Abstract
Fuzzy co-clustering is a promising approach for efficiently realizing collaborative filtering, in which personalized recommendation is achieved by summarizing the intrinsic user-item preferences through dual clustering of users and items in cooccurrence information matrices. In cases of applying fuzzy co-clustering, we can select roughly three partition models supported by different partition constraints: user targeting partition, (weak) dual exclusive partition and their hybrid approach. This paper presents a comparative study on the applicability of the three partition models to collaborative filtering tasks through empirical demonstration with two real world data sets. The experimental results reveal that user targeting partition is most suitable for the task while dual exclusive partition can also be used with sparse data sets.
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References
Aggarwal CC (2016) Recommender Systems. Springer, New York
Badaro G, Hajj H, El-Hajj W, Nachman L (2013) A hybrid approach with collaborative filtering for recommender systems. In: Proceedings of 9th international wireless communications and mobile computing conference, pp 349–354
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York
Bezdek JC, Hathaway RJ, Huband JM (2007) Visual assessment of clustering tendency for rectangular dissimilarity matrices. IEEE Trans Fuzzy Syst 15:890–903
Cui B, Jin H, Liu Z, Deng J (2015) Improved collaborative filtering with intensity-based contraction. J Ambient Intell Hum Comput 6:661–674
Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1–38
Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of 22nd conference on research and development in information retrieval, pp 230–237
Holmes I, Harris K, Quince C (2012) Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS One 7–2:e30126
Honda K (2016) Fuzzy co-clustering and application to collaborative filtering. In: Huynh VN, Inuiguchi M, Le B, Le B, Denoeux T (eds) Integrated uncertainty in knowledge modelling and decision making (IUKM 2016), Lecture Notes in Computer Science, vol 9978. Springer, New York, pp 16–23
Honda K, Notsu A, Ichihashi H (2010) Collaborative filtering by sequential user-item co-cluster extraction from rectangular relational data. Int J Knowl Eng Soft Data Paradig 2:312–327
Honda K, Muranishi M, Notsu A, Ichihashi H (2013) FCM-type cluster validation in fuzzy co-clustering and collaborative filtering applicability. Int J Comput Sci Netw Secur 13:24–29
Honda K, Oshio S, Notsu A (2015) Fuzzy co-clustering induced by multinomial mixture models. J Adv Comput Intell Inform 19:717–726
Honda K, Nakano T, Oh C-H, Ubukata S, Notsu A (2015) Partially exclusive item partition in MMMs-induced fuzzy co-clustering and its effects in collaborative filtering. J Adv Comput Intell Intell Inform 19:810–817
Konstan JA, Miller BN, Maltz D, Herlocker JL, Gardon LR, Riedl J (1997) Grouplens: applying collaborative filtering to usenet news. Commun ACM 40–3:77–87
Koren Y (2010) Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data 4–1:1
Kummamuru K, Dhawale A, Krishnapuram R (2003) Fuzzy co-clustering of documents and keywords. Proc IEEE Int Conf Fuzzy Syst 2:772–777
Lee D-D, Seung H-S (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401–6755:788–791
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, pp 76–80
MacQueen JB (1967) Some methods of classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley symposium on mathematics, statistics and probability, pp 281-297
Miyamoto S, Ichihashi H, Honda K (2008) Algorithms for fuzzy clustering. Springer, New York
MovieLens Web Page. http://www.movielens.org/
Oh C-H, Honda K, Ichihashi H (2001) Fuzzy clustering for categorical multivariate data. In: Proceedings of joint 9th IFSA world congress and 20th NAFIPS international conference, pp 2154–2159
Oja E, Ilin A, Luttinen J, Yang Z (2010) Linear expansions with nonlinear cost functions: modeling, representation, and partitioning. In: 2010 IEEE world congress on computational intelligence, plenary and invited lectures, pp 105–123
Pla Karidi D, Stavrakas Y, Vassiliou Y (2017) Tweet and followee personalized recommendations based on knowledge graphs. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-017-0491-7
Rigouste L, Cappé O, Yvon F (2007) Inference and evaluation of the multinomial mixture model for text clustering. Inf Process Manag 43(5):1260–1280
Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009–421425:1–19
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240–4857:1285–1289
Tjhi W-C, Chen L (2006) A partitioning based algorithm to fuzzy co-cluster documents and words. Pattern Recognit Lett 27:151–159
Tjhi W-C, Chen L (2008) A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data. Fuzzy Sets Syst 159:371–389
Tsuda K, Minoh M, Ikeda K (1996) Extracting straight lines by sequential fuzzy clustering. Pattern Recognit Lett 17:643–649
Xiao J, Wang M, Jiang B, Li J (2017) A personalized recommendation system with combinational algorithm for online learning. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-017-0466-8
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Honda, K., Ubukata, S. & Notsu, A. A comparative study on effects of some exclusive conditions in fuzzy co-clustering for collaborative filtering. J Ambient Intell Human Comput 14, 14589–14594 (2023). https://doi.org/10.1007/s12652-018-0789-0
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DOI: https://doi.org/10.1007/s12652-018-0789-0