Learning to Rank Query Recommendations by Semantic Similarities
Sumio Fujita, Georges Dupret(Yahoo! Labs), Ricardo Baeza-Yates(Yahoo! Research)
USEWOD2012 - 2nd International Workshop on Usage Analysis and the Web of Data, 2012/4
情報検索 (Information Retrieval) 機械学習 (Machine Learning)
- The web logs of the interactions of people with a search engine show that users often reformulate their queries. Examining these reformulations shows that recommendations that precise the focus of a query are helpful, like those based on expansions of the original queries. But it also shows that queries that express some topical shift with respect to the original query can help user access more rapidly the information they need. We propose a method to identify from search engine query logs possible candidate queries that can be recommended to focus or shift a topic. This method combines various click-based, topic-based and session based ranking strategies and uses supervised learning in order to maximize the semantic similarity between the query and the recommendations, while at the same time we diversify them. We evaluate our method using the query/click logs of a Japanese web search engine and we show that the combination of the three methods proposed is significantly better than any of them taken individually.
Learning to Rank Query Recommendations by Semantic Similarities（PDF）