Learning to Rank for Freshness and Relevance

Freshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user experience, and make the search engine appear stale. While freshness and relevance can be closely related for some topics (e.g.,

news queries), they are more independent in others (e.g., time insensitive queries). Therefore, optimizing one criterion does not necessarily improve the other, and can even do harm in some cases.

We propose a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating different temporal characteristics of queries, and the features

that can be used for capturing these properties. We then introduce our supervised framework that leverages the temporal profile of queries (inferred from pseudo-feedback documents) along with the other ranking features to improve both freshness and relevance

of search results. Our experiments on a large archival web corpus demonstrate the efficacy of our techniques.

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In   Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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