1. College of Computer science and engineering, Northwest Normal University, Lanzhou 730070, China 2. College of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
Traditional recommendation algorithms predict the latent interest of an active user by collecting rating information from other similar users or items. Recently, more and more recommendation systems attempt to involve social relations to improve recommendation performance. However, the existing works either leave out the user reliability or cannot capture the correlation between two users who are similar but not socially connected. Besides, they also take the trust value between users either 0 or 1, thus degenerating the prediction accuracy. In this paper, we propose an efficient social affect model, multiaffect(ed), for recommendation via incorporating both users’ reliability and influence propagation. Specifically, the model contains two main components, i.e., computation of user reliability and influence propagation, designing of user-shared feature space. Firstly, a reliability calculation strategy based on user similarity is developed for measuring the recommendation accuracy between users. Then, the factor of influence propagation relationship among users is taken into consideration. Finally, the multi-affect(ed) model is developed with user-shared feature space to generate the predicted ratings.
S J Ji, W Yang, S H Guo, D KW Chiu, C J Zhang, X Y Yuan. Asymmetric response aggregation heuristics for rating prediction and recommendation. Applied Intelligence, 2020, 50(5): 1416–1436 https://doi.org/10.1007/s10489-019-01594-2
2
Z Li, H K Zhao, Q Liu, Z Y Huang, T Mei, E H Chen. Learning from history and present: next-item recommendation via discriminatively ex ploiting user behaviors. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018, 1734–1743 https://doi.org/10.1145/3219819.3220014
3
W Z Zhao, H F Ma, Z X Li, X Ao, N Li. SBRNE: an improved unified framework for social and behavior recommendations with network embedding. In: Proceedings of the 24th International Conference on Database Systems for Advanced Applications. 2019, 555–571 https://doi.org/10.1007/978-3-030-18579-4_33
4
J Bobadilla, A Hernando, F Ortega, A Gutierrez. Collaborative filtering based on significances. Information Sciences, 2012, 185(1): 1–17 https://doi.org/10.1016/j.ins.2011.09.014
5
C C Chen, Y H Wan, M C Chung, Y C Sun. An effective recommendation method for cold start new users using trust and distrust networks. Information Sciences, 2013, 224: 19–36 https://doi.org/10.1016/j.ins.2012.10.037
6
G N Hu, X Y Dai, F Y Qiu, R Xia, T Li, S J Huang, J J Chen. Collaborative filtering with topic and social latent factors incorporating implicit feedback. ACM Transactions on Knowledge Discovery from Data, 2018, 12(2): 1–30 https://doi.org/10.1145/3127873
7
H Ma, H Yang, M R Lyu, I King. SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management. 2008, 931–940 https://doi.org/10.1145/1458082.1458205
G Guo, J Zhang, D Thalmann. A simple but effective method to incorporate trusted neighbors in recommender systems. In: Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization. 2012, 114–125 https://doi.org/10.1007/978-3-642-31454-4_10
10
D Zhang, C H Hsu, M Chen, Q Chen, N X Xiong, J L Mauri. Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. IEEE Transactions on Emerging Topics in Computing, 2014, 2(2): 239–250 https://doi.org/10.1109/TETC.2013.2283233
11
X Guo, S C Yin, Y W Zhang, W Li, Q He. Cold start recommendation based on attribute-fused singular value decomposition. IEEE Access, 2019, 7: 11349–11359 https://doi.org/10.1109/ACCESS.2019.2891544
12
H Fang, Y Bao, J Zhang. Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 30–36
13
G Guo, J Zhang, N Yorke-Smith. A novel recommendation model regularized with user trust and item ratings. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1607–1620 https://doi.org/10.1109/TKDE.2016.2528249
14
H Ma, D Zhou, C Liu, M R Lyu, I King. Recommender systems with social regularization. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 287–296 https://doi.org/10.1145/1935826.1935877
15
W Q Fan, Y Ma , Q Li , Y He, Y H E Zhao, J L Tang, DW Yin. Graph neural networks for social recommendation. In: Proceedings of the 28th World Wide Web Conference. 2019, 417–426 https://doi.org/10.1145/3308558.3313488
16
S Ouaftouh, A Zellou, A Idri. Social recommendation: a user profile clustering-based approach. Concurrency and Computation Practice and Experience, 2019, 31(20): e5330 https://doi.org/10.1002/cpe.5330
17
X N He, H W Zhang, M Y Kan. Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2016, 549–558 https://doi.org/10.1145/2911451.2911489
18
M Jamali, M Ester. A transitivity aware matrix factorization model for recommendation in social networks. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 2644–2649
19
H F Liu, L P Jing, J Yu. Survey of matrix factorization based recommendation methods by integrating social information. Journal of Software, 2018, 29(2): 340–362
20
B Yang, Y Lei, D Y Liu, J M Liu. Social collaborative filtering by trust. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 2747–2753
21
M Jamali, M Ester. A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 2010 ACM Conference on Recommender Systems. 2010, 26–30 https://doi.org/10.1145/1864708.1864736
22
Z Zhang, H Liu. Social recommendation model combining trust propagation and sequential behaviors. Applied Intelligence, 2015, 43(3): 695–706 https://doi.org/10.1007/s10489-015-0681-y
23
C Park, D Kim, J Oh, H Yu. Improving top-K recommendation with truster and trustee relationship in user trust network. Information Sciences, 2016, 374: 100–114 https://doi.org/10.1016/j.ins.2016.09.024
24
H Ma, I King, M Lyu. Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009, 203–210 https://doi.org/10.1145/1571941.1571978
25
J L Tang, X Hu, H J Gao, H Liu. Exploiting local and global social context for recommendation. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 2712–2718
26
J L Tang, S H Wang, X Hu, D W Yin, Y Z Bi, Y Chang, H Liu. Recommendation with social dimensions. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016, 251–257
27
S Deng, L Huang, G Xu, X Wu, Z Wu. On deep learning for trust-aware recommendations in social networks. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(5): 1147–1159 https://doi.org/10.1109/TNNLS.2016.2514368
28
Y Pan, F He, H Yu. Trust-aware collaborative denoising auto-encoder for top-N recommendation. 2017, arXiv preprint arXiv: 1703.01760
29
WQ Fan, Y Ma, DW Yin, J P Wang, J L Tang, Q Li. Deep social collaborative filtering. In: Proceedings of the 13th ACM Conference on Recommender Systems. 2019, 305–313 https://doi.org/10.1145/3298689.3347011
30
S Sedhain, A K Menon, S Sanner, L X Xie. AutoRec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web. 2015, 111–112 https://doi.org/10.1145/2740908.2742726
31
S Li, J Kawale, Y Fu. Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015, 811–820 https://doi.org/10.1145/2806416.2806527
32
Y Wu, C DuBois, A X Zheng, M Ester. Collaborative denoising autoencoders for top-N recommender systems. In: Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 2016, 153–162 https://doi.org/10.1145/2835776.2835837
33
M Gao, L H Chen, X N He, A Y Zhou. BiNE: bipartite network embedding. In: Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. 2018, 715–724 https://doi.org/10.1145/3209978.3209987
34
B Wu, Z Z Lou, Y D Ye. Co-regularized matrix factorization recommendation algorithm. Journal of Software, 2018, 29(9): 2681–2696
35
Q Liu, B Xiang, N J Yuan, E H Chen, H Xiong, Y Zheng, Y Yang. An influence propagation view of pagerank. ACM Transactions on Knowledge Discovery from Data, 2017, 11(3): 1–30 https://doi.org/10.1145/3046941
36
Y Yang, E Chen, Q Liu, B Xiang, T Xu, S A Shad. On approximation of real-world influence spread. In: Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases. 2012, 548–564 https://doi.org/10.1007/978-3-642-33486-3_35
37
Y H Yu, Y Gao, H Wang, S Z Sun. Integrating user social status and matrix factorization for item recommendation. Journal of Computer Research and Development, 2018, 55(1): 113–124
38
R Salakhutdinov, A Mnih. Probabilistic matrix factorization. In: Proceedings of the 22nd Annual Conference on Advances in Neural Information Processing Systems. 2007, 1257–1264
39
G Guo, J Zhang, N Yorke-Smith. A novel Bayesian similarity measure for recommender systems. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2013, 2619–2625
40
X W Meng, S D Liu, Y J Zhang, X Hu. Research on social recommender systems. Journal of Software, 2015, 26(6): 1356–1372
41
H Ma, I King, M R Lyu. Learning to recommend with social trust ensemble. In: Proceedings of the 32nd International ACM SIGIR Conference On Research and Development in Information Retrieval. 2009, 203–210 https://doi.org/10.1145/1571941.1571978
