Identifying "best bet" web search results by mining past user behavior

The top web search result is crucial for user satisfaction with the web search experience. We argue that the importance of the relevance at the top position necessitates special handling of the top web search result for some queries. We propose an effective approach of leveraging millions of past user interactions with a web search engine to automatically detect "best bet" top results preferred by majority of users. Interestingly, this problem can be more effectively addressed with classification than using state-ofthe-art general ranking methods. Furthermore, we show that our general machine learning approach achieves precision comparable to a heavily tuned, domain-specific algorithm, with significantly higher coverage. Our experiments over millions of user interactions for thousands of queries demonstrate the effectiveness and robustness of our techniques. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Search process, Relevance feedback, Infor...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Eugene Agichtein, Zijian Zheng
• Keywords: Data Mining, KDD 2006, Keywords Web Search, Web Search Engine, Web Search Experience