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Mining Folksonomies for Context-Aware Query Recommendation
| Content Provider | Semantic Scholar |
|---|---|
| Author | Trabelsi, Chiraz |
| Copyright Year | 2015 |
| Abstract | Collaborative tagging systems are social data repositories, in which users manage resources using freely chosen keywords, aka, tags, without any restriction to a certain vocabulary. The resulting informal social classification structure of users, tags and resources constitute the so-called folksonomy. Folksonomies based systems have recently emerged as one of the most popular tools for Web search users to find their desired information. However, the main drawback that can be addressed to these non-hierarchical structures with unsupervised vocabularies stands in low search precision and poor resource navigation and retrieval. Indeed, the widely keyword-based approaches used for locating information on the Web, are not straightforwardly adaptable to folksonomies. This drawback has created the need for an effective framework to support folksonomy users in effectively retrieving resources matching their real search intents. The primary focus of this paper is to propose a new approach for context-aware query recommendation in folksonomies that exploits the power of both Hidden Markov Models (HMMs) and triadic concepts. We demonstrate through carried out experiments that the proposed approach yields mainly highly Precise and highly Relevant tag query recommendation. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.irit.fr/journal-i3/2014/article_14_05.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
