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Since a user usually has to issue a sequence of queries and examine multipledocuments to resolve a complex information need in a search session,researchers have paid much attention to evaluating search systems at thesession level rather than the single-query level. Most existing session-levelmetrics evaluate each query separately and then aggregate the query-levelscores using a session-level weighting function. The assumptions behind thesemetrics are that all queries in the session should be involved, and theirorders are fixed. However, if a search system could make the user satisfiedwith her first few queries, she may not need any subsequent queries. Besides,in most real-world search scenarios, due to a lack of explicit feedback fromreal users, we can only leverage some implicit feedback, such as users' clicks,as relevance labels for offline evaluation. Such implicit feedback might bedifferent from the real relevance in a search session as some documents may beomitted in the previous query but identified in the later reformulations. Toaddress the above issues, we make two assumptions about session-basedevaluation, which explicitly describe an ideal session-search system and how toenhance click-through data in computing session-level evaluation metrics. Basedon our assumptions, we design a session-level metric called NormalizedU-Measure (NUM). NUM evaluates a session as a whole and utilizes an idealsession to normalize the result of the actual session. Besides, it inferssession-level relevance labels based on implicit feedback. Experiments on twopublic datasets demonstrate the effectiveness of NUM by comparing it withexisting session-based metrics in terms of correlation with user satisfactionand intuitiveness. We also conduct ablation studies to explore whether theseassumptions hold.

Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation