Predicting Failing Queries in Video Search
The ability to predict when a video search query is not likely to deliver satisfying search results is expected to enable more effective search results optimizations and improved search experience for users. In this paper, we propose a novel context-aware query failure prediction approach that predicts whether a particular query submitted in a user's search session is likely to fail. The approach builds on the well-known concept of query performance prediction introduced in conventional text-based Web search to estimate the query's retrieval performance, but extends this concept with two novel characteristics, user indicators and engine indicators. User indicators are derived from transaction logs, capture the patterns of user interactions with the video search engine, and exploit the context in which a particular query was submitted. Engine indicators are derived from the search results list and measure the consistency of visual search results at the level of visual concepts and textual metadata associated with videos. Extensive evaluation of the approach on a test set containing over one million video search queries shows its effectiveness and demonstrates a significant improvement over traditional and state-of-the-art baseline approaches.