Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Information Retrieval - 35th European Conference on IR Research, ECIR 2013, Proceedings |
| Pages | 642-655 |
| Number of pages | 14 |
| Publication status | Published - 2 Apr 2013 |
| Event | 35th European Conference on Information Retrieval, ECIR 2013 - Moscow, Russian Federation Duration: 24 Mar 2013 → 27 Mar 2013 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7814 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Query recommendation is an integral part of modern search engines that helps users find their information needs. Traditional query recommendation methods usually focus on recommending users relevant queries, which attempt to find alternative queries with close search intent to the original query. Whereas the ultimate goal of query recommendation is to assist users to accomplish their search task successfully, while not just find relevant queries in spite of they can sometimes return useful search results. To better achieve the ultimate goal of query recommendation, a more reasonable way is to recommend users high utility queries, i.e., queries that can return more useful information. In this paper, we propose a novel utility query recommendation approach based on absorbing random walk on the session-flow graph, which can learn queries' utility by simultaneously modeling both users' reformulation behaviors and click behaviors. Extensively experiments were conducted on real query logs, and the results show that our method significantly outperforms the state-of-the-art methods under the evaluation metric QRR and MRD.
Keywords
- Absorbing Random Walk, Query Recommendation, Session-Flow Graph
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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Advances in Information Retrieval - 35th European Conference on IR Research, ECIR 2013, Proceedings. 2013. p. 642-655 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7814 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Recommending High Utility Query via Session-Flow Graph
AU - Zhu, Xiaofei
AU - Guo, Jiafeng
AU - Cheng, Xueqi
AU - Lan, Yanyan
AU - Nejdl, Wolfgang
PY - 2013/4/2
Y1 - 2013/4/2
N2 - Query recommendation is an integral part of modern search engines that helps users find their information needs. Traditional query recommendation methods usually focus on recommending users relevant queries, which attempt to find alternative queries with close search intent to the original query. Whereas the ultimate goal of query recommendation is to assist users to accomplish their search task successfully, while not just find relevant queries in spite of they can sometimes return useful search results. To better achieve the ultimate goal of query recommendation, a more reasonable way is to recommend users high utility queries, i.e., queries that can return more useful information. In this paper, we propose a novel utility query recommendation approach based on absorbing random walk on the session-flow graph, which can learn queries' utility by simultaneously modeling both users' reformulation behaviors and click behaviors. Extensively experiments were conducted on real query logs, and the results show that our method significantly outperforms the state-of-the-art methods under the evaluation metric QRR and MRD.
AB - Query recommendation is an integral part of modern search engines that helps users find their information needs. Traditional query recommendation methods usually focus on recommending users relevant queries, which attempt to find alternative queries with close search intent to the original query. Whereas the ultimate goal of query recommendation is to assist users to accomplish their search task successfully, while not just find relevant queries in spite of they can sometimes return useful search results. To better achieve the ultimate goal of query recommendation, a more reasonable way is to recommend users high utility queries, i.e., queries that can return more useful information. In this paper, we propose a novel utility query recommendation approach based on absorbing random walk on the session-flow graph, which can learn queries' utility by simultaneously modeling both users' reformulation behaviors and click behaviors. Extensively experiments were conducted on real query logs, and the results show that our method significantly outperforms the state-of-the-art methods under the evaluation metric QRR and MRD.
KW - Absorbing Random Walk
KW - Query Recommendation
KW - Session-Flow Graph
UR - http://www.scopus.com/inward/record.url?scp=84875414925&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-36973-5_54
DO - 10.1007/978-3-642-36973-5_54
M3 - Conference contribution
AN - SCOPUS:84875414925
SN - 9783642369728
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 642
EP - 655
BT - Advances in Information Retrieval - 35th European Conference on IR Research, ECIR 2013, Proceedings
T2 - 35th European Conference on Information Retrieval, ECIR 2013
Y2 - 24 March 2013 through 27 March 2013
ER -