Insights into entity recommendation in web search

User engagement is a fundamental goal for search engines. Recommendations of entities that are related to the user's original search query can increase engagement by raising interest in these entities and thereby extending the user's search session. Related entity recommendations have thus become a standard feature of the interfaces of modern search engines. These systems typically combine a large number of individual signals (features) extracted from the content and interaction logs of a variety of sources. Such studies, however, do not reveal the contribution of individual features, their importance and interaction, or the quality of the sources. In this work, we measure the performance of entity recommendation features individually and by combining them based on a novel dataset of 4.5K search queries and their related entities, which have been evaluated by human assessors.