TrustRank

A Cold-Start tolerant recommender system

Haitao Zou, Zhiguo Gong, Nan Zhang, Wei Zhao, Jingzhi Guo

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The explosive growth of the World Wide Web leads to the fast advancing development of e-commerce techniques. Recommender systems, which use personalised information filtering techniques to generate a set of items suitable to a given user, have received considerable attention. User- and item-based algorithms are two popular techniques for the design of recommender systems. These two algorithms are known to have Cold-Start problems, i.e., they are unable to effectively handle Cold-Start users who have an extremely limited number of purchase records. In this paper, we develop TrustRank, a novel recommender system which handles the Cold-Start problem by leveraging the user-trust networks which are commonly available for e-commerce applications. A user-trust network is formed by friendships or trust relationships that users specify among them. While it is straightforward to conjecture that a user-trust network is helpful for improving the accuracy of recommendations, a key challenge for using user-trust network to facilitate Cold-Start users is that these users also tend to have a very limited number of trust relationships. To address this challenge, we propose a pre-processing propagation of the Cold-Start users’ trust network. In particular, by applying the personalised PageRank algorithm, we expand the friends of a given user to include others with similar purchase records to his/her original friends. To make this propagation algorithm scalable to a large amount of users, as required by real-world recommender systems, we devise an iterative computation algorithm of the original personalised TrustRank which can incrementally compute trust vectors for Cold-Start users. We conduct extensive experiments to demonstrate the consistently improvement provided by our proposed algorithm over the existing recommender algorithms on the accuracy of recommendations for Cold-Start users.

Original languageEnglish (US)
Pages (from-to)117-138
Number of pages22
JournalEnterprise Information Systems
Volume9
Issue number2
DOIs
StatePublished - Jan 1 2015

Fingerprint

Recommender systems
Information filtering
World Wide Web
Processing

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems and Management

Cite this

Zou, Haitao ; Gong, Zhiguo ; Zhang, Nan ; Zhao, Wei ; Guo, Jingzhi. / TrustRank : A Cold-Start tolerant recommender system. In: Enterprise Information Systems. 2015 ; Vol. 9, No. 2. pp. 117-138.
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Zou, H, Gong, Z, Zhang, N, Zhao, W & Guo, J 2015, 'TrustRank: A Cold-Start tolerant recommender system', Enterprise Information Systems, vol. 9, no. 2, pp. 117-138. https://doi.org/10.1080/17517575.2013.804587

TrustRank : A Cold-Start tolerant recommender system. / Zou, Haitao; Gong, Zhiguo; Zhang, Nan; Zhao, Wei; Guo, Jingzhi.

In: Enterprise Information Systems, Vol. 9, No. 2, 01.01.2015, p. 117-138.

Research output: Contribution to journalArticle

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