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| Content Provider | Springer Nature Link |
|---|---|
| Author | Zhou, Dong Lawless, Séamus Wade, Vincent |
| Copyright Year | 2012 |
| Abstract | Social tagging systems have gained increasing popularity as a method of annotating and categorizing a wide range of different web resources. Web search that utilizes social tagging data suffers from an extreme example of the vocabulary mismatch problem encountered in traditional information retrieval (IR). This is due to the personalized, unrestricted vocabulary that users choose to describe and tag each resource. Previous research has proposed the utilization of query expansion to deal with search in this rather complicated space. However, non-personalized approaches based on relevance feedback and personalized approaches based on co-occurrence statistics only showed limited improvements. This paper proposes a novel query expansion framework based on individual user profiles mined from the annotations and resources the user has marked. The underlying theory is to regularize the smoothness of word associations over a connected graph using a regularizer function on terms extracted from top-ranked documents. The intuition behind the model is the prior assumption of term consistency: the most appropriate expansion terms for a query are likely to be associated with, and influenced by terms extracted from the documents ranked highly for the initial query. The framework also simultaneously incorporates annotations and web documents through a Tag-Topic model in a latent graph. The experimental results suggest that the proposed personalized query expansion method can produce better results than both the classical non-personalized search approach and other personalized query expansion methods. Hence, the proposed approach significantly benefits personalized web search by leveraging users’ social media data. |
| Starting Page | 218 |
| Ending Page | 242 |
| Page Count | 25 |
| File Format | |
| ISSN | 13864564 |
| Journal | Information Retrieval |
| Volume Number | 15 |
| Issue Number | 3-4 |
| e-ISSN | 15737659 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2012-02-24 |
| Publisher Place | Dordrecht |
| Access Restriction | Subscribed |
| Subject Keyword | Personalization Query expansion Web search Social media Tag-Topic model Graph algorithm Document Preparation and Text Processing Pattern Recognition Information Storage and Retrieval Data Structures, Cryptology and Information Theory Data Mining and Knowledge Discovery |
| Content Type | Text |
| Resource Type | Article |
| Subject | Library and Information Sciences Information Systems |
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