Multiple Models for Recommending Temporal Aspects of Entities

Entity aspect recommendation is an emerging task in semantic search that help users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the only important factor in previous work. However, entity aspects are temporally dynamic and often driven by happening events. For such cases, aspect suggestion based solely on salience features can give unsatisfactory results, for two reasons. First, salience is often accumulated over a long time period and does not account for recency. Second, an aspect that is related to an event entity is often strongly time-dependent. In this paper, we study the task of temporal aspect recommendation for a given entity, which aims at recommending the most relevant aspects and takes into account aforementioned challenges in order to improve search experience. We propose a novel event-centric ensemble ranking method that learns from multiple time and type-dependent models and dynamically trades-off between the salience and recency characteristics of entity aspects. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines