ABSTRACT
In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.
- Acar, E. and Yener, B., 2009. Unsupervised multiway data analysis: A literature survey. Knowledge and Data Engineering, IEEE Transactions on 21, 1, 6--20. Google ScholarDigital Library
- Adomavicius, G. and Tuzhilin, A., 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on 17, 6, 734--749. Google ScholarDigital Library
- Adomavicius, G. and Tuzhilin, A., 2011. Context-aware recommender systems. In Recommender systems handbook Springer, 217--253.Google Scholar
- Bar, A., Rokach, L., Shani, G., Shapira, B., and Schclar, A., 2013. Improving simple collaborative filtering models using ensemble methods. In Multiple Classifier Systems Springer, 1--12.Google Scholar
- Brand, M., 2002. Incremental singular value decomposition of uncertain data with missing values. In Computer Vision---ECCV 2002 Springer, 707--720. Google ScholarDigital Library
- Deshpande, M. and Karypis, G., 2004. Item-based top-n recommendation algorithms. ACM T INFORM SYST 22, 1, 143--177. Google ScholarDigital Library
- Desrosiers, C. and Karypis, G., 2011. A comprehensive survey of neighborhood-based recommendation methods. In Recommender systems handbook Springer, 107--144.Google Scholar
- Herlocker, J., Konstan, J., Terveen, L., and Riedl, J., 2004. Evaluating Collaborative Filtering Recommender Systems. ACM T INFORM SYST 22, 1 (01/2004), 5--53. DOI= http://dx.doi.org/10.1145/223904.223929. Google ScholarDigital Library
- Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., and Stumme, G., 2007. Tag recommendations in folksonomies. In Knowledge Discovery in Databases: PKDD 2007 Springer, 506--514. Google ScholarDigital Library
- Karatzoglou, A., Amatriain, X., Baltrunas, L., and Oliver, N., 2010. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems ACM, 79--86. DOI= http://dx.doi.org/10.1145/1864708.1864727. Google ScholarDigital Library
- Knowledge and Data Engineering Group, University of Kassel, 2007, Benchmark Folksonomy Data from BibSonomy. http://www.kde.cs.uni-kassel.de/bibsonomy/dumps/Google Scholar
- Kolda, T. G. and Bader, B. W., 2009. Tensor decompositions and applications. SIAM REV 51, 3, 455--500. DOI= http://dx.doi.org/10.1137/07070111X. Google ScholarDigital Library
- Koren, Y., Bell, R., and Volinsky, C., 2009. Matrix factorization techniques for recommender systems. COMPUTER 42, 8, 30--37. DOI= http://dx.doi.org/10.1109/MC.2009.263. Google ScholarDigital Library
- Lee, J.-S. and Olafsson, S., 2009. Two-way cooperative prediction for collaborative filtering recommendations. Expert Systems with Applications 36, 3, 5353--5361. Google ScholarDigital Library
- Liang, H., Xu, Y., Li, Y., Nayak, R., and Tao, X., 2010. Connecting users and items with weighted tags for personalized item recommendations. In Proceedings of the Proceedings of the 21st ACM conference on Hypertext and hypermedia (Toronto, Ontario, Canada2010), ACM, 51--60. DOI= http://dx.doi.org/10.1145/1810617.1810628. Google ScholarDigital Library
- Marinho, L. B., Hotho, A., Jäschke, R., Nanopoulos, A., Rendle, S., Schmidt-Thieme, L., Stumme, G., and Symeonidis, P., 2012. Recommender systems for social tagging systems. Springer. Google ScholarDigital Library
- Rendle, S., Balby Marinho, L., Nanopoulos, A., and Schmidt-Thieme, L., 2009. Learning optimal ranking with tensor factorization for tag recommendation. In Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining ACM, 727--736. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., 2002. Incremental singular value decomposition algorithms for highly scalable recommender systems. In Fifth International Conference on Computer and Information Science Citeseer, 27--28.Google Scholar
- Sen, S., Vig, J., and Riedl, J., 2009. Tagommenders: connecting users to items through tags. In Proceedings of the 18th international conference on World wide web ACM, Madrid, Spain, 671--680. DOI= http://dx.doi.org/10.1145/1526709.1526800. Google ScholarDigital Library
- Su, X. and Khoshgoftaar, T. M., 2009. A survey of collaborative filtering techniques. LECT NOTES ARTIF INT 2009. DOI= http://dx.doi.org/10.1155/2009/421425. Google ScholarCross Ref
- Symeonidis, P., Nanopoulos, A., and Manolopoulos, Y., 2010. A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE T KNOWL DATA EN 22, 2, 179--192. DOI= http://dx.doi.org/10.1109/TKDE.2009.85. Google ScholarCross Ref
- Symeonidis, P., Nanopoulos, A., Papadopoulos, A., and Manolopoulos, Y., 2006. Scalable collaborative filtering based on latent semantic indexing. In Proc. 21st Assoc. for Advancement of Artificial Intelligence (AAAI) Workshop Intelligent Techniques for Web Personalization (ITWP'06) AAAI, 1--9.Google Scholar
- Tso-Sutter, K. H. L., Marinho, L. B., and Schmidt-Thieme, L., 2008. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the 2008 ACM symposium on Applied computing ACM, 1995--1999. DOI= http://dx.doi.org/10.1145/1363686.1364171. Google ScholarDigital Library
- Wang, J., De Vries, A. P., and Reinders, M. J. T., 2006. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval ACM, 501--508. DOI= http://dx.doi.org/10.1145/1148170.1148257. Google ScholarDigital Library
- Wetzker, R., Zimmermann, C., and Bauckhage, C., 2008. Analyzing social bookmarking systems: A del. icio. us cookbook. In Proceedings of the ECAI 2008 Mining Social Data Workshop IOS Press, 26--30.Google Scholar
Index Terms
- Refining User and Item Profiles based on Multidimensional Data for Top-N Item Recommendation
Recommendations
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
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