Log in

Relevant bibliographies by topics / Hybrid Recommendation / Journal articles

To see the other types of publications on this topic, follow the link: Hybrid Recommendation.

Journal articles on the topic 'Hybrid Recommendation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 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 'Hybrid Recommendation.'

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

Phan, Lan Phuong, Hung Huu Huynh, and Hiep Xuan Huynh. "Implicative Rating-Based Hybrid Recommendation Systems." International Journal of Machine Learning and Computing 8, no. 3 (2018): 223–28. http://dx.doi.org/10.18178/ijmlc.2018.8.3.691.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Kumar, Praveen, Mukesh Kumar Gupta, Channapragada Rama Seshagiri Rao, M. Bhavsingh, and M. Srilakshmi. "A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (2023): 184–92. http://dx.doi.org/10.17762/ijritcc.v11i3s.6180.

Full text

Abstract:

Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recom

APA, Harvard, Vancouver, ISO, and other styles

3

Alrashidi, Muhammad, Roliana Ibrahim, and Ali Selamat. "Hybrid CNN-based Recommendation System." Baghdad Science Journal 21, no. 2(SI) (2024): 0592. http://dx.doi.org/10.21123/bsj.2024.9756.

Full text

Abstract:

Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model i

APA, Harvard, Vancouver, ISO, and other styles

4

Shambour, Qusai Y., Mosleh M. Abualhaj, and Ahmad Adel Abu-Shareha. "Restaurant Recommendations Based on Multi-Criteria Recommendation Algorithm." JUCS - Journal of Universal Computer Science 29, no. (2) (2023): 179–200. https://doi.org/10.3897/jucs.78240.

Full text

Abstract:

Recent years have witnessed a rapid explosion of online information sources about restaurants, and the selection of an appropriate restaurant has become a tedious and time-consuming task. A number of online platforms allow users to share their experiences by rating restaurants based on more than one criterion, such as food, service, and value. For online users who do not have enough information about suitable restaurants, ratings can be decisive factors when choosing a restaurant. Thus, personalized systems such as recommender systems are needed to infer the preferences of each user and then s

APA, Harvard, Vancouver, ISO, and other styles

5

Kudori, Dio Saputra. "Event Recommendation System using Hybrid Method Based on Mobile Device." Journal of Information Technology and Computer Science 6, no. 1 (2021): 107–16. http://dx.doi.org/10.25126/jitecs.202161221.

Full text

Abstract:

In everyday life there are many events that are held. Theseeventuse various ways in term of announcing eventfor attracting people to come.Because there are many event that are held in everyday life,an event recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the onethat frequently used is collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event recommendation syste

APA, Harvard, Vancouver, ISO, and other styles

6

Putri, Salsa Nadira, Tjut Awaliyah Zuraiyah, and Dinar Munggaran Akhmad. "Recommender Systems using Hybrid Demographic and Content-Based Filtering methods for UMKM Products." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 21, no. 1 (2024): 31–44. http://dx.doi.org/10.33751/komputasi.v21i1.8991.

Full text

Abstract:

Marketing digitization such as e-commerce is needed by micro, small and medium enterprises (UMKM) in Bogor City and Regency so that the products are more easily accessible to consumers. One of the digital marketing that is commonly used by consumers is an e-commerce website. The Recommendation System is implemented into e-commerce websites to increase consumer convenience in online shopping. The recommendation systems method applied is Demographic Filtering and Content-based Filtering. Demographic Filtering uses IMDB Weighted Rating calculations which generate recommendations globally and give

APA, Harvard, Vancouver, ISO, and other styles

7

Wang, Jing, and Nianlong Luo. "A New Hybrid Popular Model for Personalized Tag Recommendation." Journal of Computers 11, no. 2 (2016): 116–23. http://dx.doi.org/10.17706/jcp.11.2.116-123.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Zhanwar, Radhe, Shivaji Pawar, Atul Narkhede, and Kavita Venkatachari. "A Hybrid Movie Recommendation System Integrating Content-Based Filtering With Personality Traits." International Journal of Environmental Sciences 11, no. 9s (2025): 623–30. https://doi.org/10.64252/xkhkh891.

Full text

Abstract:

This paper presents a novel approach to movie recommendations by integrating traditional content-based filtering with personality traits analysis. We propose a hybrid system that combines TF-IDF-based similarity measures with the Big Five personality model to generate personalized movie recommendations. Our system analyzes movie features, including genres, cast, crew, and keywords, while incorporating user personality traits to adjust recommendations. Experimental results demonstrate improved recommendation diversity and user satisfaction compared to traditional content-based approaches. The h

APA, Harvard, Vancouver, ISO, and other styles

9

Piatykop, O., K. Minina, and R. Bezuglov. "A Hybrid approach for movie recommendation." Reporter of the Priazovskyi State Technical University. Section: Technical sciences, no. 41 (December 24, 2020): 13–19. https://doi.org/10.31498/2225-6733.41.2020.226119.

Full text

Abstract:

The rapid growth in the amount of digital information available and in the number of Internet users has created a potential problem of information overload and quick access to the items that may interest the users. Therefore, there arises a necessity to filter, prioritize and effectively deliver relevant information to the users. Recommendation systems solve this problem through searching for dynamically generated information to provide the users with personalized content and services. From a large amount of data, recommendation systems filter information according to personal preferences, int

APA, Harvard, Vancouver, ISO, and other styles

10

Cheng, Guang Hua. "An Effective Hybrid Collaborative Recommendation Algorithm for Alleviating Data Sparsity." Applied Mechanics and Materials 39 (November 2010): 535–39. http://dx.doi.org/10.4028/www.scientific.net/amm.39.535.

Full text

Abstract:

Every day there is lots of information obtained via the Internet. The problem of information overload is becoming increasingly serious, and we have all experienced the feeling of being overwhelmed. Many researchers and practitioners more attention on building a suitable tool that can help users conserve resources and services that are wanted. Personalized recommendation systems are used to make recommendations for the user invisible elements get to their preferences, which differ in the position, a user from one another in order to provide information based. The paper presented a personalized

APA, Harvard, Vancouver, ISO, and other styles

11

Svrcek, Martin, Michal Kompan, and Maria Bielikova. "Towards understandable personalized recommendations: Hybrid explanations." Computer Science and Information Systems 16, no. 1 (2019): 179–203. http://dx.doi.org/10.2298/csis171217012s.

Full text

Abstract:

Nowadays, personalized recommendations are widely used and popular. There are a lot of systems in various fields, which use recommendations for different purposes. One of the basic problems is the distrust of users of recommended systems. Users often consider the recommendations as an intrusion of their privacy. Therefore, it is important to make recommendations transparent and understandable to users. To address these problems, we propose a novel hybrid method of personalized explanation of recommendations. Our method is independent of recommendation technique and combines basic explanation s

APA, Harvard, Vancouver, ISO, and other styles

12

Ni, Wenkai, Yanhui Du, Xingbang Ma, and Haibin Lv. "Research on Hybrid Recommendation Model for Personalized Recommendation Scenarios." Applied Sciences 13, no. 13 (2023): 7903. http://dx.doi.org/10.3390/app13137903.

Full text

Abstract:

One of the five types of Internet information service recommendation technologies is the personalized recommendation algorithm, and knowledge graphs are frequently used in these algorithms. RippleNet is a personalized recommendation model based on knowledge graphs, but it is susceptible to localization issues in user portrait updating. In this study, we propose NRH (Node2vec-side and RippleNet Hybrid Model), a hybrid recommendation model based on RippleNet that uses Node2vec-side for item portrait modeling and explores potential association relationships of items; the user portrait is split in

APA, Harvard, Vancouver, ISO, and other styles

13

Song, Cen, Qing Yu, Esther Jose, Jun Zhuang, and He Geng. "A Hybrid Recommendation Approach for Viral Food Based on Online Reviews." Foods 10, no. 8 (2021): 1801. http://dx.doi.org/10.3390/foods10081801.

Full text

Abstract:

Nowadays, there are many types of viral foods and consumers expect to be able to quickly find foods that meet their own tastes. Traditional recommendation systems make recommendations based on the popularity of viral foods or user ratings. However, because of the different sentimental levels of users, deviations occur and it is difficult to meet the user’s specific needs. Based on the characteristics of viral food, this paper constructs a hybrid recommendation approach based on viral food reviews and label attribute data. A user-based recommendation approach is combined with a content-based re

APA, Harvard, Vancouver, ISO, and other styles

14

Yang, Fan. "A hybrid recommendation algorithm–based intelligent business recommendation system." Journal of Discrete Mathematical Sciences and Cryptography 21, no. 6 (2018): 1317–22. http://dx.doi.org/10.1080/09720529.2018.1526408.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Duan, Yaowei, Liang Zhang, Xu Lu, and Junqing Li. "Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading." Applied Sciences 15, no. 4 (2025): 1898. https://doi.org/10.3390/app15041898.

Full text

Abstract:

With the explosive growth of information on the internet, personalized recommendation technology has become an important tool for helping users efficiently acquire information. However, existing spreading-based recommendation algorithms only consider user choices and fail to fully leverage the potential relationships between users and items. Additionally, the incomplete utilization of user and item information limits their application potential and applicable scenarios, resulting in suboptimal recommendation performance in practical applications. To address this issue, we propose a Light Graph

APA, Harvard, Vancouver, ISO, and other styles

16

Gandhi, Pritesh. "Distributed Hybrid Book Recommendation System." International Journal for Research in Applied Science and Engineering Technology V, no. III (2017): 297–98. http://dx.doi.org/10.22214/ijraset.2017.3056.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Hussaina, M. Munafur, and R. Parimala. "Item Recommendation Using Hybrid Method." International Journal of Computer Sciences and Engineering 6, no. 6 (2018): 266–70. http://dx.doi.org/10.26438/ijcse/v6i6.266270.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Hao, Yaxian, and Lijiao Feng. "Improved GMMKNN Hybrid Recommendation Algorithm." Journal of Physics: Conference Series 2747, no. 1 (2024): 012032. http://dx.doi.org/10.1088/1742-6596/2747/1/012032.

Full text

Abstract:

Abstract This paper proposes a hybrid recommendation algorithm that combines the advantages of Gaussian Mixture Model (GMM) and K-Nearest Neighbors (KNN) algorithms. The algorithm first applies GMM to cluster the training data, grouping users with similar interests using clustering techniques. It then utilizes the KNN algorithm for prediction. During the KNN recommendation process for a target user, the algorithm searches for neighboring users with similar interests and features within the same cluster. This significantly reduces the search scope of the nearest neighbors, thanks to the assista

APA, Harvard, Vancouver, ISO, and other styles

19

Arabi, Hossein, and Vimala Balakrishnan. "Personalized Hybrid Book Recommender." International Journal of Information Systems in the Service Sector 11, no. 3 (2019): 70–97. http://dx.doi.org/10.4018/ijisss.2019070105.

Full text

Abstract:

Personalized Recommendation Systems (RS) provide end users with suggestions about items that are likely to be of their interest based on users' details such as demographics, location, time, and emotion. In this article, a Personalized Hybrid Book Recommender (PHyBR) is presented, which integrates personality traits with users' demographic data and geographical location to improve the quality of recommendations. The Ten Item Personality Inventory (TIPI) was used to determine users' personality traits. PHyBR was evaluated using two metrics, that are, Standardized Root Mean Square Residual (SRMR)

APA, Harvard, Vancouver, ISO, and other styles

20

Suka Parwita, Wayan Gede, and Edi Winarko. "Hybrid Recommendation System Memanfaatkan Penggalian Frequent Itemset dan Perbandingan Keyword." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 9, no. 2 (2015): 167. http://dx.doi.org/10.22146/ijccs.7545.

Full text

Abstract:

AbstrakRecommendation system sering dibangun dengan memanfaatkan data peringkat item dan data identitas pengguna. Data peringkat item merupakan data yang langka pada sistem yang baru dibangun. Sedangkan, pemberian data identitas pada recommendation system dapat menimbulkan kekhawatiran penyalahgunaan data identitas.Hybrid recommendation system memanfaatkan algoritma penggalian frequent itemset dan perbandingan keyword dapat memberikan daftar rekomendasi tanpa menggunakan data identitas pengguna dan data peringkat item. Penggalian frequent itemset dilakukan menggunakan algoritma FP-Growth. Seda

APA, Harvard, Vancouver, ISO, and other styles

21

Malyeyeva, Olga, Vadym Yesipov, Roman Artiukh, and Viktor Kosenko. "IMPLEMENTATION OF A HYBRID METHOD OF SEARCHING FOR CLOSE OBJECTS, TAKING INTO ACCOUNT THE GENERAL AND ACOUSTIC CHARACTERISTICS." Innovative Technologies and Scientific Solutions for Industries, no. 1 (15) (March 31, 2021): 59–68. http://dx.doi.org/10.30837/itssi.2021.15.059.

Full text

Abstract:

The subject of research in the article is the methods of finding close objects and technologies of forming recommendations. The aim of the article is to develop a recommendation system based on a hybrid method of searching for objects, taking into account both user preferences and audio characteristics of objects. The following tasks are solved: analysis of methods and algorithms used in recommendation systems; development of a hybrid method of forming recommendations on the principle of double organization; determination of the main functions and architecture of the system of formation of mus

APA, Harvard, Vancouver, ISO, and other styles

22

San, Kyawt Kyawt, Hlaing Hlaing Win, and Khin Ei Ei Chaw. "Enhancing Hybrid Course Recommendation with Weighted Voting Ensemble Learning." Journal of Future Artificial Intelligence and Technologies 1, no. 4 (2025): 337–47. https://doi.org/10.62411/faith.3048-3719-55.

Full text

Abstract:

Course recommendation aims to find suitable and attractive courses for students based on their needs, playing a significant role in the curricula-variable system. However, with the abundant available courses, students often face cognitive overload when selecting the most appropriate ones. This research proposes a course recommendation system called the Enhanced Hybrid Course Recommender to address this challenge. This system uses an ensemble learning approach to combine and leverage the power of multiple machine learning classifiers, including Random Forest, Naive Bayes, and Support Vector Mac

APA, Harvard, Vancouver, ISO, and other styles

23

Fazira Ansshory, Azrina, and Erwin Budi Setiawan. "Social Media (Twitter) Based Movie Recommendation System On Disney+ With Hybrid Filtering Using Neighboor's K-Nearest Method." JINAV: Journal of Information and Visualization 4, no. 2 (2023): 189–96. https://doi.org/10.35877/454ri.jinav1954.

Full text

Abstract:

The research aims on the development of a film recommendation system that combines the Hybrid Filtering and K-Nearest Neighbors method. (KNN). Hybrid Filtering combines a variety of recommendation techniques, including collaborative filtering and content-based filtering, while KNN is a simple method for classifying data based on similarities with close neighbors. The data set used is Kaggle’s Rotten Tomatoes, which includes information such as critics’ names, genres, movie titles, and review content. The aim of the study was to build an accurate system of recommendations based on user ratings

APA, Harvard, Vancouver, ISO, and other styles

24

Zhang, Shuting, Kechen Liu, Zekai Yu, Bowen Feng, and Zijie Ou. "Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction." Applied and Computational Engineering 2, no. 1 (2023): 927–39. http://dx.doi.org/10.54254/2755-2721/2/20220579.

Full text

Abstract:

With the development of recommendation systems, large amount of information collected from e-commerce could help customers to find the potential interesting products. Collaborative filtering and content-based recommendation systems are two common recommendation systems. While collaborative filtering has the problem of cold-start, content-based recommendation system could not explore the potential interests of users. Hybrid system combining these two techniques could achieve better results. This paper applies hybrid recommendation methods to the Amazon food reviews and evaluate the results in t

APA, Harvard, Vancouver, ISO, and other styles

25

UYANIK, Begüm, and Günce Keziban ORMAN. "A Manhattan distance based hybrid recommendation system." International Journal of Applied Mathematics Electronics and Computers 11, no. 1 (2023): 20–29. http://dx.doi.org/10.18100/ijamec.1232090.

Full text

Abstract:

Many online service providers use a recommendation system to assist their customers' decision-making by generating recommendations. Accordingly, this paper proposes a new recommendation system for tourism customers to make online reservations for hotels with the features they need, saving customers time and increasing the impact of personalized hotel recommendations. This new system combined collaborative and content-based filtering approaches and created a new hybrid recommendation system. Two datasets containing customer information and hotel features were analyzed by Recency, Frequency, Mon

APA, Harvard, Vancouver, ISO, and other styles

26

Bohra, Sneha, Amit Gaikwad, and Ghanapriya Singh. "Hybrid Machine Learning Based Recommendation Algorithm for Multiple Movie Dataset." Indian Journal Of Science And Technology 16, no. 37 (2023): 3121–28. http://dx.doi.org/10.17485/ijst/v16i37.2065.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Vasyl, Lytvyn, Vysotska Victoria, Shatskykh Viktor, et al. "DESIGN OF A RECOMMENDATION SYSTEM BASED ON COLLABORATIVE FILTERING AND MACHINE LEARNING CONSIDERING PERSONAL NEEDS OF THE USER." Eastern-European Journal of Enterprise Technologies 4, no. 2 (100) (2019): 6–28. https://doi.org/10.15587/1729-4061.2019.175507.

Full text

Abstract:

The paper reports a study into recommendation algorithms and determination of their advantages and disadvantages. The method for developing recommendations based on collaborative filtering such as Content-Based Filtering (CBF), Collaborative Filtering (CF), and hybrid methods of Machine Learning (ML) has been improved. The paper describes the design principles and functional requirements to a recommendation system in the form of a Web application for choosing the content required by user using movies as an example. The research has focused on solving issues related to cold start and scalabilit

APA, Harvard, Vancouver, ISO, and other styles

28

Pasrija, Vatesh, and Supriya Pasrija. "Demystifying Recommendations: Transparency and Explainability in Recommendation Systems." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 1376–83. http://dx.doi.org/10.22214/ijraset.2024.58541.

Full text

Abstract:

Abstract: Recommendation algorithms are widely used, however many consumers want more clarity on why specific goods are recommended to them. The absence of explainability jeopardizes user trust, satisfaction, and potentially privacy. Improving transparency is difficult and involves the need for flexible interfaces, privacy protection, scalability, and customisation. Explainable recommendations provide substantial advantages such as enhancing relevance assessment, bolstering user interactions, facilitating system monitoring, and fostering accountability. Typical methods include giving summaries

APA, Harvard, Vancouver, ISO, and other styles

29

Tian, Yonghong, Bing Zheng, Yanfang Wang, Yue Zhang, and Qi Wu. "College Library Personalized Recommendation System Based on Hybrid Recommendation Algorithm." Procedia CIRP 83 (2019): 490–94. http://dx.doi.org/10.1016/j.procir.2019.04.126.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Rey Muhamad Rifqi, Djupriadi, Widodo Tri Haryanto, Ema Utami, and Alva Hendi Muhamad. "Hybrid Recommendation System in E-Commerce." International Journal of Integrated Science and Technology 3, no. 5 (2025): 1983–92. https://doi.org/10.59890/ijist.v3i5.29.

Full text

Abstract:

This study explores hybrid recommendation systems in e-commerce, which combine content-based and collaborative filtering to overcome limitations such as cold-start and data sparsity. Through a Systematic Literature Review of 15 selected papers, it identifies key hybrid types—Weighted, Switching, and Cascade Hybridization—and analyzes trends in their adoption. Weighted Hybridization is found to be the most frequently used due to its effectiveness in improving recommendation accuracy. The study also discusses the strengths of hybrid systems in providing personalized and adaptive suggestions, alo

APA, Harvard, Vancouver, ISO, and other styles

31

Bhopale, Prajyot P. "Music Recommendation System Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 1234–37. https://doi.org/10.22214/ijraset.2025.68469.

Full text

Abstract:

Music recommendation systems function as personalized assistants that analyze listener preferences and suggest relevant songs or playlists. These systems utilize past user data to generate recommendations that align with individual tastes. However, users often struggle to identify the most suitable songs due to the vast availability of music content. Various techniques have been employed to enhance recommendation accuracy, including collaborative filtering, content-based filtering, and hybrid models. Initially, the system gathers substantial user data, such as listening history and ratings, to

APA, Harvard, Vancouver, ISO, and other styles

32

Iyer, Sridhar, Nirzari Parikh, Khushi Jobanputra, Gauri Bhosle, and Anushka Pandit. "Virtual One-day Trip Planner." Journal of Sustainable Development Innovations 1, no. 2 (2024): 71–81. http://dx.doi.org/10.61552/jsi.2024.02.003.

Full text

Abstract:

A virtual one-day trip planner is necessary to simplify trip planning for users who wish to explore different places within a limited span of time. A Recommender system is used to make various suggestions according to the budget and time. A Recommendation system is of utmost importance to provide recommendations to users on all the aspects using the technology. Different types of recommendation systems are available, i.e., Hybrid Recommendation System, User-based Collaborative Filtering System and Item-Based Collaborative Filtering System. The user-based filtering compares a user with other us

APA, Harvard, Vancouver, ISO, and other styles

33

Zhang, Tingting, and Shengnan Liu. "Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph." Security and Communication Networks 2022 (April 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/5889724.

Full text

Abstract:

Combining music as a specific recommendation object, a hybrid recommendation algorithm based on music genes and improved knowledge graph is proposed for the traditional single recommendation algorithm that cannot effectively solve the accuracy problem in music recommendation. The algorithm first gives the recommendation pattern of music genes and gets the relevant recommendation results through the genetic preference analysis. After that, the algorithm in this paper utilizes item and user label information and knowledge graphs from two different domains to enrich and mine the potential informa

APA, Harvard, Vancouver, ISO, and other styles

34

Meriem, Adraoui, Souabi Sonia, Retbi Asmaâ, Khalidi Idrissi Mohammed, and Bennani Samir. "Towards a hybrid recommendation approach using a community detection and evaluation algorithm." International Journal of Electrical and Computer Engineering (IJECE) 13 (December 1, 2023): 6718–28. https://doi.org/10.11591/ijece.v13i6.pp6718-6728.

Full text

Abstract:

In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests. Leveraging their similari

APA, Harvard, Vancouver, ISO, and other styles

35

PUTRA, KURNIA RAMADHAN, and MOHAMMAD ADITIYA RACHMAN. "Perbandingan Metode Content-based, Collaborative dan Hybrid Filtering pada Sistem Rekomendasi Lagu." MIND Journal 9, no. 2 (2024): 179–93. https://doi.org/10.26760/mindjournal.v9i2.179-193.

Full text

Abstract:

AbstrakSistem rekomendasi dapat dimanfaatkan untuk membantu pengguna menemukan item atau informasi sesuai preferensi mereka, termasuk lagu. Metode seperti Collaborative Filtering (CF), Content-Based Filtering (CBF), dan Hybrid Filtering digunakan untuk meningkatkan kualitas rekomendasi berdasarkan interaksi pengguna dan karakteristik konten. Penelitian ini membandingkan efektivitas ketiga metode tersebut dalam rekomendasi lagu menggunakan dataset dengan 68.330 entri data. Metode CF dan CBF diterapkan secara terpisah, lalu dikombinasikan dalam pendekatan hybrid untuk mengevaluasi peningkatan ha

APA, Harvard, Vancouver, ISO, and other styles

36

Sharma, Saurabh, and Harish Kumar Shakya. "Hybrid Movie Recommendation System Using Machine Learning." International Journal of Emerging Technology and Advanced Engineering 13, no. 1 (2023): 100–123. http://dx.doi.org/10.46338/ijetae0123_12.

Full text

Abstract:

This research suggests a hybrid movie recommendation system and optimization approach based on weighted classification and user collaborative filtering algorithm to address the issue that the single model of the standard recommendation system cannot adequately reflect user preferences. The top-N personalized movie recommendations are made by fusing the weighted classification model with the local recommendation model, which is trained based on user clustering, and the sparse linear model, which serves as the fundamental recommendation model. The scoring matrix is transformed into a low-dimensi

APA, Harvard, Vancouver, ISO, and other styles

37

Pawar, Vedant. "Book Recommendation System with Integrated Chatbot." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35001.

Full text

Abstract:

In today’s digital era, the overwhelming volume of available literature necessitates a more personalized approach to book recommendations. Our research aims to address this need by pioneering a solution that integrates hybrid filtering methodologies and advanced chatbot technology driven by machine learning algorithms. By leveraging these cutting-edge techniques, we aspire to redefine the book discovery experience, providing tailored recommendations that resonate with individual preferences. Through the fusion of popularized, collaborative and content-based alongside the capabilities of machin

APA, Harvard, Vancouver, ISO, and other styles

38

Upreti, Mohini. "A Book Tracking and Recommender System using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2377–85. https://doi.org/10.22214/ijraset.2025.70694.

Full text

Abstract:

Abstract: Book recommendation systems play a crucial role in enhancing user experience by suggesting books tailored to individual preferences. Traditional approaches, such as collaborative filtering and content-based filtering, have limitations, including cold-start issues and lack of diversity in recommendations. This paper proposes a hybrid book recommendation system that integrates content-based filtering with popularity-based filtering to generate personalized yet diverse book suggestions. The system utilizes vector similarityfor content-based recommendations while leveraging user engageme

APA, Harvard, Vancouver, ISO, and other styles

39

Mini, T. V. "Recommender Systems: Enhancing Prediction Accuracy Through Hybrid Data Mining Techniques." International Journal of Information Technology Research Studies (IJITRS) 1, no. 1 (2025): 7–19. https://doi.org/10.5281/zenodo.15309672.

Full text

Abstract:

This research explores the integration of multiple data mining approaches to improve recommendation accuracy in modern recommender systems. Despite significant advancements in recommendation algorithms, challenges persist in addressing the cold-start problem, data sparsity, and preference volatility. This study investigates how hybrid techniques combining collaborative filtering, content-based filtering, and knowledge-based approaches can overcome these limitations. Using a comprehensive dataset from an e-commerce platform with 2.3 million user-item interactions, we implemented a novel hybrid

APA, Harvard, Vancouver, ISO, and other styles

40

Huang, Zhao, and Pavel Stakhiyevich. "A Time-Aware Hybrid Approach for Intelligent Recommendation Systems for Individual and Group Users." Complexity 2021 (February 27, 2021): 1–19. http://dx.doi.org/10.1155/2021/8826833.

Full text

Abstract:

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. Th

APA, Harvard, Vancouver, ISO, and other styles

41

Adraoui, Meriem, Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi, and Samir Bennani. "Towards a hybrid recommendation approach using a community detection and evaluation algorithm." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6718. http://dx.doi.org/10.11591/ijece.v13i6.pp6718-6728.

Full text

Abstract:

<span lang="EN-US">In social learning platforms, community detection algorithms are used to identify groups of learners with similar interests, behavior, and levels. While, recommendation algorithms personalize the learning experience based on learners' profile information, including interests and past behavior. Combining these algorithms can improve the recommendation quality by identifying learners with similar needs and interests for more accurate and relevant suggestions. Community detection enhances recommendations by identifying groups of learners with similar needs and interests.

APA, Harvard, Vancouver, ISO, and other styles

42

Parasuraman, Desabandh, and Sathiyamoorthy Elumalai. "Hybrid Recommendation Using Temporal Data for Accuracy Improvement in Item Recommendation." Journal of information and organizational sciences 45, no. 2 (2021): 535–51. http://dx.doi.org/10.31341/jios.45.2.10.

Full text

Abstract:

Recommender systems have become a vital entity to the business world in form of software tools to make decisions. It estimates the overloaded information and provides the suitable decisions in any kind of business work through online. Especially in the area of e-commerce, recommender systems provide suggestions to users on the items that are likely based upon user’s true interest. Collaborative Filtering and Content Based Filtering are the main techniques of recommender systems. Collaborative Filtering is considered to be the best in all domains and always outperforms Content Based filtering.

APA, Harvard, Vancouver, ISO, and other styles

43

Phan, Hong Thi Thu, Vuong Luong Nguyen, Trinh Quoc Vo, and Nguyen Ho Trong Pham. "Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation." HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY 14, no. 1 (2024): 41–51. http://dx.doi.org/10.46223/hcmcoujs.tech.en.14.1.2927.2024.

Full text

Abstract:

This article proposes an enhanced knowledge-based collaborative filtering model for movie recommendation services to address the limitations of collaborative filtering in capturing the diverse preferences and specific characteristics of movies. The proposed model integrates external knowledge sources, such as movie plots and reviews, to enrich the recommendation process. By leveraging this additional information, the model can better understand movies’ unique features and attributes, improving recommendation accuracy and relevance. The knowledge-based features are extracted and incorporated in

APA, Harvard, Vancouver, ISO, and other styles

44

Vishwajith, V., S. Kaviraj, and R. Vasanth. "Hybrid Recommender System for Therapy Recommendation." IJARCCE 8, no. 1 (2019): 78–84. http://dx.doi.org/10.17148/ijarcce.2019.8118.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Mulay, Aneesh, Shriyash Sutar, Jiten Patel, Aditi Chhabria, and Snehal Mumbaikar. "Job Recommendation System Using Hybrid Filtering." ITM Web of Conferences 44 (2022): 02002. http://dx.doi.org/10.1051/itmconf/20224402002.

Full text

Abstract:

As for today’s era, recruitment can be considered as one of most difficult process to undergo for job seeking candidate. Many fresher candidates face issue while job recruitment process to undergo which field of interest. The proposed system will help the user to overcome this difficulties by matching their work experience, skills and other details with appropriate companies suitable for respective user. The system will also help experienced users in getting their intended job on the basis of their last job profile. The job recommendation algorithm developed is tedious nor complicated and will

APA, Harvard, Vancouver, ISO, and other styles

46

Zeng, Cheng, Haifeng Zhang, Junwei Ren, Chaodong Wen, and Peng He. "Hybrid recommendation based on graph embedding." China Communications 18, no. 11 (2021): 243–56. http://dx.doi.org/10.23919/jcc.2021.11.017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

KANG, Ying, Aiqin HOU, Zimin ZHAO, and Daguang GAN. "A Hybrid Approach for Paper Recommendation." IEICE Transactions on Information and Systems E104.D, no. 8 (2021): 1222–31. http://dx.doi.org/10.1587/transinf.2020bdp0008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Chornous, Galyna, and Tetiana Lem. "Developing hybrid recommendation systems: Ukrainian dimension." Access Journal - Access to Science, Business, Innovation in the digital economy 3, no. 2 (2022): 89–106. http://dx.doi.org/10.46656/access.2022.3.2(1).

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Gaurav, D. Ganesh. "HOTEL RECOMMENDATION SYSTEM USING HYBRID TECHNIQUE." International Journal of Advanced Research in Computer Science 11, no. 3 (2020): 47–49. http://dx.doi.org/10.26483/ijarcs.v11i3.6529.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Barathan, Monishkanna, and Ershad Sharifahmadian. "Hybrid POI Recommendation System for Tourism." International Journal of System Modeling and Simulation 3, no. 1 (2018): 1. http://dx.doi.org/10.24178/ijsms.2018.3.1.01.

Full text

Abstract:

Due to the increase in amount of available information, finding places and planning of the activities to be done during a tour can be strenuous. Tourists are looking for information about a place in which they have not been before, which worsen the selection of places that fit better with user’s preferences. Recommendation systems have been fundamentally applicable in tourism, suggest suitable places, and effectively prune large information from different locations, so tourists are directed toward those places where are matched with their needs and preferences. Several techniques have been stu

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!