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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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
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
Karatzoglou A., Baltrunas L., Church K., Böhmer M. Climbing the app wall: enabling mobile app discovery through context-aware recommendations // In Proceedings of the 21st ACM international conference on Information and knowledge management (CIKM '12). P. 2527-2530
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
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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