Improving multi-faceted book search by incorporating sparse latent semantic analysis of click-through logs

  • Authors:

  • Deng Yi;Yin Zhang;Haihan Yu;Yanfei Yin;Jing Pan;Baogang Wei

  • Affiliations:

  • Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China;Zhejiang University, Hangzhou, China

  • Venue:
  • Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
  • Year:
  • 2012

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Abstract

Multi-faceted book search engine presents diverse category-style options to allow users to refine search results without re-entering a query. In this paper, we propose a novel multi-faceted book search engine that utilizes users' query-related latent intents mined from click-through logs as multiple facets for books. The latent query intents can be effectively and efficiently discovered by applying the Sparse Latent Semantic Analysis (LSA) model to users' query and clicking behaviors in the click-through logs. This paper presents the details to improve the multi-faceted book search by incorporating the compact representation of query-intent-book relationships generated by Sparse LSA into the off-line and online processing procedures. The specificity of latent query intents can be flexibly changed by adjusting the sparsity level of projection matrix in the Sparse LSA model. We evaluated our approach on CADAL click-through logs containing 45,892 queries and 164,822 books. The experimental results show the Sparse LSA model with more sparse projection matrix tends to discover the more specific latent query intents. The latent query intents suggested by our approach usually gain the high user satisfaction ratio.