Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach

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 mappin...

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Vydáno v:	Decision Support Systems Ročník 54; číslo 2; s. 880 - 890
Hlavní autoři:	Li, Xin, Chen, Hsinchun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.01.2013 Elsevier Elsevier Sequoia S.A
Témata:	Algorithms Applied sciences Artificial intelligence Bipartite graph Collaborative filtering Computer science; control theory; systems Computer systems and distributed systems. User interface Decision support systems Enrichment Exact sciences and technology Filtering Filtration Firm modelling Graph representations Graphs Kernel-based methods Kernels Learning and adaptive systems Link prediction Links Mapping Markov processes Mathematics Operational research and scientific management Operational research. Management science Probability and statistics Probability theory and stochastic processes Random walk Recommender systems Sciences and techniques of general use Software Studies Link prediction Bipartite graph Collaborative filtering Kernel-based methods Recommender systems Content access Random walk Graph theory Transaction processing Recommendation Kernel method Graph connectivity User interface Learning (artificial intelligence) Information structure Learning algorithm Artificial intelligence Binary classification Service economy Molecular computation
ISSN:	0167-9236, 1873-5797
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Popis
Shrnutí:	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. ► We propose a kernel-based approach for link prediction and recommendation. ► We design a graph kernel to exploit features in the context of focal user–item pair. ► The kernel works with a one-class SVM algorithm to predict user–item interactions. ► We prove the validity and computational efficiency of the graph kernel. ► Our model outperforms benchmarks, particularly for large amounts of recommendations.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0167-9236 1873-5797
DOI:	10.1016/j.dss.2012.09.019