Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Mining anchor text for query refinement
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Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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Query recommendation is an invaluable tool for enabling users to speed up their searches. In this paper, we present algorithms for generating query suggestions, assuming no previous knowledge of the collection. We developed an online OLAP algorithm to generate query suggestions for the users based on the frequency of the keywords in the selected documents and the correlation between the keywords in the collection. In addition, performance and scalability experiments of these algorithms are presented as proof of their feasibility. We also present sampling as an additional approach for improving performance by using approximate results. We show valid recommendations as a result of combinations generated using the correlations between the keywords. The online OLAP algorithm is also compared with the well-known Apriori algorithm and found to be faster only when simple computations were performed in smaller collections with a few keywords. On the other hand, OLAP showed a more stable behavior between collections, and allows us to have more complex policies during the aggregation and term combinations. Additionally, sampling showed improvement in the time without a significant change on the suggested queries, and proved to be an accurate alternative with a few small samples.