Context-aware recommendation methods
Автор: Tosin Agagu, Thomas Tran
Статья в выпуске: 9 vol.10, 2018 года.
A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts have been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems. Several methods have been used to incorporate contextual information into traditional recommendation algorithms and data modeling techniques. These methods focus on incorporating contextual information to improve general recommendations for users rather than identifying the different context applicable to the user and providing recommendations geared towards those specific contexts. We develop two methods: the first method attaches user preference across multiple contextual conditions, assuming that user preference remains the same, but the suitability of items differs across different contextual conditions. The second method assumes that item suitability remains the same across different contextual conditions but user preference changes. We perform some experiments on the last.fm dataset to evaluate our methods. We also compared our work to other context-aware recommendation approaches. Our results show that grouping ratings by context and jointly factorizing with common factors improves prediction accuracy.
Context-aware, recommender system, coupled matrix factorization, context, recommendations
Короткий адрес: https://readera.ru/15016521
IDR: 15016521 | DOI: 10.5815/ijisa.2018.09.01
Список литературы Context-aware recommendation methods
- Cheng, Heng-Tze, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson. "Wide & deep learning for recommender systems." In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7-10. ACM, 2016.
- Dourish, Paul. "What we talk about when we talk about context." Personal and ubiquitous computing 8.1 (2004): 19-30.
- Rendle, Steffen, Zeno Gantner, Christoph Freudenthaler, and Lars Schmidt-Thieme. "Fast context-aware recommendations with factorization machines." In Proceedings of the 34th international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 635-644. ACM, 2011.
- Jannach, Dietmar, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. "Recommender Systems: An Introduction–Cambridge University Press." New York, 2010.–352 P (2010).
- Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-aware recommender systems." In Recommender Systems handbook, pp. 217-253. Springer US, 2011.
- Baltrunas, Linas, Bernd Ludwig, and Francesco Ricci. "Matrix factorization techniques for context aware recommendation." In Proceedings of the fifth ACM conference on Recommender systems, pp. 301-304. ACM, 2011.
- Zheng, Yong, Bamshad Mobasher, and Robin Burke. "Incorporating context correlation into context-aware matrix factorization." In Proceedings of the 2015 International Conference on Constraints and Preferences for Configuration and Recommendation and Intelligent Techniques for Web Personalization-Volume 1440, pp. 21-27. CEUR-WS. org, 2015.
- Li, Jiyun, Pengcheng Feng, and Juntao Lv. "ICAMF: improved context-aware matrix factorization for collaborative filtering." In Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on, pp. 63-70. IEEE, 2013.
- Nguyen, Trung, Alexandros Karatzoglou, and Linas Baltrunas. "Gaussian process factorization machines for context-aware recommendations." In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 63-72. ACM, 2014.
- Acar, Evrim, Gozde Gurdeniz, Morten A. Rasmussen, Daniela Rago, Lars O. Dragsted, and Rasmus Bro. "Coupled matrix factorization with sparse factors to identify potential biomarkers in metabolomics." In Data Mining Workshops (ICDMW), 2012 IEEE 12th International Conference on, pp. 1-8. IEEE, 2012.
- Li, Fangfang, Guandong Xu, and Longbing Cao. "Coupled item-based matrix factorization." In International Conference on Web Information Systems Engineering, pp. 1-14. Springer, Cham, 2014.
- Li, Fangfang, Guandong Xu, and Longbing Cao. "Coupled matrix factorization within non-iid context." In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 707-719. Springer International Publishing, 2015.
- Burke, Robin. "Recommender Systems: An Introduction, by Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich: Cambridge University Press, 2011. 336 pages. ISBN: 978-0-521-49336-9." (2012): 72-73.
- Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." IEEE Internet computing 7, no. 1 (2003): 76-80.
- Desrosiers, Christian, and George Karypis. "A comprehensive survey of neighborhood-based recommendation methods." Recommender systems handbook (2011): 107-144.
- Adomavicius, Gediminas, Jesse Bockstedt, Shawn Curley, and Jingjing Zhang. "Recommender systems, consumer preferences, and anchoring effects." In RecSys 2011 Workshop on Human Decision Making in Recommender Systems, pp. 35-42. 2011.
- Boström, Fredrik. "Andromedia-towards a context-aware mobile music recommender." (2008).
- Pagano, Roberto, Paolo Cremonesi, Martha Larson, Balázs Hidasi, Domonkos Tikk, Alexandros Karatzoglou, and Massimo Quadrana. "The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems." In RecSys, pp. 249-252. 2016.
- Adomavicius, Gediminas, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin. "Incorporating contextual information in recommender systems using a multidimensional approach." ACM Transactions on Information Systems (TOIS) 23, no. 1 (2005): 103-145.
- Takács, Gábor, István Pilászy, Bottyán Németh, and Domonkos Tikk. "Matrix factorization and neighbor based algorithms for the netflix prize problem." In Proceedings of the 2008 ACM conference on Recommender systems, pp. 267-274. ACM, 2008.
- Koren, Yehuda. "Factorization meets the neighborhood: a multifaceted collaborative filtering model." In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426-434. ACM, 2008.
- Wu, Mingrui. "Collaborative filtering via ensembles of matrix factorizations." Proceedings of KDD Cup and Workshop. Vol. 2007. 2007.
- Paterek, Arkadiusz. "Improving regularized singular value decomposition for collaborative filtering." In Proceedings of KDD cup and workshop, vol. 2007, pp. 5-8. 2007.
- Melville, Prem, and Vikas Sindhwani. "Recommender systems." In Encyclopedia of machine learning, pp. 829-838. Springer US, 2011.
- Bell, Robert M., Yehuda Koren, and Chris Volinsky. "The bellkor 2008 solution to the netflix prize." Statistics Research Department at AT&T Research (2008).
- Isinkaye, Folajimi, and Ojokoh. "Recommendation systems: Principles, methods and evaluation." Egyptian Informatics Journal 16, no. 3 (2015): 261-273.
- Shani, Guy, and Asela Gunawardana. "Evaluating recommendation systems." Recommender systems handbook (2011): 257-297
- Wilderjans, Tom, Eva Ceulemans, and Iven Van Mechelen. "Simultaneous analysis of coupled data blocks differing in size: A comparison of two weighting schemes." Computational Statistics & Data Analysis 53, no. 4 (2009): 1086-1098.