TY - JOUR
T1 - Recommendation as link prediction in bipartite graphs
T2 - A graph kernel-based machine learning approach
AU - Li, Xin
AU - Chen, Hsinchun
N1 - Funding Information:
This work was supported in part by: the City University of Hong Kong SRG-7002518 , SRG-7002625 , StUp-7200170 , and the United States NSF IIS-0114011 . All opinions in this article are those of the authors and do not necessarily reflect the views of the funding agencies.
PY - 2013/1
Y1 - 2013/1
N2 - Recommender systems have been widely adopted in online applications to suggest products, services, and contents to potential users. Collaborative filtering (CF) is a successful recommendation paradigm that employs transaction information to enrich user and item features for recommendation. By mapping transactions to a bipartite user-item interaction graph, a recommendation problem is converted into a link prediction problem, where the graph structure captures subtle information on relations between users and items. To take advantage of the structure of this graph, we propose a kernel-based recommendation approach and design a novel graph kernel that inspects customers and items (indirectly) related to the focal user-item pair as its context to predict whether there may be a link. In the graph kernel, we generate random walk paths starting from a focal user-item pair and define similarities between user-item pairs based on the random walk paths. We prove the validity of the kernel and apply it in a one-class classification framework for recommendation. We evaluate the proposed approach with three real-world datasets. Our proposed method outperforms state-of-the-art benchmark algorithms, particularly when recommending a large number of items. The experiments show the necessity of capturing user-item graph structure in recommendation.
AB - Recommender systems have been widely adopted in online applications to suggest products, services, and contents to potential users. Collaborative filtering (CF) is a successful recommendation paradigm that employs transaction information to enrich user and item features for recommendation. By mapping transactions to a bipartite user-item interaction graph, a recommendation problem is converted into a link prediction problem, where the graph structure captures subtle information on relations between users and items. To take advantage of the structure of this graph, we propose a kernel-based recommendation approach and design a novel graph kernel that inspects customers and items (indirectly) related to the focal user-item pair as its context to predict whether there may be a link. In the graph kernel, we generate random walk paths starting from a focal user-item pair and define similarities between user-item pairs based on the random walk paths. We prove the validity of the kernel and apply it in a one-class classification framework for recommendation. We evaluate the proposed approach with three real-world datasets. Our proposed method outperforms state-of-the-art benchmark algorithms, particularly when recommending a large number of items. The experiments show the necessity of capturing user-item graph structure in recommendation.
KW - Bipartite graph
KW - Collaborative filtering
KW - Kernel-based methods
KW - Link prediction
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=84871720096&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871720096&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2012.09.019
DO - 10.1016/j.dss.2012.09.019
M3 - Article
AN - SCOPUS:84871720096
SN - 0167-9236
VL - 54
SP - 880
EP - 890
JO - Decision Support Systems
JF - Decision Support Systems
IS - 2
ER -