RS) has depended on their design factors, recent work proposes a key role for consumer’s psychological factors. Involvement should reduce the compliance with RS advice because a consumer highly involved with the product perceives high choice risk and assigns low value to the advice. However, a consumer’s activated mind-set captured by implicit theory (fixed vs. growth mind-set) should also shape compliance. It is hypothesized that the two factors interact to jointly mitigate advice taking. Specifically, consumers

APA, Harvard, Vancouver, ISO, and other styles

41

Guo, Shangzhi, Xiaofeng Liao, Gang Li, Kaiyi Xian, Yuhang Li, and Cheng Liang. "A Hybrid Recommender System Based on Autoencoder and Latent Feature Analysis." Entropy 25, no. 7 (2023): 1062. http://dx.doi.org/10.3390/e25071062.

Full text

Abstract:

A recommender system (RS) is highly efficient in extracting valuable information from a deluge of big data. The key issue of implementing an RS lies in uncovering users’ latent preferences on different items. Latent Feature Analysis (LFA) and deep neural networks (DNNs) are two of the most popular and successful approaches to addressing this issue. However, both the LFA-based and the DNNs-based models have their own distinct advantages and disadvantages. Consequently, relying solely on either the LFA or DNN-based models cannot ensure optimal recommendation performance across diverse real-world

APA, Harvard, Vancouver, ISO, and other styles

42

Prof., Vaishali V. Jikar Janvi Pandey Vrushabh Baraskar Hrushikesh Thorat Reshma Zade. "Book Recommendation System Using Machine Learning." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 267–70. https://doi.org/10.5281/zenodo.12736812.

Full text

Abstract:

Recommendation System (RS) is software that suggests similar items to a purchaser based on his/her earlier purchases or preferences. RS examines huge data of objects and compiles a list of those objects which would fulfil the requirements of the buyer. Nowadays most ecommerce companies are using Recommendation systems to lure buyers to purchase more by offering items that the buyer is likely to prefer. Book Recommendation System is being used by Amazon, Barnes and Noble, Flipkart, Goodreads, etc. to recommend books the customer would be tempted to buy as they are matched with his/her choices.

APA, Harvard, Vancouver, ISO, and other styles

43

Martínez-López, Francisco J., Irene Esteban-Millat, Ana Argila, and Francisco Rejón-Guardia. "Consumers’ psychological outcomes linked to the use of an online store’s recommendation system." Internet Research 25, no. 4 (2015): 562–88. http://dx.doi.org/10.1108/intr-01-2014-0033.

Full text

Abstract:

Purpose – Psychological perspective has been omitted or considered a secondary issue by past studies focused on e-commerce recommendation systems (RS). However, this perspective is key to gaining a better understanding of consumer behaviours when these systems are used to support purchasing processes at online stores. The paper aims to discuss these issues. Design/methodology/approach – The field study consisted of a simulated online shopping process undertaken by a sample of internet users with a recommender system at a real online store (Pixmania). The authors applied rigorous and detailed e

APA, Harvard, Vancouver, ISO, and other styles

44

Zhu, Hongyun. "RS on video games based on item-based collaborative filtering algorithm." Applied and Computational Engineering 5, no. 1 (2023): 11–17. http://dx.doi.org/10.54254/2755-2721/5/20230515.

Full text

Abstract:

With the rapid development of the Internet and e-commerce, recommender systems have received great attention and wide application in this environment. Because it is difficult for people to choose the one that they like in the face of the dazzling array of items on the Internet, and these e-commerce sites also need to consider how to improve efficiency, the recommendation system is an excellent solution. This paper mainly reviewed the development of recommender systems, focusing on the research and experiments of a recommender system based on an item-based collaborative filtering algorithm. Acc

APA, Harvard, Vancouver, ISO, and other styles

45

Lavanya, R., Ebani Gogia, and Nihal Rai. "Comparison Study on Improved Movie Recommender Systems." Webology 18, Special Issue 04 (2021): 1470–78. http://dx.doi.org/10.14704/web/v18si04/web18285.

Full text

Abstract:

Recommendation system is a crucial part of offering items especially in services that offer streaming. For streaming movie services on OTT, RS are a helping hand for users in finding new movies for leisure. In this paper, we propose a machine learning an approach based on auto encoders to produce a CF system which outputs movie rating for a user based on a huge DB of ratings from other users. Utilising Movie Lens dataset, we explore the use of deep learning neural network based Stacked Auto encoders to predict user s ratings on new movies, thereby enabling movie recommendations. We consequentl

APA, Harvard, Vancouver, ISO, and other styles

46

Bahramian, Z., and R. Ali Abbaspour. "AN ONTOLOGY-BASED TOURISM RECOMMENDER SYSTEM BASED ON SPREADING ACTIVATION MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 10, 2015): 83–90. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-83-2015.

Full text

Abstract:

A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user’s pr

APA, Harvard, Vancouver, ISO, and other styles

47

Haw, Su-Cheng, Jayapradha Jayaram, Elham Abdulwahab Anaam, and Heru Agus Santoso. "Exploring Recommender Systems in the Healthcare: A Review on Methods, Applications and Evaluations." International Journal on Robotics, Automation and Sciences 6, no. 2 (2024): 6–15. http://dx.doi.org/10.33093/ijoras.2024.6.2.2.

Full text

Abstract:

Due to the vast amount of publicly available online data, people may find it difficult to obtain relevant information to find food or meals that match their taste and health while maintaining a healthy lifestyle. The overload of information makes it difficult to separate relevant, personalized information from massive volumes of data. Recommendation systems (RS) are suggestion system that provides users with information that they may be interested in. With RS, this enormous amount of information is filtered and analyzed for further insights. This paper will explore several generations of recom

APA, Harvard, Vancouver, ISO, and other styles

48

Ahmed, Muqeem, Mohd Dilshad Ansari, Ninni Singh, Vinit Kumar Gunjan, Santhosh Krishna B. V., and Mudassir Khan. "Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices." Mobile Information Systems 2022 (July 11, 2022): 1–18. http://dx.doi.org/10.1155/2022/2854741.

Full text

Abstract:

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filterin

APA, Harvard, Vancouver, ISO, and other styles

49

Obeid, Charbel, Christine Lahoud, Khoury El, and Pierre-Antoine Champin. "A novel hybrid recommender system approach for student academic advising named COHRS, supported by case-based reasoning and ontology." Computer Science and Information Systems, no. 00 (2022): 11. http://dx.doi.org/10.2298/csis220215011o.

Full text

Abstract:

The recent development of the WorldWideWeb, information, and communications technology have transformed the world and moved us into the data era resulting in an overload of data analysis. Students at high school use, most of the time, the internet as a tool to search for universities/colleges, university?s majors, and career paths that match their interests. However, selecting higher education choices such as a university major is a massive decision for students leading them, to surf the internet for long periods in search of needed information. Therefore, the purpose of this study is to assis

APA, Harvard, Vancouver, ISO, and other styles

50

Yin, Hongzhi, Weiqing Wang, Liang Chen, Xingzhong Du, Quoc Viet Hung Nguyen, and Zi Huang. "Mobi-SAGE-RS: A sparse additive generative model-based mobile application recommender system." Knowledge-Based Systems 157 (October 2018): 68–80. http://dx.doi.org/10.1016/j.knosys.2018.05.028.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!