Abstract
With the increase in resource-sharing websites such as YouTube and Flickr, many shared resources have arisen on the Web. Personalized searches have become more important and challenging since users demand higher retrieval quality. To achieve this goal, personalized searches need to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags, which provides a simple but powerful way for organizing, retrieving and sharing different types of social resources. In this article, we examine the limitations of previous tag-based personalized searches. To handle these limitations, we propose a new method to model user profiles and resource profiles in collaborative tagging systems. We use a normalized term frequency to indicate the preference degree of a user on a tag. A novel search method using such profiles of users and resources is proposed to facilitate the desired personalization in resource searches. In our framework, instead of the keyword matching or similarity measurement used in previous works, the relevance measurement between a resource and a user query (termed the query relevance) is treated as a fuzzy satisfaction problem of a user's query requirements. We implement a prototype system called the Folksonomy-based Multimedia Retrieval System (FMRS). Experiments using the FMRS data set and the MovieLens data set show that our proposed method outperforms baseline methods.
Original language | English |
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Pages (from-to) | 98-110 |
Number of pages | 13 |
Journal | Neural Networks |
Volume | 58 |
Early online date | 4 Jun 2014 |
DOIs | |
Publication status | Published - Oct 2014 |
Externally published | Yes |
Funding
The research presented in this paper has been supported by a Strategic Research Grant from City University of Hong Kong (project no. 9667095), National Natural Science Foundation of China (Grant no. 61300137 ), the Guangdong Natural Science Foundation, China (no. S2013010013836 ), the Fundamental Research Funds for the Central Universities, SCUT (no. 2014ZZ0035 ).
Keywords
- Big data
- Folksonomy
- Personalized search
- Social media
- User profile