Identifying comparable entities with indirectly associative relations and word embeddings from web search logs

摘要

Comparable entity identification plays an essential role in the decision making of both consumers and firms in competitive environment. In contrast to traditional cooccurrence approaches, this paper proposes a novel method, namely, ICE (identifying comparable entities) for effectively identifying comparable entities from web search logs, which are online user-generated contents that reflect users' attention and preferences. ICE consists of two stages: the formulation of directly and indirectly associative relations, followed by a generative procedure that is designed for deriving a broad set of candidate entities that are indirectly associative with a specified focal entity; and a deep-learning-based semantic analysis with a word embedding procedure for measuring the similarities between entity profiles so as to target comparable entities from the candidate set. Extensive experiments show that ICE outperforms several baseline methods in the identification of accurate, broad and novel comparable entities with suitable rankings.