Abstract Hybrid recommender is a significant way for solving the defect of various single recommender methods.A hybrid recommender algorithm based on graph is proposed in this paper.Various recommended factors are fused into graph to produce the final recommendation results. The similarity between items is calculated using the content attribute of recommended items to build correlation matrix of the nearest graph. The item profile is constructed according to the scored record of item to generate a vector function. Grounded on the above, a regular framework is used to build a graph-based learning model by combining correlation matrix and vector function and realize a personalization recommendation based on graph. By the experiments on MovieLens datasets and transaction data of Amazon online mart, the effectiveness of the proposed algorithm is verified.
Fund:Supported by National Natural Science Foundation of China(No.61603065), Scientific and Technological Research Program of Chongqing Municipal Education Commission(No.kj1500920)
About author:: (ZHANG Yihao(Corresponding author), born in 1982, Ph.D., lecturer. His research interests include recommender system and machine learning.) (LIU Xiaoyang, born in 1980, Ph. D., associate professor. His research interests include Chinese information processing.) (LIU Wanping, born in 1986, Ph.D., associate professor. His research interests include complex network.) (ZHU Changpeng, born in 1981, Ph.D., lecturer. His research interests include virtual machine.)
[1] GOMEZ-RODRIGUEZ M, GUMMADI K P, SCHLKOPF B. Quantifying Information Overload in Social Media and Its Impact on Social Contagions [C/OL]. [2016-08-25]. https://people.mpi-sws.org/~gummadi/papers/icwsm2014-overload.pdf. [2] QIAN X M, FENG H, ZHAO G S, et al. Personalized Recommendation Combining User Interest and Social Circle. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7): 1763-1777. [3] YANG X W, GUO Y, LIU Y, et al. A Survey of Collaborative Filtering Based Social Recommender Systems. Computer Communications, 2014, 41: 1-10. [4] PAMPIN H J C, JERBI H, O'MAHONY M P. Evaluating the Relative Performance of Collaborative Filtering Recommender Systems. Journal of Universal Computer Science, 2015, 21(13): 1849-1868. [5] ANAVA O, GOLAN S, GOLBANDI N, et al. Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design // Proc of the 24th International Conference on World Wide Web. New York, USA: ACM, 2015: 45-54. [6] BOGERS T, KOOLEN M, CANTADOR I. Workshop on New Trends in Content-Based Recommender Systems // Proc of the 8th ACM Conference on Recommender Systems. New York, USA: ACM, 2014: 379-380. [7] YOSHII K, GOTO M, KOMATANI K, et al. An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model. IEEE Transactions on Audio, Speech, and Language Processing, 2008, 16(2): 435-447. [8] DOOMS S. Dynamic Generation of Personalized Hybrid Recommender Systems // Proc of the 7th ACM Conference on Recommender Systems. New York, USA: ACM, 2013: 443-446. [9] MOURO F, ROCHA L, KONSTAN J A, et al. Exploiting Non-content Preference Attributes through Hybrid Recommendation Method // Proc of the 7th ACM Conference on Recommender Systems. New York, USA: ACM, 2013: 177-184. [10] SON L H. HU-FCF: A Hybrid User-Based Fuzzy Collaborative Filtering Method in Recommender Systems. Expert Systems with Applications, 2014, 41(15): 6861-6870. [11] BRAUNHOFER M. Hybridisation Techniques for Cold-Starting Context-Aware Recommender Systems // Proc of the 8th ACM Conference on Recommender Systems. New York, USA: ACM, 2014: 405-408. [12] LEE K, LEE K. Escaping Your Comfort Zone: A Graph-Based Recommender System for Finding Novel Recommendations among Relevant Items. Expert Systems with Applications, 2015, 42(10): 4851-4858. [13] LIEN D T, ANH N X, PHUONG N D. A Graph Model for Hybrid Recommender System // Proc of the 7th International Conference on Knowledge and Systems Engineering. Washington, USA: IEEE, 2015: 138-143. [14] 廖志芳,李 玲,刘丽敏,等.三部图张量分解标签推荐算法.计算机学报, 2012, 35(12): 2625-2632. (LIAO Z F, LI L, LIU L M, et al. A Tripartite Decomposition of Tensor for Social Tagging. Chinese Journal of Computers, 2012, 35(12): 2625-2632.) [15] 吕 晨,姜 伟,虎嵩林.一种基于新型图模型的API推荐系统.计算机学报, 2015, 38(11): 2172-2187. (L C, JIANG W, HU S L. APISynth: A New Graph-Based API Recommender System. Chinese Journal of Computers, 2015, 38(11): 2172-2187.) [16] 刘梦娟,王 巍,李扬曦,等. AttentionRank+:一种基于关注关系与多用户行为的图推荐算法.计算机学报, 2017, 40(3): 634-648. (LIU M J, WANG W, LI Y X, et al. AttentionRank+: A Graph-Based Recommendation Combining Attention Relationship and Multi-behaviors. Chinese Journal of Computers, 2017, 40(3): 634-648.) [17] CAI D, HE X F, HAN J W, et al. Graph Regularized Nonnegative Matrix Factorization for Data Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1548-1560. [18] LI Z C, LIU J, TANG J H, et al. Robust Structured Subspace Learning for Data Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(10): 2085-2098. [19] BREESE J S, HECKERMAN D, KADIE C. Empirical Analysis of Predictive Algorithms for Collaborative Filtering // Proc of the 14th Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann Publishers, 1998: 43-52. [20] LINDEN G, SMITH B, YORK J. Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 2003, 7(1): 76-80. [21] 丁少衡,姬东鸿,王路路.基于用户属性和评分的协同过滤推荐算法.计算机工程与设计, 2015, 36(2): 487-491, 497. (DING S H, JI D H, WANG L L. Collaborative Filtering Recommendation Algorithm Based on User Attributes and Scores. Computer Engineering and Design, 2015, 36(2): 487-491, 497.) [22] 黄 波,严宣辉,林建辉.基于联合非负矩阵分解的协同过滤推荐算法.模式识别与人工智能, 2016, 29(8): 725-734. (HUANG B, YAN X H, LIN J H. Collaborative Filtering Recommendation Algorithm Based on Joint Nonnegative Matrix Factorization. Pattern Recognition and Artificial Intelligence, 2016, 29(8): 725-734.)
2017, Vol. 30 
