Name
Abstract | ||
|---|---|---|
User engagement is a fundamental goal for search engines. Recommendations of entities that are related to the useru0027s original search query can increase engagement by raising interest in these entities and thereby extending the useru0027s 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. |
Year | Venue | Field |
|---|---|---|
2015 | IESD@ISWC | Web search query,World Wide Web,Search engine,Semantic search,Information retrieval,Computer science,User engagement,Semantic Web,Search analytics |
DocType | Citations | PageRank |
Conference | 4 | 0.47 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nitish Aggarwal | 1 | 61 | 8.76 |
| Peter Mika | 2 | 2049 | 176.71 |
| Roi Blanco | 3 | 872 | 57.42 |
| Paul Buitelaar | 4 | 994 | 121.79 |