Generating relevant and diverse query suggestions using sparse manifold ranking with sink regions

Authors:
Van Thanh Nguyen;Kim Anh Nguyen
Affiliations:
School of Information and Communication Technology, Hanoi University of Science and Technology, Vietnam;School of Information and Communication Technology, Hanoi University of Science and Technology, Vietnam
Venue:
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2013

Citing 7
Cited 0

The use of MMR, diversity-based reranking for reordering documents and producing summaries

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Agglomerative clustering of a search engine query log

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Context-aware query suggestion by mining click-through and session data

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Query suggestion using hitting time

Proceedings of the 17th ACM conference on Information and knowledge management
Learning latent semantic relations from clickthrough data for query suggestion

Proceedings of the 17th ACM conference on Information and knowledge management
Relative transformation-based neighborhood optimization for isometric embedding

Neurocomputing
A unified framework for recommending diverse and relevant queries

Proceedings of the 20th international conference on World wide web

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to improve the usability of a search engines, Query Suggestion, a technique for generating alternative queries to Web users, has become an indispensable feature for such systems. By measuring the similarity between queries in the Euclidean space, however, most existing works mainly focus on suggesting relevant queries to the original query while ignoring diversity in the suggestions, which will potentially dissatisfy Web users' information needs. In fact, it is more natural and reasonable to assume that the query space is a sparse manifold. In this paper, we present a novel query suggestion method based on sparse query manifold learning and sparse manifold ranking with sink regions. By turning selected queries and their sparse neighbors into sink regions on the sparse query manifold, our approach can extract query suggestions by simultaneously considering both diversity and relevance in a unified way. Empirical experimental results on a large scale query log show that our approach is able to effectively generate highly diverse as well as semantically related suggestions.