HuaXin Zhang
ABSTRACT
In the era of information overload, both information consumers and information producers have encountered great challenges: for information consumers, it is very difficult to find information of interest from a large amount of information. The recommendation system is an important tool to resolve this contradiction. Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start and data sparseness problems. This paper took commodity recommendation as to the research object and proposed a recommendation algorithm that combines Spectral-CF and FP-Growth. Firstly, Firstly, the association rule algorithm FP-Growth is mine the association rules of the target user and the target item directly, and recommend the collection of items with higher similarity for the user. Secondly, using a spectral collaborative filtering algorithm Perform convolution operations in the spectral domain. Finally, providing the final result by combining the Spectral-CF and FP-Growth recommendation. The experimental results on the MovieLens dataset show that the proposed method can better solve the problem of data sparseness and cold-start problems, improvement the accuracy of recommendation.
- Goldberg D, Nichols D, Oki B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communicati-ons of ACM, 1992, 35(12):61--70.Google Scholar
Digital Library
- Su X Y, Khoshgoftaar T M. Collaborative filtering for multi-class data using belief nets algorithms[C]// Proceedings of the International Conference on Tools with Artificial Inter-lligence. Arlington, USA: IEEE Computer Society, 2006: 497--504.Google Scholar
- Yu K, Schwaighofer A, Tresp V, et al. Probabilistic memory-based collaborative filtering[J]. IEEE Transactions on Kno-wledge and Data Engineering, 2004, 16(1):56--69.Google ScholarDigital Library
- Ben J, Dan F, Jon H. The Adaptive Web: Methods and Strategies of Web Personalization[M]. Berlin Heidelberg: Springer, 2004.Google Scholar
- Sarwar B, Karypis G, Konstan J, et al. Analysis of recommendation algorithms for E-commerce[C]// Pro-ceedings of the ACM E-Commerce. NewYork, USA: ACM Press, 2000:158--167.Google Scholar
- Leung C W K, Chan S C F, Chung F L. A collaborative filtering framework based on fuzzy association rules and multi-level similarity[J]. Knowledge and Information Sy-stems, 2006, 10(3):357--381.Google ScholarDigital Library
- Sobhanam H, Mariappan A K. Addressing cold start problem in recommender systems using association rules and cluster-ing technique[C]// Proceeding of the International Confere-nceon on Computer Communiation and Informatics, Coimb-atre:IEEE press, 2013:1--5.Google Scholar
- Tyagi S, Bharadwaj K. Enhanced new user recommenda-tions based on quantitative association rule mining[J]. Proce-dia Computer Science, 2012, 10: 102--109.Google ScholarCross Ref
- Ye H W. A personalized collaborative filtering recommend-ation using association rules mining and self-organizing map[J]. Journal of Software, 2011, 6(4):732--739.Google ScholarCross Ref
- Yang H. Improved collaborative filtering recommendation algorithm based on weighted association rules[J]. Applied Mechanics and Materials, 2013, (411--414):94--97Google ScholarCross Ref
- WEI J, HE J, CHEN K, et al. Collaborative filtering and deep learning based recommendation system for cold start items-[J].Expert Systems with Applications, 2017, 69:29--39.Google ScholarCross Ref
- WANG H, WANG N, YEUNG D Y.Collaborative deep learning for recommender systems[C]//Proceeding of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2018:1235--1244Google Scholar
- Lei Zheng, Chun-Ta Lu, Fei Jiang, Jiawei Zhang, and Philip S. Yu. 2018. Spectral Collaborative Filtering. In Twelfth ACM Conference on Recommender Systems (RecSys '18), October 2-7, 2018, Vancouver, BC, Canada.ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3240323.3240343Google Scholar
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommend-ation algorithms.In Proceedings of the 10th international conference on World Wide Web ACM, 285--295.Google Scholar
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intel-igence. AUAIPress, 452--461.Google Scholar
- Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua.2016. Fast matrix factorization for online recommend-dation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 549--558.Google Scholar
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filter-ing. In Proceedings of the 26th International Conference on World Wide Web, WWW2017, Perth, Australia, April 3-7, 2017.173--182. https://doi.org/10.1145/3038912.3052569Google Scholar
- Deng Cai, Xiao fei He, Xiao yun Wu, and Jiawei Han. 2008. Non-negativematrix factorization on manifold. In Data Min-ing, 2008. ICDM'08. Eighth IEEE International Conference on. IEEE, 63--72Google Scholar
- Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph Convolutional Matrix Completion. arXiv pre-print arXiv:1706.02263 (2017).Google Scholar
- Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734--749.Google ScholarDigital Library
- Lin W, Alvarez S A, Ruiz C. Efficient adaptive-support ass-ociation rule mining for recommender systems[J]. Data Min-ing and Knowledge Discovery, 2014, 6(1): 83--105.Google ScholarDigital Library
- Koren Y. Factorization meets the neighborhood: A multifaceted collaborative filtering model [C]// Proceedings of 14th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining. Las Vegas, USA: ACM Press, 2008: 426--434.Google Scholar
- Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann machines for collaborative filtering [C]// Proceedings of the 24th International Conference on Machine learning. Corva-llis, USA: ACM Press:2007: 791--798.Google Scholar
Index Terms
- Integrating Spectral-CF and FP-Growth for Recommendation
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