A Resource Recommendation Method Based on User Taste Diffusion Model in Folksonomies

A Resource Recommendation Method Based on User Taste Diffusion Model in Folksonomies

Jiangning Wu (Dalian University of Technology, China), Yunfei Shi (Dalian University of Technology, China) and Chonghui Guo (Dalian University of Technology, China)
Copyright: © 2012 |Pages: 13
DOI: 10.4018/jkss.2012010102
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Collaborative tagging has been very popular with the development of the Web 2.0, which helps users manage, share and utilize resources effectively. For various kinds of resources, the way to recommend appropriate resources to right users is the key problem in tagging system. This paper proposes a user taste diffusion model based on the tripartite hypergraph to deal with the tri-relation of user-resource-tag in folksonomies and the data sparsity problem in personalized recommendation. Through the defined tri-relation model and diffusion probability matrix, the user’s taste is diffused from itself to other users, resources and tags. When diffusion stops, the candidate resources can be identified then be ranked according to the taste values. As a result the top resources that have not been collected by the given user are selected as the final recommendations. Benefiting from the introduction of iterative diffusion mechanism, the recommendation results not only cover the resources collected by the given user’s direct neighbors but also cover the ones which are collected by his/her extended neighbors. Experimental results show that our method performs better in terms of precision and recall than other recommendation methods.
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1. Introduction

Folksonomies, also known as collaborative tagging systems, allow users to annotate, share and manage their resources with tags. Delicious (http://www.citeulike.org/) are two popular websites based on folksonomies which support users to collect different resources such as URLs or publications according to their preference and specify the chosen resources with some words namely tags. However, with the explosive growth of the Internet, finding the relevant or interesting resources from thousands, even millions of websites is a challenge for users when they involve in folksonomies.

Personalized recommendation technique is proved to be one of effective ways to solve the above problem. The task of a recommendation system is to recommend the resources that satisfy the user’s interest based on the historical records of his/her activities. Here resources could be photos, movies, URLs, publication references, etc. For example, a movie recommendation system recommends the movies according to user’s preference on the ones he/she has watched. Classical recommendation methods, which can be broadly classified into collaborative filtering, content-based (Adomavicius & Tuzhilin, 2005), and diffusion-based (Zhou et al., 2007; Huang et al., 2004), have attracted more attention from researchers for recommending resources in folksonomy systems, in which only binary relationships such as user-resource relationships or user-tag relationships are considered (Yeung et al., 2008; Niwa et al., 2006; Tso-Sutter et al., 2008; Peng et al., 2010; Liang et al., 2008).

In reality, there exist tripartite relationships among users, resources and tags in folksonomies. Making full use of such relationships may improve the recall and precision of recommendation systems (Marinho et al., 2011). Let’s see a scenario: A, B and C are three users in CiteULike. A collects paper p1 and annotates it with two tags “text classification” and “feature selection”. B uses tag “text classification” to annotate paper p2. C collects the same paper as A, but C annotates this paper with two different tags “text mining” and “knowledge management” with A. In this case, users A and B are related to tag “text classification” and users A and C are connected by paper p1. Such case is very popular in folksonomies that different users are linked with the same resources or the same tags. It implies that the information on resources and tags is all useful for identifying the candidate resources when recommending, which inspires us to develop a model to clearly represent the tripartite relationships among users, resources and tags.

Before modeling, one main issue on data sparsity should be considered seriously, which is still a challenge for personalized recommendation. However, diffusion-based recommendation methods have demonstrated that they can effectively solve the data sparsity problem and meanwhile improve the accuracy and the diversification of recommendation systems (Zhou et al., 2007; Huang et al., 2004; Zhang et al., 2007). These kinds of methods suppose that user’s interest or taste can diffuse from itself to others. Zhang et al. (2010) developed a bipartite graph based taste diffusion model and combined two diffusion processes which conducted on user-resource bipartite graph and resource-tag bipartite graph respectively to recommend resources to users, but their research ignored the information between users and tags.

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