Investigating per Topic Upper Bound for Session Search Evaluation

Session search is a complex Information Retrieval (IR) task. As a result, its evaluation is also complex. A great number of factors need to be considered in the evaluation of session search. They include document relevance, document novelty, aspect-related novelty discounting, and user's efforts in examining the documents. Due to increased complexity, most existing session search evaluation metrics are NP-hard. Consequently, the optimal value, i.e. the upper bound, of a metric highly varies with the actual search topics. In Cranfield-like settings such as the Text REtrieval Conference (TREC), scores for systems are usually averaged across all search topics. With undetermined upper bound values, however, it could be unfair to compare IR systems across different topics. This paper addresses the problem by investigating the actual per topic upper bounds of existing session search metrics. Through decomposing the metrics, we derive the upper bounds via mathematical optimization. We show that after being normalized by the bounds, the NP-hard session search metrics are then able to provide robust comparison across various search topics. The new normalized metrics are experimented on official runs submitted to the TREC 2016 Dynamic Domain (DD) Track.