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Статті в журналах з теми "CONTEXT-AWARE RECOMMENDER"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Adomavicius, Gediminas, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. "Context-Aware Recommender Systems." AI Magazine 32, no. 3 (October 31, 2011): 67. http://dx.doi.org/10.1609/aimag.v32i3.2364.

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Анотація:

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 and future research directions for context-aware recommender systems.

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2

WANG, Li-Cai, Xiang-Wu MENG, and Yu-Jie ZHANG. "Context-Aware Recommender Systems." Journal of Software 23, no. 1 (March 5, 2012): 1–20. http://dx.doi.org/10.3724/sp.j.1001.2012.04100.

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3

Pisotskyi, Marian, and Alexey Botchkaryov. "Online Video Platform with Context-aware Content-based Recommender System." Advances in Cyber-Physical Systems 6, no. 1 (January 23, 2021): 46–53. http://dx.doi.org/10.23939/acps2021.01.046.

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Анотація:

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 depending on context, determining the similarity between the video profile and a profile of user preferences (with and without context consideration), determining the relevance of the video to the context, the conclusive estimation of the relevance of the video to the user’s preferences based on the proposed composite relevance indicator. The developed structure of online video platform has been presented. The algorithm of its work has been considered. The structure of the online video platform database has been proposed. Features of designing the user interface of the online video platform have been considered. The issue of testing the developed online video platform has been considered.

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4

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 (January 2020): 63–68. http://dx.doi.org/10.18178/ijmlc.2020.10.1.899.

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5

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.

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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 filtering approaches were used to develop a holistic user context-aware (HUC) algorithm. The algorithm was evaluated on a social network using online experimental evaluations. The algorithm demonstrated dynamism, novelty, and serendipity with an average of 84% novelty and 85% serendipity.

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6

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.

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7

Iqbal, Misbah, Mustansar Ali Ghazanfar, Asma Sattar, Muazzam Maqsood, Salabat Khan, Irfan Mehmood, and Sung Wook Baik. "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.

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8

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.

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9

Kumar, Rajeev, B. K. Verma, and Shyam Sunder Rastogi. "Context-aware Social Popularity based Recommender System." International Journal of Computer Applications 92, no. 2 (April 18, 2014): 37–42. http://dx.doi.org/10.5120/15985-4907.

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10

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 (January 2009): 67–85. http://dx.doi.org/10.4018/jitwe.2009010105.

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11

Yao, Yonglei, and Jingfa Liu. "On Privacy-preserving Context-aware Recommender System." International Journal of Hybrid Information Technology 8, no. 10 (October 31, 2015): 27–40. http://dx.doi.org/10.14257/ijhit.2015.8.10.04.

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12

Jeong, Soo-Yeon, and Young-Kuk Kim. "Deep Learning-Based Context-Aware Recommender System Considering Change in Preference." Electronics 12, no. 10 (May 22, 2023): 2337. http://dx.doi.org/10.3390/electronics12102337.

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Анотація:

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 choices due to changes in their personal environment. Therefore, in this paper, we propose a context-aware recommender system that considers the change in users’ preferences over time. The proposed method is a context-aware recommender system that uses Matrix Factorization with a preference transition matrix to capture and reflect the changes in users’ preferences. To evaluate the performance of the proposed method, we compared the performance with the traditional recommender system, context-aware recommender system, and dynamic recommender system, and confirmed that the performance of the proposed method is better than the existing methods.

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13

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

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Анотація:

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 develop a new CARS algorithm that is tailored to textual contexts, that they call Textual Context Aware Factorization Machines (TCAFM). An empirical evaluation shows that the proposed architecture allows to significantly improve recommendation accuracy using state of the art RS and CARS algorithms, whereas TCAFM leads to additional improvements.

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14

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.

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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 several dimensions for the personalization of mobile devices – location, time and the user’s needs type. In particular, we employ a classification rule to understand user’s needs type using a decision tree algorithm. We empirically validated the effectiveness of the proposed model by using a real-world dataset. Experimental results show that our model makes more accurate and satisfactory advertisements than comparative systems.

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15

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.

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16

Liu, Xiangyong, Guojun Wang, and Md Zakirul Alam Bhuiyan. "Personalised context-aware re-ranking in recommender system." Connection Science 34, no. 1 (November 3, 2021): 319–38. http://dx.doi.org/10.1080/09540091.2021.1997915.

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17

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 (September 1, 2012): 125–33. http://dx.doi.org/10.20533/ijmip.2042.4647.2012.0016.

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18

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.

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19

Richa and Punam Bedi. "Parallel proactive cross domain context aware recommender system." Journal of Intelligent & Fuzzy Systems 34, no. 3 (March 22, 2018): 1521–33. http://dx.doi.org/10.3233/jifs-169447.

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20

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.

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21

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.

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22

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.

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23

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.

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24

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.

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25

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.

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26

Keikha, Fateme, and Mahdi Heidari. "Properties of Context-Aware Recommender Systems: A Survey." International Journal of Computer Applications 127, no. 5 (October 15, 2015): 9–13. http://dx.doi.org/10.5120/ijca2015906379.

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27

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.

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28

Valera, Adrián, Álvaro Lozano Murciego, and María N. Moreno-García. "Context-Aware Music Recommender Systems for Groups: A Comparative Study." Information 12, no. 12 (December 7, 2021): 506. http://dx.doi.org/10.3390/info12120506.

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Анотація:

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 systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

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29

Wörndl, Wolfgang, and Béatrice Lamche. "User Interaction with Context-aware Recommender Systems on Smartphones." icom 14, no. 1 (April 15, 2015): 19–28. http://dx.doi.org/10.1515/icom-2015-0007.

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Анотація:

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 is based on several studies we did in different mobile scenarios.

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30

Zheng, Yong. "Context-Aware Collaborative Filtering Using Context Similarity: An Empirical Comparison." Information 13, no. 1 (January 17, 2022): 42. http://dx.doi.org/10.3390/info13010042.

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Анотація:

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 similarity of contexts and utilizing rating profiles with similar contexts to build the recommendation model. In this paper, we summarize the context-aware collaborative filtering methods using context similarity, and deliver an empirical comparison based on multiple context-aware data sets.

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31

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.

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32

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.

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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’s choice of the next item to a certain extent. In different periods of sequential recommendation, the user’s rating of the item reflects the change in the user’s preference. In this paper, we separately model the time interval of items in the user’s interaction sequence and the ratings of the items in the interaction sequence to obtain temporal context and rating context, respectively. Finally, we exploit the self-attention mechanism to capture the impact of temporal context and rating context on users’ preferences to predict items that users would click next. Experiments on three public benchmark datasets show that our proposed model (SATRAC) outperforms several state-of-the-art methods. The Hit@10 value of the SATRAC model on the three datasets (Movies-1M, Amazon-Movies, Amazon-CDs) increased by 0.73%, 2.73%, and 1.36%, and the NDCG@10 value increased by 5.90%, 3.47%, and 4.59%, respectively.

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33

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.

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34

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 (January 1, 2021): WI2—D_1–10. http://dx.doi.org/10.1527/tjsai.36-1_wi2-d.

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35

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.

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36

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.

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37

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 (February 2015): 1743–58. http://dx.doi.org/10.1016/j.eswa.2014.09.017.

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38

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 (June 1, 2023): 3192. http://dx.doi.org/10.11591/ijece.v13i3.pp3192-3203.

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Анотація:

<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 firstly build a context-aware recommender system that considers multi-user’s preferences and solves their conflicts by using unsupervised algorithms to deliver useful recommendation services. Secondly, we improve smart home’s responsive using parallel computing. The results reveal that the proposed method is better than existing approaches.</span>

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39

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 (March 31, 2015): 1–32. http://dx.doi.org/10.1007/s11257-015-9158-2.

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40

Sharma, Mugdha, Laxmi Ahuja, and Vinay Kumar. "A Hybrid Filtering Approach for an Improved Context-aware Recommender System." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 39–47. http://dx.doi.org/10.2174/1872212112666180813124358.

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Анотація:

Background: The domain of context-aware recommender approaches has made a substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. Objective: There are generally three algorithms which can be used to include context and those are - pre-filter approach, post-filter approach and contextual modeling. Each of the algorithms has their own drawbacks if any single approach is chosen. The goal of this work is to identify and propose a new hybrid approach which can include contextual information to improve the current movie recommender systems. Method: Post evaluation of various patents related to recommender systems, the proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. Results: The performance of the proposed system is measured in terms of precision of the system and ranking of the recommended movies to the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to the user. Conclusion: With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach. The proposed system will be vital for movie ticketing brands for the promotional purposes and various online content providers to recommend the accurate movies to their users.

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41

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 (January 28, 2019): 42. http://dx.doi.org/10.3390/info10020042.

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Анотація:

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 into the recommender systems. Given the importance of this type of texts and their usage along with opinion mining and contextual information extraction techniques for recommender systems, we present a systematic review on the recommender systems that explore both contextual information and opinion mining. This systematic review followed a well-defined protocol. Its results were based on 17 papers, selected among 195 papers identified in four digital libraries. The results of this review give a general summary of the current research on this subject and point out some areas that may be improved in future primary works.

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42

Jeong, Soo-Yeon, and Young-Kuk Kim. "Deep Learning-Based Context-Aware Recommender System Considering Contextual Features." Applied Sciences 12, no. 1 (December 21, 2021): 45. http://dx.doi.org/10.3390/app12010045.

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Анотація:

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 model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01–0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset.

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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 (March 16, 2018): 1. http://dx.doi.org/10.5539/cis.v11n2p1.

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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 of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.

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Rodríguez-García, Miguel Ángel, Rafael Valencia-García, Ricardo Colomo-Palacios, and Juan Miguel Gómez-Berbís. "BlindDate recommender: A context-aware ontology-based dating recommendation platform." Journal of Information Science 45, no. 5 (October 22, 2018): 573–91. http://dx.doi.org/10.1177/0165551518806114.

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Online dating sites have become popular platforms for those individuals who utilise the Internet to develop a personal or romantic relationship. Unlike typical recommenders systems, which attempt to suggest items such as films, songs, books and so on. According to a user’s interests, dating recommender systems provide services that people can use to find potential romantic partners. Since these services have a higher expectancy of users, online dating sites are considering the introduction of recommender systems in order to build an improved dating network. Different kinds of techniques based on content-based, collaborative filtering or hybrid techniques exist. In this article, we introduce BlindDate recommender, a context-based platform that utilises semantic technologies to describe users’ preferences more precisely. We utilise DBPedia repositories to obtain information that is subsequently used to enrich a previously generated ontology model. The instances inserted into the ontology enable the matching algorithms that we have generated to identify potential matches between users. In order to validate the performance of the platform, we utilise a real-world data set that has produced relevant results enhancing the accuracy compared with other well-known approaches and identifying the discriminant parameters used in the dating domain. More specifically, the proposed approach attains 0.79, 0.8 and 0.55 in the I-Precision, I-Recall and I-F-measure, respectively, when employed in separate topics.

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Liang, Zilu. "Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review." Electronics 11, no. 20 (October 19, 2022): 3384. http://dx.doi.org/10.3390/electronics11203384.

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The practice of quantified-self sleep tracking has become increasingly common among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization and are incapable of providing actionable recommendations that are tailored to users’ physical, behavioral, and environmental context. A promising solution to address this gap is the context-aware sleep health recommender system (CASHRS), an emerging research field that bridges ubiquitous sleep computing and context-aware recommender systems. This paper presents a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges identified in peer-reviewed publications that meet the characteristics of CASHRS. The analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.

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심재문, 강지욱, and Ohbyung Kwon. "Driver Preference Based Traffic Information Recommender Using Context-Aware Technology." Knowledge Management Society of Korea 11, no. 2 (June 2010): 75–93. http://dx.doi.org/10.15813/kmr.2010.11.2.005.

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Adomavicius, Gediminas, and Dietmar Jannach. "Preface to the special issue on context-aware recommender systems." User Modeling and User-Adapted Interaction 24, no. 1-2 (March 10, 2013): 1–5. http://dx.doi.org/10.1007/s11257-013-9139-2.

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LIU, QI, HAIPING MA, ENHONG CHEN, and HUI XIONG. "A SURVEY OF CONTEXT-AWARE MOBILE RECOMMENDATIONS." International Journal of Information Technology & Decision Making 12, no. 01 (January 2013): 139–72. http://dx.doi.org/10.1142/s0219622013500077.

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Mobile recommender systems target on recommending the right product or information to the right mobile users at anytime and anywhere. It is well known that the contextual information is often the key for the performances of mobile recommendations. Therefore, in this paper, we provide a focused survey of the recent development of context-aware mobile recommendations. After briefly reviewing the state-of-the-art of recommender systems, we first discuss the general notion of mobile context and how the contextual information is collected. Then, we introduce the existing approaches to exploit contextual information for modeling mobile recommendations. Furthermore, we summarize several existing recommendation tasks in the mobile scenarios, such as the recommendations in the tourism domain. Finally, we discuss some key issues that are still critical in the field of context-aware mobile recommendations, including the privacy problem, the energy efficiency issues, and the design of user interfaces.

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Javed, Umair, Kamran Shaukat, Ibrahim A. Hameed, Farhat Iqbal, Talha Mahboob Alam, and Suhuai Luo. "A Review of Content-Based and Context-Based Recommendation Systems." International Journal of Emerging Technologies in Learning (iJET) 16, no. 03 (February 12, 2021): 274. http://dx.doi.org/10.3991/ijet.v16i03.18851.

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In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

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Yang, Dan, Jing Zhang, Sifeng Wang, and XueDong Zhang. "A Time-Aware CNN-Based Personalized Recommender System." Complexity 2019 (December 18, 2019): 1–11. http://dx.doi.org/10.1155/2019/9476981.

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Recommender system has received tremendous attention and has been studied by scholars in recent years due to its wide applications in different domains. With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. The success of modern recommender system mainly depends on the understanding and application of the context of recommendation requests. However, when leveraging deep learning algorithms for recommendation, the impact of context information such as recommendation time and location is often neglected. In this paper, a time-aware convolutional neural network- (CNN-) based personalized recommender system TC-PR is proposed. TC-PR actively recommends items that meet users’ interests by analyzing users’ features, items’ features, and users’ ratings, as well as users’ time context. Moreover, we use Tensorflow distributed open source framework to implement the proposed time-aware CNN-based recommendation algorithm which can effectively solve the problems of large data volume, large model, and slow speed of recommender system. The experimental results on the MovieLens-1m real dataset show that the proposed TC-PR can effectively solve the cold-start problem and greatly improve the speed of data processing and the accuracy of recommendation.

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