Обзор методов учета контекста в системах коллаборативной фильтрации
Ключевые слова:
рекомендующие системы, коллаборативная фильтрация, контекст, персонализацияАннотация
По мнению многих исследователей, одним из наиболее действенных способов повышения качества рекомендующих систем является использование этими системами информации о текущем контексте. В статье произведен обзор основных методов использования информации о контексте в системах коллаборативной фильтрации. Особое внимание уделено разновидностям метода предварительной контекстной фильтрации и метода разложения матрицы предпочтений в связи с их перспективностью и широким распространением.Литература
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Yang S.-H., Long B., Smola A.J., Zha H., Zheng Z. Collaborative competitive filtering: learning recommender using context of user choice // SIGIR 2011: P. 295-304
Ackerman E., Guizzo E. 5 Technologies That Will Shape the Web // IEEE Spectrum. June 2011. P. 33-37
Adomavicius G., Mobasher B., Ricci F., Tuzhilin A. Context-aware recommender systems // AI Magazine. 2011. 32(3), P. 67-80
Adomavicius G., Sankaranarayanan R., Sen S., Tuzhilin A. Incorporating contextual information in recommender systems using a multidimensional approach // ACM Trans. Inf. Syst. January 2005. vol. 23, no. 1. P. 103-145
Adomavicius G., Tuzhilin A. oward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions // IEEE Transactions on Knowledge and Data Engineering. June 2005. vol. 17, no. 6, P. 734-749
Adomavicius G., Tuzhilin A. Context-Aware Recommender Systems // In Recommender Systems Handbook, Ricci F., Rokach L., Shapira B., Kantor P.B. (eds.), Springer, 2011. P. 217-256
Baltrunas L., Ludwig B., Ricci F. Matrix factorization techniques for context aware recommendation // In Proceedings of the fifth ACM conference on Recommender systems (RecSys '11). P. 301-304
Baltrunas L., Ricci F. Context-based splitting of item ratings in collaborative filtering // In Proceedings of the third ACM conference on Recommender systems (RecSys '09), ACM, 2009. P. 245-248
Baltrunas L., Ricci F. Context-Dependent Recommendations with Items Splitting // Proceedings of the 1st Italian Information Retrieval Workshop (IIR’10), January 27–28, 2010, Padua, Italy. P. 71-75
Burke R. Knowledge-based recommender systems // Encyclopedia of Library and Information Science, 2000. 69(32). P. 180-200
Campos P.G., Cantador I., Díez F. xploiting time contexts in collaborative filtering: an item splitting approach // In Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation (CaRR '13), New York, NY, USA, 2013. P. 3-6
Codina V., Ricci F., Ceccaroni L. Semantically-enhanced pre-filtering for context-aware recommender systems // In Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation (CaRR '13), New York, NY, USA, 2013. P. 15-18
Felfernig A., Burke R. Constraint-based recommender systems: technologies and research issues // In: Proceedings of the 10th International Conference on Electronic Commerce, ICEC’08, ACM, New York, NY, USA, 2008. P. 1–10
Jannach D., Kreutler G. Rapid development of knowledge-based conversational recommender applications with advisor suite // Journal of Web Engineering, 2007. vol. 2, no. 6. , P. 165–192
Karatzoglou A., Amatriain X., Baltrunas L., Oliver N. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering // In Proceedings of the 4th ACM Conference on Recommender Systems (RecSys'10), Barcelona, Spain, 2010. P. 79-86
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Koren Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model // In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2008. P. 426-434
Koren Y., Bell R. Advances in Collaborative Filtering // In Recommender Systems Handbook. Ricci F., Rokach L., Shapira B., Kantor P.B. (eds.) Springer, 2011. P. 145-186
Koren Y., Bell R., Volinsky C. Matrix factorization techniques for recommender systems // IEEE Computer, Aug 2009. vol. 42, no. 8. P. 30-37
Murthy S. Automatic construction of decision trees from data: A Multi-disciplinary survey // Data Mining and Knowledge Discovery, 1997. no. 2, P. 345-389
Oku K., Nakajima S., Miyazaki J., Uemura S. Context-aware svm for context-dependent information recommendation // In Proceedings of the 7th international Conference on Mobile Data Management, 2006. P. 109
Panniello U., Tuzhilin A., Gorgoglione M. et al. Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems // In Proceedings of the third ACM conference on Recommender systems (RecSys '09). ACM, New York, NY, USA, P. 265-268
Rahmani H., Piccart B., Fierens D., Blockeel H. Three complementary approaches to context aware movie recommendation // In Proceedings of the Workshop on Context-Aware Movie Recommendation (CAMRa '10). P. 57-60
Rendle S. Factorization Machines with libFM // ACM Trans. on Intell. Syst. Technol., May 2012. vol. 3, no. 3. P. 57:1-57:22
Rendle S., Gantner Z., Freudenthaler C., Schmidt-Thieme L. Fast context-aware recommendations with factorization machines // In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR '11). P. 635-644
Ricci F. Contextualizing Useful Recommendations // Personalization — UMAP 2012, Montreal, July 16-20, 2012 [Электронный ресурс] URL: http://www.inf.unibz.it/ ricci/Slides/Context-UMAP-2012-Ricci.pdf (Дата обращения: 31.05.2013)
Shi Y., Larson M., Hanjalic A. Mining contextual movie similarity with matrix factorization for context-aware recommendation // ACM Trans. Intell. Syst. Technol., February 2013. vol. 4, no. 1. P. 16:1-16:19
Wermser H., Rettinger A., Tresp V. Modeling and Learning Context-Aware Recommendation Scenarios using Tensor Decomposition // International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2011, P. 137-144
Yang S.-H., Long B., Smola A.J., Zha H., Zheng Z. Collaborative competitive filtering: learning recommender using context of user choice // SIGIR 2011: P. 295-304
Incorporating context into collaborative filtering systems: a survey
Abstract
It is recognized that incorporating context information into recommender systems is one of the most effective ways to increase their quality and predictive abilities. The paper surveys primary methods of enhancing collaborative filtering systems by taking actual context information into account. The focus is mostly on different flavours of contextual prefiltering and matrix factorization approaches which are the most popular and promising.Опубликован
2013-12-01
Как цитировать
Пономарев, А. В. (2013). Обзор методов учета контекста в системах коллаборативной фильтрации. Труды СПИИРАН, 7(30), 169-188. https://doi.org/10.15622/sp.30.11
Раздел
Статьи
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