A Rating-Based Integrated Recommendation Framework with Improved Collaborative Filtering Approaches

Shulin Cheng, Bofeng Zhang, Guobing Zou


Collaborative filtering (CF) approach is successfully applied in the rating prediction of personal recommendation. But individual information source is leveraged in many of them, i.e., the information derived from single perspective is used in the user-item matrix for recommendation, such as user-based CF method mainly utilizing the information of user view, item-based CF method mainly exploiting the information of item view. In this paper, in order to take full advantage of multiple information sources embedded in user-item rating matrix, we proposed a rating-based integrated recommendation framework of CF approaches to improve the rating prediction accuracy. Firstly, as for the sparsity of the conventional item-based CF method, we improved it by fusing the inner similarity and outer similarity based on the local sparsity factor. Meanwhile, we also proposed the improved user-based CF method in line with the user-item-interest model (UIIM) by preliminary rating. Second, we put forward a background method called user-item-based improved CF (UIBCF-I), which utilizes the information source of both similar items and similar users, to smooth itembased and user-based CF methods. Lastly, we leveraged the three information sources and fused their corresponding ratings into an Integrated CF model (INTE-CF). Experiments demonstrate that the proposed rating-based INTE-CF indeed improves the prediction accuracy and has strong robustness and low sensitivity to sparsity of dataset by comparisons to other mainstream CF approaches.


personalized recommendation, collaborative filtering, rating integration

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Anand D., Bharadwaj K.K. (2013); Pruning trust-distrust network via reliability and risk estimates for quality recommendations, Social Network Analysis and Mining, 3(1), 65-84, 2013. https://doi.org/10.1007/s13278-012-0049-9

Bobadilla J., Ortega F., Hernando A.; Alcal J. (2011); Improving collaborative filtering recommender system results and performance using genetic algorithms, Knowledge-based systems, 24(8), 1310-1316, 2011. https://doi.org/10.1016/j.knosys.2011.06.005

Breese J.S., Heckerman D., Kadie C. (1998); Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 43-52, 1998.

Choi K., Suh Y. (2013); A new similarity function for selecting neighbors for each target item in collaborative filtering, Knowledge-Based Systems, 37, 146-153, 2013. https://doi.org/10.1016/j.knosys.2012.07.019

Das A. S., Datar M., Garg A., Rajaram S. (2007); Google news personalization: scalable online collaborative filtering, Proceedings of the 16th international conference on World Wide Web, 271-280, 2007.

Deng A.L., Zhu Y.Y., Shi B. (2003); A collaborative filtering recommendation algorithm based on item rating prediction, Journal of Software (Chinese), 14(9), 1621-1628, 2003.

Deshpande M., Karypis G. (2004); Item-based top-n recommendation algorithms, ACM Transactions on Information Systems (TOIS), 22(1), 143-177, 2004. https://doi.org/10.1145/963770.963776

Ghazanfar M.A., Pršgel-Bennett A. (2013); The Advantage of Careful Imputation Sources in Sparse Data-Environment of Recommender Systems: Generating Improved SVD-based Recommendations, Informatica (Slovenia), 37(1), 61-92, 2013.

Goldberg D., Nichols D., Oki B.M., Terry D. (1992); Using collaborative filtering to weave an information tapestry, Communications of the ACM, 35(12), 61-70, 1992. https://doi.org/10.1145/138859.138867

Koren Y. (2010); Collaborative filtering with temporal dynamics, Communications of the ACM, 53(4), 89-97, 2010. https://doi.org/10.1145/1721654.1721677

Li Q., Sato I., Murakami Y. (2007); Efficient stochastic gradient search for automatic image registration, International Journal of Simulation Modelling (IJSIMM), 6(2), 114-123, 2007. https://doi.org/10.2507/IJSIMM06(2)S.06

Li W., Ye Z., Xin M., Jin Q. (2015); Social recommendation based on trust and influence in SNS environments, Multimedia Tools and Applications, 1-18, 2015.

Linden G., Smith B., York J. (2003); Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet computing, 7(1), 76-80, 2003. https://doi.org/10.1109/MIC.2003.1167344

Liu N.N., Zhao M., Yang Q. (2009); Probabilistic latent preference analysis for collaborative filtering, Proceedings of the 18th ACM conference on Information and knowledge management, 759-766, 2009.

Lu Z., Dou Z., Lian J., Xie X., Yang Q. (2015); Content-Based Collaborative Filtering for News Topic Recommendation, Twenty-Ninth AAAI Conference on Artificial Intelligence, 217-223, 2015.

Ma H., King I., Lyu M.R. (2007); Effective missing data prediction for collaborative filtering, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, 39-46, 2007.

Moin A., Ignat C.L. (2014); Hybrid weighting schemes for collaborative filtering (Doctoral dissertation, INRIA Nancy), France, 2014.

Nilashi M., bin Ibrahim O., Ithnin N. (2014); Hybrid recommendation approaches for multicriteria collaborative filtering, Expert Systems with Applications, 41(8), 3879-3900, 2014. https://doi.org/10.1016/j.eswa.2013.12.023

Park D. H., Kim H. K., Choi I.Y., Kim J.K. (2012); A literature review and classification of recommender systems research, Expert Systems with Applications, 39(11), 10059-10072, 2012. https://doi.org/10.1016/j.eswa.2012.02.038

Paterek A. (2007); Improving regularized singular value decomposition for collaborative filtering, Proceedings of KDD cup and workshop, 5-8, 2007.

Ricci F., Rokach L., Shapira B. (2011); Introduction to recommender systems handbook, Springer, 2011.

Sarwar B., Karypis G., Konstan J., Riedl J. (2001); Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, 285-295, 2001.

Shi Y., Larson M., Hanjalic A. (2014); Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges, ACM Computing Surveys (CSUR), 47(1), 3-45, 2014.

Song R.P., Wang B., Huang G.M., Liu Q.D., Hu R.J., Zhang R.S. (2014); A hybrid recommender algorithm based on an improved similarity method, Applied Mechanics and Materials, 475, 978-982, 2014.

Wang J., De Vries A.P., Reinders M.J. (2006); Unifying user-based and item-based collaborative filtering approaches by similarity fusion, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, 501-508, 2006.

Xu S. Y., Raahemi B. (2016); A Semantic-based service discovery framework for collaborative environments, International Journal of Simulation Modelling (IJSIMM), 15(1), 83-96, 2016. https://doi.org/10.2507/IJSIMM15(1)7.326

Yang X., Guo Y., Liu Y., Steck H. (2014); A survey of collaborative filtering based social recommender systems, Computer Communications, 41, 1-10, 2014. https://doi.org/10.1016/j.comcom.2013.06.009

Zenebe A., Zhou L., Norcio, A.F. (2010); User preferences discovery using fuzzy models, Fuzzy Sets and Systems, 161(23), 3044-3063, 2010. https://doi.org/10.1016/j.fss.2010.06.006

DOI: http://dx.doi.org/10.15837/ijccc.2017.3.2692


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INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.


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