Log in

Relevant bibliographies by topics / CONTEXT-AWARE RECOMMENDER / Journal articles

To see the other types of publications on this topic, follow the link: CONTEXT-AWARE RECOMMENDER.

Journal articles on the topic 'CONTEXT-AWARE RECOMMENDER'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 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 'CONTEXT-AWARE RECOMMENDER.'

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

Adomavicius, Gediminas, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. "Context-Aware Recommender Systems." AI Magazine 32, no. 3 (2011): 67. http://dx.doi.org/10.1609/aimag.v32i3.2364.

Full text

Abstract:

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges

APA, Harvard, Vancouver, ISO, and other styles

2

WANG, Li-Cai, Xiang-Wu MENG, and Yu-Jie ZHANG. "Context-Aware Recommender Systems." Journal of Software 23, no. 1 (2012): 1–20. http://dx.doi.org/10.3724/sp.j.1001.2012.04100.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Verbert, Katrien, Erik Duval, Stefanie Lindstaedt, and Denis Gillet. "Context-aware Recommender Systems." JUCS - Journal of Universal Computer Science 16, no. (16) (2010): 2175–78. https://doi.org/10.3217/jucs-016-16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Azri, Muhammad Haziq Fikri Bin, Su-Cheng Haw, Kok-Why Ng, and Mohamad Firdaus Mat Saad. "Context-Aware Job Recommender System." JOIV : International Journal on Informatics Visualization 9, no. 2 (2025): 877. https://doi.org/10.62527/joiv.9.2.3021.

Full text

Abstract:

Context-aware recommendation systems have emerged as essential to interactive web content and online job search. Primarily, since so many job offers are published on different online platforms, it can make the users take some time to find good opportunities that match exactly what they are looking for, as well as countless qualified candidates and other characteristics within that context, such as temporality. This comes as no surprise, as many practitioners and researchers have resorted to machine learning to create context-aware job recommendation systems that cater not only to job seekers.

APA, Harvard, Vancouver, ISO, and other styles

5

Pisotskyi, Marian, and Alexey Botchkaryov. "Online Video Platform with Context-aware Content-based Recommender System." Advances in Cyber-Physical Systems 6, no. 1 (2021): 46–53. http://dx.doi.org/10.23939/acps2021.01.046.

Full text

Abstract:

The problem of developing an online video platform with a context-aware content-based recommender system has been considered. Approaches to developing online video platforms have been considered. A comparison of popular online video platforms has been presented. A method of context-aware content-based recommendation of videos has been proposed. A method involves saving information about user interaction with video, obtaining and storing information about which videos the user liked, determining user context, composing a profile of user preferences, composing a profile of user preferences depen

APA, Harvard, Vancouver, ISO, and other styles

6

Kavu, Tatenda D., Kudakwashe Dube, and Peter G. Raeth. "Holistic User Context-Aware Recommender Algorithm." Mathematical Problems in Engineering 2019 (September 29, 2019): 1–15. http://dx.doi.org/10.1155/2019/3965845.

Full text

Abstract:

Existing recommender algorithms lack dynamism, human focus, and serendipitous recommendations. The literature indicates that the context of a user influences user decisions, and when incorporated in recommender systems (RSs), novel and serendipitous recommendations can be realized. This article shows that social, cultural, psychological, and economic contexts of a user influence user traits or decisions. The article demonstrates a novel approach of incorporating holistic user context-aware knowledge in an algorithm to solve the highlighted problems. Web content mining and collaborative filteri

APA, Harvard, Vancouver, ISO, and other styles

7

Qassimi, Sara, El Hassan Abdelwahed, and Meriem Hafidi. "Folksonomy Graphs Based Context-Aware Recommender System Using Spectral Clustering." International Journal of Machine Learning and Computing 10, no. 1 (2020): 63–68. http://dx.doi.org/10.18178/ijmlc.2020.10.1.899.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Iqbal, Misbah, Mustansar Ali Ghazanfar, Asma Sattar, et al. "Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm." IEEE Access 7 (2019): 24719–37. http://dx.doi.org/10.1109/access.2019.2897003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Ali, Waqar, Jay Kumar, Cobbinah Bernard Mawuli, Lei She, and Jie Shao. "Dynamic context management in context-aware recommender systems." Computers and Electrical Engineering 107 (April 2023): 108622. http://dx.doi.org/10.1016/j.compeleceng.2023.108622.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Naha, Sanchita, and Sudeep Marwaha. "Context-Aware Recommender System for Maize Cultivation." Journal of Community Mobilization and Sustainable Development 15, no. 2 (2020): 485–90. http://dx.doi.org/10.5958/2231-6736.2020.00034.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Kumar, Rajeev, B. K. Verma, and Shyam Sunder Rastogi. "Context-aware Social Popularity based Recommender System." International Journal of Computer Applications 92, no. 2 (2014): 37–42. http://dx.doi.org/10.5120/15985-4907.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Woerndl, Wolfgang, Michele Brocco, and Robert Eigner. "Context-Aware Recommender Systems in Mobile Scenarios." International Journal of Information Technology and Web Engineering 4, no. 1 (2009): 67–85. http://dx.doi.org/10.4018/jitwe.2009010105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Yao, Yonglei, and Jingfa Liu. "On Privacy-preserving Context-aware Recommender System." International Journal of Hybrid Information Technology 8, no. 10 (2015): 27–40. http://dx.doi.org/10.14257/ijhit.2015.8.10.04.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

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

15

Jeong, Soo-Yeon, and Young-Kuk Kim. "Deep Learning-Based Context-Aware Recommender System Considering Change in Preference." Electronics 12, no. 10 (2023): 2337. http://dx.doi.org/10.3390/electronics12102337.

Full text

Abstract:

In order to predict and recommend what users want, users’ information is required, and more information is required to improve the performance of the recommender system. As IoT devices and smartphones have made it possible to know the user’s context, context-aware recommender systems have emerged to predict preferences by considering the user’s context. A context-aware recommender system uses contextual information such as time, weather, and location to predict preferences. However, a user’s preferences are not always the same in a given context. They may follow trends or make different choice

APA, Harvard, Vancouver, ISO, and other styles

16

Ahn, Hyun Chul, and Kyoung Jae Kim. "Context-Aware Recommender System for Location-Based Advertising." Key Engineering Materials 467-469 (February 2011): 2091–96. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.2091.

Full text

Abstract:

Demand for context-aware systems continues to grow due to the diffusion of mobile devices. This trend may represent good market opportunities for mobile service industries. Thus, context-aware or location-based advertising (LBA) has been an interesting marketing tool for many companies. However, some studies reported that the performance of context-aware marketing or advertising has been quite disappointing. In this study, we propose a novel context-aware recommender system for LBA. Our proposed model is designed to apply a modified collaborative filtering (CF) algorithm with regard to the sev

APA, Harvard, Vancouver, ISO, and other styles

17

Boyinbode, Olutayo, and Tunde Fatoke. "Context-aware recommender system for adaptive ubiquitous learning." International Journal of Mobile Learning and Organisation 15, no. 4 (2021): 409. http://dx.doi.org/10.1504/ijmlo.2021.118437.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Liu, Xiangyong, Guojun Wang, and Md Zakirul Alam Bhuiyan. "Personalised context-aware re-ranking in recommender system." Connection Science 34, no. 1 (2021): 319–38. http://dx.doi.org/10.1080/09540091.2021.1997915.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Tair, H. Al, M. J. Zemerly, M. Al-Qutayri, and M. Leida. "Architecture for Context-Aware Pro-Active Recommender System." International Journal of Multimedia and Image Processing 2, no. 3/4 (2012): 125–33. http://dx.doi.org/10.20533/ijmip.2042.4647.2012.0016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Boyinbode, Olutayo, and Tunde Fatoke. "Context-Aware Recommender System for Adaptive Ubiquitous Learning." International Journal of Mobile Learning and Organisation 15, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijmlo.2021.10034146.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Richa and Punam Bedi. "Parallel proactive cross domain context aware recommender system." Journal of Intelligent & Fuzzy Systems 34, no. 3 (2018): 1521–33. http://dx.doi.org/10.3233/jifs-169447.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Kavu, Tatenda, Kudakwashe Dube, and Peter Raeth. "Erratum to “Holistic User Context-Aware Recommender Algorithm”." Mathematical Problems in Engineering 2020 (November 30, 2020): 1. http://dx.doi.org/10.1155/2020/4706185.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Singh, Richa, and Punam Bedi. "Parallel context-aware multi-agent tourism recommender system." International Journal of Computational Science and Engineering 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijcse.2017.10010189.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Richa, N. A., and Punam Bedi. "Parallel context-aware multi-agent tourism recommender system." International Journal of Computational Science and Engineering 20, no. 4 (2019): 536. http://dx.doi.org/10.1504/ijcse.2019.104440.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Colombo-Mendoza, Luis Omar, Rafael Valencia-García, Giner Alor-Hernández, and Paolo Bellavista. "Special Issue on Context-aware Mobile Recommender Systems." Pervasive and Mobile Computing 38 (July 2017): 444–45. http://dx.doi.org/10.1016/j.pmcj.2017.03.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Sundermann, Camila Vaccari, Marcos Aurélio Domingues, Merley da Silva Conrado, and Solange Oliveira Rezende. "Privileged contextual information for context-aware recommender systems." Expert Systems with Applications 57 (September 2016): 139–58. http://dx.doi.org/10.1016/j.eswa.2016.03.036.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Raza, Shaina, and Chen Ding. "Progress in context-aware recommender systems — An overview." Computer Science Review 31 (February 2019): 84–97. http://dx.doi.org/10.1016/j.cosrev.2019.01.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Keikha, Fateme, and Mahdi Heidari. "Properties of Context-Aware Recommender Systems: A Survey." International Journal of Computer Applications 127, no. 5 (2015): 9–13. http://dx.doi.org/10.5120/ijca2015906379.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Véras, Douglas, Ricardo Prudêncio, and Carlos Ferraz. "CD-CARS: Cross-domain context-aware recommender systems." Expert Systems with Applications 135 (November 2019): 388–409. http://dx.doi.org/10.1016/j.eswa.2019.06.020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Wörndl, Wolfgang, and Béatrice Lamche. "User Interaction with Context-aware Recommender Systems on Smartphones." icom 14, no. 1 (2015): 19–28. http://dx.doi.org/10.1515/icom-2015-0007.

Full text

Abstract:

SummaryIn this article we give an overview on selected aspects of user interaction with context-aware recommender systems on smartphones. We discuss these according to the three steps of user interaction with recommender systems using subjective and objective evaluation criteria: 1. Preference elicitation: how input methods on mobile devices can influence the users’ rating behavior, 2. Result delivery and presentation: how results can be adapted to the mobile context, 3. Feedback, critiquing and refinement: how interactive explanation can improve the user experience. The selection of examples

APA, Harvard, Vancouver, ISO, and other styles

31

Kusuma Adi Achmad, Lukito Edi Nugroho, Achmad Djunaedi, and Widyawan. "Socio-user Context Aware-Based Recommender System: Context Suggestions for A Better Tourism Recommendation." International Journal on Information and Communication Technology (IJoICT) 9, no. 2 (2023): 96–119. http://dx.doi.org/10.21108/ijoict.v9i2.858.

Full text

Abstract:

The existing tourism recommender system model is mostly predictive analytics for destination recommendations (item recommendation). Limited research has been conducted in the discussion of a recommender system model, particularly context suggestion. Thus, it is necessary to develop a recommender system model not only to predict tourism destinations but also to suggest contexts appropriate for tourist preferences (context suggestions). A deep learning method was used to create a model of the socio-user context aware-based recommender system for context suggestions. The attribute used as a label

APA, Harvard, Vancouver, ISO, and other styles

32

Valera, Adrián, Álvaro Lozano Murciego, and María N. Moreno-García. "Context-Aware Music Recommender Systems for Groups: A Comparative Study." Information 12, no. 12 (2021): 506. http://dx.doi.org/10.3390/info12120506.

Full text

Abstract:

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender

APA, Harvard, Vancouver, ISO, and other styles

33

Zheng, Yong. "Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison." Information 13, no. 1 (2022): 42. http://dx.doi.org/10.3390/info13010042.

Full text

Abstract:

Recommender systems can assist with decision-making by delivering a list of item recommendations tailored to user preferences. Context-aware recommender systems additionally consider context information and adapt the recommendations to different situations. A process of context matching, therefore, enables the system to utilize rating profiles in the matched contexts to produce context-aware recommendations. However, it suffers from the sparsity problem since users may not rate items in various context situations. One of the major solutions to alleviate the sparsity issue is measuring the simi

APA, Harvard, Vancouver, ISO, and other styles

34

Sielis, George, Christos Mettouris, George Papadopoulos, Aimilia Tzanavari, Roger Dols, and Quintin Siebers. "A Context Aware Recommender System for Creativity Support Tools." JUCS - Journal of Universal Computer Science 17, no. (12) (2011): 1743–63. https://doi.org/10.3217/jucs-017-12-1743.

Full text

Abstract:

The development of methods that can enhance the creativity process is becoming a continuous necessity. Through the years several researchers modelled and defined creativity focusing to the psychological aspect of the topic. More recent researchers approach creativity as a computerized process by simulating it within creativity support tools (CST). This article supports that usage of context aware recommender system, in creativity support tools and more specifically, collaborative creativity support tools (CCST) can enhance creativity process. In this work we focus on the development of a conte

APA, Harvard, Vancouver, ISO, and other styles

35

Livne, Amit, Eliad Shem Tov, Adir Solomon, Achiya Elyasaf, Bracha Shapira, and Lior Rokach. "Evolving context-aware recommender systems with users in mind." Expert Systems with Applications 189 (March 2022): 116042. http://dx.doi.org/10.1016/j.eswa.2021.116042.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Zha, Yongfu, Yongjian Zhang, Zhixin Liu, and Yumin Dong. "Self-Attention Based Time-Rating-Aware Context Recommender System." Computational Intelligence and Neuroscience 2022 (September 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/9288902.

Full text

Abstract:

The sequential recommendation can predict the user’s next behavior according to the user’s historical interaction sequence. To better capture users’ preferences, some sequential recommendation models propose time-aware attention networks to capture users’ long-term and short-term intentions. However, although these models have achieved good results, they ignore the influence of users on the rating information of items. We believe that in the sequential recommendation, the user’s displayed feedback (rating) on an item reflects the user’s preference for the item, which directly affects the user’

APA, Harvard, Vancouver, ISO, and other styles

37

Inzunza, Sergio, Reyes Juárez-Ramírez, Samantha Jiménez, and Guillermo Licea. "GUMCARS: General User Model for Context-Aware Recommender Systems." Computing and Informatics 37, no. 5 (2018): 1149–83. http://dx.doi.org/10.4149/cai_2018_5_1149.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Takama, Yasufumi, Jing-cheng Zhang, and Hiroki Shibata. "Context-aware Music Recommender System Based on Implicit Feedback." Transactions of the Japanese Society for Artificial Intelligence 36, no. 1 (2021): WI2—D_1–10. http://dx.doi.org/10.1527/tjsai.36-1_wi2-d.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Villegas, Norha M., Cristian Sánchez, Javier Díaz-Cely, and Gabriel Tamura. "Characterizing context-aware recommender systems: A systematic literature review." Knowledge-Based Systems 140 (January 2018): 173–200. http://dx.doi.org/10.1016/j.knosys.2017.11.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Hosseinzadeh Aghdam, Mehdi. "Context-aware recommender systems using hierarchical hidden Markov model." Physica A: Statistical Mechanics and its Applications 518 (March 2019): 89–98. http://dx.doi.org/10.1016/j.physa.2018.11.037.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Champiri, Zohreh Dehghani, Seyed Reza Shahamiri, and Siti Salwah Binti Salim. "A systematic review of scholar context-aware recommender systems." Expert Systems with Applications 42, no. 3 (2015): 1743–58. http://dx.doi.org/10.1016/j.eswa.2014.09.017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Sobhy, Shymaa, Eman M. Mohamed, Arabi Keshk, and Mahmoud Hussein. "Context-aware recommender system for multi-user smart home." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3192. http://dx.doi.org/10.11591/ijece.v13i3.pp3192-3203.

Full text

Abstract:

<span lang="EN-US">Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstl

APA, Harvard, Vancouver, ISO, and other styles

43

Codina, Victor, Francesco Ricci, and Luigi Ceccaroni. "Distributional semantic pre-filtering in context-aware recommender systems." User Modeling and User-Adapted Interaction 26, no. 1 (2015): 1–32. http://dx.doi.org/10.1007/s11257-015-9158-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Shymaa, Sobhy, M. Mohamed Eman, Keshk Arabi, and Hussein Mahmoud. "Context-aware recommender system for multi-user smart home." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (2023): 3192–203. https://doi.org/10.11591/ijece.v13i3.pp3192-3203.

Full text

Abstract:

Smart home is one of the most important applications of the internet of things (IoT). Smart home makes life simpler, easier to control, saves energy based on user’s behavior and interaction with the home appliances. Many existing approaches have designed a smart home system using data mining algorithms. However, these approaches do not consider multiusers that exist in the same location and time (which needs a complex control). They also use centralized mining algorithm, then the system’s efficiency is reduced when datasets increase. Therefore, in this paper, we firstly build a con

APA, Harvard, Vancouver, ISO, and other styles

45

Ubaid, Ayesha, Adrian Lie, and Xiaojie Lin. "SMART Restaurant ReCommender: A Context-Aware Restaurant Recommendation Engine." AI 6, no. 4 (2025): 64. https://doi.org/10.3390/ai6040064.

Full text

Abstract:

With the rise of e-commerce and web application usage, recommendation systems have become important to our daily tasks. They provide personalized suggestions to assist with any task under consideration. While various machine learning algorithms have been developed for recommendation tasks, existing systems still face limitations. This research focuses on advancing context-aware recommendation sytems by leveraging the capabilities of Large Language Models (LLMs) in conjunction with real-time data. The research exploits the integration of existing real-time data APIs with LLMs to enhance the cap

APA, Harvard, Vancouver, ISO, and other styles

46

Oguntuase, Rianat Abimbola, Arome Junior Gabriel, and Bolanle Adefowoke Ojokoh. "A Personalized Context-Aware Places of Interest Recommender System." Journal of Computing Theories and Applications 2, no. 4 (2025): 481–97. https://doi.org/10.62411/jcta.12362.

Full text

Abstract:

This research presents a personalized, context-aware recommender system to suggest Places of Interest (POIs) using a hybrid approach combining Bayesian inference and collaborative filtering. The system explicitly addresses the cold-start problem that new users face and improves recommendation accuracy by considering contextual variables such as user mood, budget, companion, and location. The system collects real-time contextual inputs for new users with no historical data and applies Bayesian inference to generate relevant POI suggestions. As users begin to interact and provide ratings, the sy

APA, Harvard, Vancouver, ISO, and other styles

47

Abdi, Mohamed Hussein, George Onyango Okeyo, and Ronald Waweru Mwangi. "Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey." Computer and Information Science 11, no. 2 (2018): 1. http://dx.doi.org/10.5539/cis.v11n2p1.

Full text

Abstract:

Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem

APA, Harvard, Vancouver, ISO, and other styles

48

Sundermann, Camila, Marcos Domingues, Roberta Sinoara, Ricardo Marcacini, and Solange Rezende . "Using Opinion Mining in Context-Aware Recommender Systems: A Systematic Review." Information 10, no. 2 (2019): 42. http://dx.doi.org/10.3390/info10020042.

Full text

Abstract:

Recommender systems help users by recommending items, such as products and services, that can be of interest to these users. Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user’s current context (e.g., location and time). Moreover, the advent of Web 2.0 and the growing popularity of social and e-commerce media sites have encouraged users to naturally write texts describing their assessment of items. There are increasing efforts to incorporate the rich information embedded in user’s reviews/texts int

APA, Harvard, Vancouver, ISO, and other styles

49

Jeong, Soo-Yeon, and Young-Kuk Kim. "Deep Learning-Based Context-Aware Recommender System Considering Contextual Features." Applied Sciences 12, no. 1 (2021): 45. http://dx.doi.org/10.3390/app12010045.

Full text

Abstract:

A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed mod

APA, Harvard, Vancouver, ISO, and other styles

50

MADANI, Rabie, Abderrahmane EZ-ZAHOUT, Fouzia OMARY, and Abdelhaq CHEDMI. "Advancing Context-Aware Recommender Systems: A Deep Context-Based Factorization Machines Approach." International Journal of Computing and Digital Systems 15, no. 1 (2024): 353–63. http://dx.doi.org/10.12785/ijcds/160128.

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!