Name
Abstract | ||
|---|---|---|
Web query recommendation has long been considered a key feature of search engines. Building a good Web query recommendation system, however, is very difficult due to the fundamental challenge of predicting users' search intent, especially given the limited user context information. In this paper, we propose a novel "sequential query prediction" approach that tries to grasp a user's search intent based on his/her past query sequence and its resemblance to historical query sequence models mined from massive search engine logs. Different query sequence models were examined, including the naive variable length N-gram model, variable memory Markov (VMM) model, and our proposed mixture variable memory Markov (MVMM) model. Extensive experiments were conducted to benchmark our sequence prediction algorithms against two conventional pairwise approaches on large-scale search logs extracted from a commercial search engine. Results show that the sequence-wise approaches significantly outperform the conventional pair-wise ones in terms of prediction accuracy. In particular, our MVMM approach, consistently leads the pack, making it an effective and practical approach towards Web query recommendation. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICDE.2009.71 | ICDE |
Keywords | Field | DocType |
search intent,sequence prediction algorithms,sequential query prediction,large-scale search log,web query recommendation,query recommendation,different query sequence model,commercial search engine,naive variable length n-gram model,massive search engine log,past query sequence,historical query sequence model,large-scale search logs,good web query recommendation,user context information,markov processes,search engines,pairwise approaches,query processing,mixture variable memory markov model,data mining,probabilistic logic,markov model,java,recommender system,context modeling,search engine,hidden markov models,predictive models | Query optimization,Web search query,Data mining,RDF query language,Query language,Query expansion,Information retrieval,Computer science,Sargable,Web query classification,Ranking (information retrieval),Database | Conference |
ISSN | ISBN | Citations |
1084-4627 E-ISBN : 978-0-7695-3545-6 | 978-0-7695-3545-6 | 58 |
PageRank | References | Authors |
1.63 | 33 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi He | 1 | 2326 | 132.92 |
| Daxin Jiang | 2 | 1316 | 72.60 |
| Zhen Liao | 3 | 405 | 13.33 |
| Steven C. H. Hoi | 4 | 3830 | 174.61 |
| Kuiyu Chang | 5 | 917 | 60.50 |
| Ee-Peng Lim | 6 | 5889 | 754.17 |
| Hang Li | 7 | 6294 | 317.05 |