Multi-affect(ed): improving recommendation with similarity-enhanced user reliability and influence propagation

doi:10.1007/s11704-020-9511-4

Front. Comput. Sci.

2021, Vol. 15

Issue (5) : 155331 https://doi.org/10.1007/s11704-020-9511-4

RESEARCH ARTICLE

Multi-affect(ed): improving recommendation with similarity-enhanced user reliability and influence propagation

Lele HUANG¹, Huifang MA^1,²(

), Xiangchun HE¹, Liang CHANG²

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

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Abstract

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.

Keywords recommender systems similarity-enhanced user reliability user-shared feature space influence propagation matrix factorization

Corresponding Author(s): Huifang MA

Just Accepted Date: 13 May 2020 Issue Date: 13 July 2021

Cite this article:

Lele HUANG,Huifang MA,Xiangchun HE, et al. Multi-affect(ed): improving recommendation with similarity-enhanced user reliability and influence propagation[J]. Front. Comput. Sci., 2021, 15(5): 155331.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-020-9511-4
https://academic.hep.com.cn/fcs/EN/Y2021/V15/I5/155331

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