Multiple Models for Recommending Temporal Aspects of Entities
Autor: | Nattiya Kanhabua, Tu Ngoc Nguyen, Wolfgang Nejdl |
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Rok vydání: | 2018 |
Předmět: |
Information retrieval
Multiple Models Salience (language) business.industry Computer science 020204 information systems 0202 electrical engineering electronic engineering information engineering Semantic search Multiple time 020201 artificial intelligence & image processing 02 engineering and technology business Popularity |
Zdroj: | The Semantic Web ISBN: 9783319934167 ESWC |
Popis: | 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. |
Databáze: | OpenAIRE |
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