Entity aspect recommendation is an emerging task in semantic search that helps users discover serendipitous and prominent information with respect to an entity, of which salience (e.g., popularity) is the most important factor in previous work. However, entity aspects are temporally dynamic and often driven by events happening over time. 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, many aspects related to an event entity are 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 time 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 salience and recency characteristics. Through extensive experiments on real-world query logs, we demonstrate that our method is robust and achieves better effectiveness than competitive baselines.

翻译：实体方面推荐是语义搜索中的新兴任务，旨在帮助用户发现与实体相关的意外且突出的信息，其中显著性（如流行度）是先前工作中最重要的因素。然而，实体方面具有时间动态性，且往往由随时间发生的事件驱动。在这种情况下，仅基于显著性特征的方面推荐可能产生令人不满意的结果，原因有二：首先，显著性通常是在长时间段内积累的，未考虑时效性；其次，许多与事件实体相关的方面具有强时间依赖性。本文针对给定实体研究时间方面推荐任务——旨在推荐最相关的方面并考虑时间因素以改善搜索体验。我们提出了一种新颖的事件中心集成排序方法，该方法从多个时间和类型依赖模型中学习，并动态平衡显著性与时效性特征。通过在真实查询日志上的大量实验，我们证明该方法具有鲁棒性，且比竞争基线方法实现了更好的效果。