Learning search popularity for personalized query completion in information retrieval

Abstract

Query completion approaches assist searchers in formulating queries with few keystrokes when using an information retrieval system to address their information needs, which help users benefit from avoiding spelling mistakes and from producing clear query formulations, etc. Previous work on query completion algorithms returns a ranked list of queries to the users mostly based on the overall observed search popularity of query candidates in the whole query logs. However, the query search popularity could be changed over time, i.e., it’s time-aware. Thus, these ranking approaches based on the overall search popularity could not work very well and users may fail to find an acceptable query in the returned list, resulting in a limited search satisfaction. Hence, this paper proposes a Learning-based Personalized Query Ranking approach, i.e., LQR, where the features on the observed and predicted search popularity both in the whole logs and the recent period are exploited. Taking a pair-wise learning scenario, this paper presents a method for generating a ranked list of query candidates, and then reranks the candidates by the similarity to current search context. The experimental results show the proposed approach outperforms the baseline in terms of Mean Reciprocal Rank (MRR), reporting an average MRR improvement of 7% against the baseline.