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一项新兴任务，帮助用户发现与实体相关的偶然性和突出信息，其中显著性（如流行度）是先前工作中最重要的因素。然而，实体方面是随时间动态变化的，且通常受事件发生所驱动。对于此类情况，仅基于显著性特征进行方面推荐可能产生不理想的结果，原因有二：首先，显著性通常是在长时间段内累积的，并未考虑时效性；其次，与事件实体相关的许多方面具有强烈的时间依赖性。本文研究针对给定实体的时间方面推荐任务，旨在推荐最相关的方面，并考虑时间因素以提升搜索体验。我们提出了一种新颖的以事件为中心的集成排序方法，该方法从多个时间和类型相关模型中进行学习，并动态权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，且其有效性优于竞争基线方法。