Memory-based Recommendations of Entities for Web Search Users
ACM International Conference on Information and Knowledge Management(2016)
摘要
Modern search engines have evolved from mere document retrieval systems to
platforms that assist the users in discovering new information. In this context,
entity recommendation systems exploit query log data to proactively provide the
users with suggestions of entities (people, movies, places, etc.) from knowledge
bases that are relevant for their current information need. Previous works consider
the problem of ranking facts and entities related to the user's current query, or
focus on specific recommendation domains requiring supervised selection and extraction of features from knowledge bases. In this paper we propose a set of domain-agnostic
methods based on nearest-neighbors collaborative filtering that exploit query log
data to generate entity suggestions, taking into account the user's full search
session. Our experimental results on a large dataset from a commercial search
engine show that the proposed methods are able to compute relevant entity
recommendations outperforming a number of baselines. Finally, we perform an
analysis on a cross-domain scenario using different entity types, and conclude
that even if knowing the right target domain is important for providing
effective recommendations, some inter-domain user interactions are helpful for
the task at hand.
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关键词
recommender systems,web search,entity recommendation,collaborative filtering
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