Memory-based recommendations of entities for web search users

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.