A number of search user behavior studies show that queries with un- clear intents are commonly submitted to search engines. Result diversification is usually adopted to deal with those queries, in which search engine tries to trade-off some relevancy for some diversity to improve user experience. In this work, we aim to improve the performance of search results diversification by generating an intent subtopics list with fusion of multiple resources. We based our approach by thinking that to collect a large panel of intent subtopics, we should consider as well a wide range of resources from which to extract. The resources adopted cover a large panel of sources, such as external resources (Wikipedia, Google Keywords Generator, Google Insights, Search Engines query suggestion and completion), anchor texts, page snippets and more. We selected resources to cover both information seeker (What a user is searching for) and information provider (The websites) aspects. We also proposed an effi- cient Bayesian optimization approach to maximize resources selection perfor- mances, and a new technique to cluster subtopics based on the top results snip- pet information and Jaccard Similarity coefficient. Experiments based on TREC 2012 web track and NTCIR-10 intent task show that our framework can greatly improve diversity while keeping a good precision. The system developed with the proposed techniques also achieved the best English subtopic mining performance in NTCIR-10 intent task.
['A Damien', 'Min Zhang', 'Yiqun Liu', 'Shaoping Ma']
Improve Web Search Diversification with Intent Subtopic Mining