CRRA: A collaborative approach to re-ranking search results

Web search is not only an individual activity, but also a collaborative activity. By analyzing users' search activities at a community level, effectively refining the ranking of search results has received wide attention in information retrieval in recent years. Previous research has proposed approaches that predefined communities to search collaboratively. However, these approaches usually neglected correlation between users. In this study, we propose a novel collaborative approach (CRRA) for re-ranking search results based on user search activities recorded in query logs. The central idea is to establish correlations among three factors: user, query and document terms, by analyzing user logs. These correlations are then employed to determine community dynamically based on probability theory and collaborative filtering technique and calculate re-ranking score of search results. Evaluation results show that CRRA is more effective than other collaborative ranking approaches.

[1]  Clement T. Yu,et al.  Personalized web search by mapping user queries to categories , 2002, CIKM '02.

[2]  Eric Horvitz,et al.  SearchTogether: an interface for collaborative web search , 2007, UIST.

[3]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[4]  Filip Radlinski,et al.  Improving personalized web search using result diversification , 2006, SIGIR.

[5]  Mohamed S. Kamel,et al.  Efficient phrase-based document indexing for Web document clustering , 2004, IEEE Transactions on Knowledge and Data Engineering.

[6]  Stephen E. Robertson,et al.  Probabilistic relevance ranking for collaborative filtering , 2008, Information Retrieval.

[7]  Sung-Bae Cho,et al.  Personalized mining of web documents using link structures and fuzzy concept networks , 2007, Appl. Soft Comput..

[8]  Abdur Chowdhury,et al.  A picture of search , 2006, InfoScale '06.

[9]  Silviu Cucerzan,et al.  Re-ranking search results using query logs , 2006, CIKM '06.

[10]  Wei-Ying Ma,et al.  Query Expansion by Mining User Logs , 2003, IEEE Trans. Knowl. Data Eng..

[11]  Vasudeva Varma,et al.  A Novel Approach for Re-Ranking of Search Results Using Collaborative Filtering , 2007, 2007 International Conference on Computing: Theory and Applications (ICCTA'07).

[12]  Paolo Ferragina,et al.  A personalized search engine based on Web‐snippet hierarchical clustering , 2008, Softw. Pract. Exp..

[13]  U. Rohini,et al.  A Collaborative Filtering Based Re-ranking Strategy for Search in Digital Libraries , 2005, ICADL.

[14]  Chris H. Q. Ding,et al.  PageRank, HITS and a unified framework for link analysis , 2002, SIGIR '02.

[15]  Natalie S. Glance,et al.  Community search assistant , 2001, IUI '01.

[16]  Boris Chidlovskii,et al.  Collaborative Re-Ranking of Search Results , 2000 .

[17]  Barry Smyth,et al.  A Community-Based Approach to Personalizing Web Search , 2007, Computer.

[18]  Jonathan L. Herlocker,et al.  Click data as implicit relevance feedback in web search , 2007, Inf. Process. Manag..

[19]  Chao Li,et al.  A Query Expansion Algorithm Based on Phrases Semantic Similarity , 2008, 2008 International Symposiums on Information Processing.