Paper Info
Title
TruCom: Exploiting Domain-Specific Trust Networks for Multicategory Item Recommendation
Abstract
Recommender systems (RSs) have become important tools for solving the problem of information overload. With the advent and popularity of online social networks, some studies on network-based recommendation have emerged, raising the concern of many researchers. Trust is one kind of important information available in social networks and is often used for performance improvement in social-network-based RSs. However, most trust-aware RSs ignore the fact that people trust different subsets of friends pertaining to different domains, such as music and movies, because people behave differently in diverse domains according to different interests. This paper proposes a novel recommendation method called TruCom. In a multicategory item recommendation domain, TruCom first generates a domain-specific trust network pertaining to each domain and then builds a unified objective function for improving recommendation accuracy by incorporating the hybrid information of direct and indirect trust into a matrix factorization recommendation model. Through relevant benchmark experiments on two real-world data sets, we show that TruCom achieves better performance than other existing recommendation methods, which demonstrates the effectiveness and reliability of TruCom.
Year
DOI
Venue
2017
10.1109/JSYST.2015.2427193
IEEE Systems Journal
Keywords
Field
DocType
Collaborative filtering (CF),item recommendation,matrix factorization (MF),recommender system (RS),trust networks
Data modeling,Information overload,Social network,Computer science,Popularity,Computer network,Artificial intelligence,Recommender system,Information retrieval,Multicategory,RSS,Machine learning,Performance improvement
Journal
Volume
Issue
ISSN
PP
99
1932-8184
Citations 
PageRank 
References 
8
0.46
37
Authors
4
Name
Order
Citations
PageRank
Liu, H.180.46
Feng Xia22013153.69
Chen, Z.3227.11
Nana Yaw Asabere4876.31