Incremental Algorithms for Effective and Efficient Query Recommendation

Abstract

Abstract. Query recommender systems give users hints on possible in-teresting queries relative to their information needs. Most query rec-ommenders are based on static knowledge models built on the basis of past user behaviors recorded in query logs. These models should be pe-riodically updated, or rebuilt from scratch, to keep up with the possible variations in the interests of users. We study query recommender algo-rithms that generate suggestions on the basis of models that are updated continuously, each time a new query is submitted. We extend two state-of-the-art query recommendation algorithms and evaluate the effects of continuous model updates on their effectiveness and efficiency. Tests con-ducted on an actual query log show that contrasting model aging by con-tinuously updating the recommendation model is a viable and effective solution.