Post-Ranking Query Suggestion by Diversifying Search Results

  • Yang Song ,
  • Denny Zhou ,
  • Li-wei He

SIGIR '11 Proceedings of the 34st annual international ACM SIGIR conference on Research and development in information retrieval |

Published by Association for Computing Machinery, Inc.

Query suggestion refers to the process of suggesting related queries to search engine users. Most existing researches have focused on improving the relevance of suggested queries. In this paper, we introduce the concept of diversifying the content of the search results from suggested queries while keep the suggestion relevant. Our framework first retrieves a set of query candidates from search engine logs using random walk and other techniques. We then re-rank the suggested queries by ranking them in the order which maximizes the diversification function that measures the difference between the original search results and the results from suggested queries. The diversification function we proposed includes features like ODP category, URL and domain similarity and so on. One important outcome from our research which contradicts with most existing researches is that, with the increase of suggestion relevance, the similarity between the queries actually decreases. Experiments are conducted on a large set of human-labeled data, which is randomly sampled from a commercial search engine’s log. Results indicate that the post-ranking framework significantly improves the relevance of suggested queries by comparing to existing models.