Improved hybrid recommendation algorithm based on stacked denoising autoencoder
YANG Shuai1, WANG Juan2
1. National Engineering Research Center for Multimedia Software(School of Computer Science, Wuhan University), Wuhan Hubei 430072, China; 2. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education(School of Computer Science, Wuhan University), Wuhan Hubei 430072, China
Abstract:Concerning the problem that traditional collaborative filtering algorithm just utilizes users' ratings on items when generating recommendation, without considering users' labels or comments, which can not reflect users' real preference on different items and the prediction accuracy is not high and easily overfits, a Stacked Denoising AutoEncoder (SDAE)-based improved Hybrid Recommendation (SDHR) algorithm was proposed. Firstly, SDAE was used to extract items' explicit features from users' free-text labels. Then, Latent Factor Model (LFM) algorithm was improved, the LFM's abstract item features were replaced with extracted explicit ones to train matrix decomposition model. Finally, the user-item preference matrix was used to generate recommendations. Experimental tests on the dataset MovieLens showed that the accuracy of the proposed algorithm was improved by 38.4%, 16.1% and 45.2% respectively compared to the three recommendation models (including the model based on label-based weights with collaborative filtering, the model based on SDAE and extreme learning machine, and the model based on recurrent neural networks). The experimental results show that the proposed algorithm can make full use of items' free-text label information to improve recommendation performance.
[1] RICCI F, ROKACH L, SHAPIRA B, et al. Recommender Systems Handbook[M]. Berlin:Springer, 2015:127-131. [2] TUZHILIN A. Towards the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J]. IEEE Transactions of Knowledge and Data Engineering, 2005, 17(6):734-749. [3] ABHISHEK K, KULKARNI S, KUMAR V N, et al. A review on personalized information recommendation system using collaborative filtering[J]. International Journal of Computer Science and Information Technologies, 2011, 2(3):1272-1278. [4] HU L, CAO J, XU G, et al. Personalized recommendation via cross-domain triadic factorization[C]//Proceedings of the 22nd International World Wide Web Conference. New York:ACM, 2013:595-606. [5] SEVIL S G, KUCUKTUNC O, DUYGULU P, et al. Automatic tag expansion using visual similarity for photo sharing websites[J]. Multimedia Tools & Applications, 2010, 49(1):81-99. [6] WANG C, BLEI D M. Collaborative topic modeling for recommending scientific articles[C]//Proceedings of the 2011 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2011:448-456. [7] 赵宇翔,范哲,朱庆华.用户生成内容(UGC)概念解析及研究进展[J].中国图书馆学报,2012,38(5):68-81.(ZHAO Y X, FAN Z, ZHU Q H. Conceptualization and research progress on user-generated content[J]. Journal of Library Science in China, 2012, 38(5):68-81.) [8] 张敏,丁弼原,马为之,等.基于深度学习加强的混合推荐方法[J].清华大学学报(自然科学版),2017,57(10):1014-1021.(ZHANG M, DING B Y, MA W Z, et al. Hybrid recommendation approach enhanced by deep learning[J]. Journal of Tsinghua University (Science and Technology), 2017, 57(10):1014-1021.) [9] WANG H, WANG N, YEUNG D Y. Collaborative deep learning for recommender systems[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2014:1235-1244. [10] ALMAHAIRI A, KASTNER K, CHO K, et al. Learning distributed representations from reviews for collaborative filtering[C]//Proceedings of the 9th ACM Conference on Recommender Systems. New York:ACM, 2015:147-154. [11] GOLDER S A, HUBERMAN B A. The structure of collaborative tagging systems[J]. Journal of Information Science, 2006, 32(2):198-208. [12] ADRIAN B, SAUERMANN L, ROTH T. ConTag:a semantic tag recommendation system[C]//I-SEMANTICS 2007:Proceedings of the 3rd International Semantic Technology Conference. New York:ACM, 2007:297-304. [13] WANG H, SHI X, YEUNG Y. Relational stacked denoising autoencoder for tag recommendation[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. Menlo Park:AAAI Press, 2015:3052-3058. [14] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked de-noising autoencoders:learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11(12):3371-3408. [15] HINTON G, OSINDERO S, TEH Y. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554. [16] ZHANG W, WANG J, FENG W. Combining latent factor model with location features for event-based group recommendation[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2013:910-918. [17] YUAN K, LING Q, YIN W. On the convergence of decentralized gradient descent[J]. SIAM Journal on Optimization, 2016, 26(3):1835-1854. [18] REFAEILZADEH P, TANG L, LIU H. Cross-validation[M]//Encyclopedia of Database Systems. Berlin:Springer, 2009:532-538. [19] 郭彩云,王会进.改进的基于标签的协同过滤算法[J].计算机工程与应用,2016,52(8):56-61.(GUO C Y, WANG H J. Improved collaborative filtering algorithm based on tags[J]. Computer Engineering and Applications, 2016, 52(8):56-61.) [20] 潘昊,王新伟.基于SDAE及极限学习机模型的协同过滤应用研究[J].计算机应用研究,2017,34(8):2332-2335.(PAN H, WANG X W. Study on collaborative filtering recommendation algorithm based on extreme learning machine stacked denoising autoencodes[J]. Application Research of Computers, 2017, 34(8):2332-2335.)