Gotowe bibliografie tematyczne / CONTEXT-AWARE RECOMMENDER

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  1. Artykuły w czasopismach

Gotowa bibliografia na temat „CONTEXT-AWARE RECOMMENDER”

Autor: Grafiati
Data publikacji: 11 września 2023
Data aktualizacji: 12 czerwca 2025

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Artykuły w czasopismach na temat "CONTEXT-AWARE RECOMMENDER"

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.

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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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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.

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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 (2021): 46–53. http://dx.doi.org/10.23939/acps2021.01.046.

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

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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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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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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.

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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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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.

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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.

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