Name
Abstract | ||
|---|---|---|
Recent research has shown that the performance of search personalization depends on the richness of user profiles which normally represent the user's topical interests. In this paper, we propose a new embedding approach to learning user profiles, where users are embedded on a topical interest space. We then directly utilize the user profiles for search personalization. Experiments on query logs from a major commercial web search engine demonstrate that our embedding approach improves the performance of the search engine and also achieves better search performance than other strong baselines. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-56608-5_54 | ADVANCES IN INFORMATION RETRIEVAL, ECIR 2017 |
DocType | Volume | ISSN |
Conference | 10193 | 0302-9743 |
Citations | PageRank | References |
9 | 0.48 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thanh Vu | 1 | 40 | 6.87 |
| Dat Quoc Nguyen | 2 | 246 | 25.87 |
| Mark Johnson | 3 | 3533 | 331.42 |
| Dawei Song | 4 | 75 | 12.93 |
| alistair willis | 5 | 198 | 13.63 |