On the reuse of past optimal queries
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th 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
Proceedings of the 6th international conference on Intelligent user interfaces
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Using navigation data to improve IR functions in the context of web search
Proceedings of the tenth international conference on Information and knowledge management
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
On bipartite and multipartite clique problems
Journal of Algorithms
Introduction to Algorithms
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Enhancing Web Search by Aggregating Results of Related Web Queries
WISE '09 Proceedings of the 10th International Conference on Web Information Systems Engineering
A unified framework for recommending diverse and relevant queries
Proceedings of the 20th international conference on World wide web
TOAST: a topic-oriented tag-based recommender system
WISE'11 Proceedings of the 12th international conference on Web information system engineering
More than relevance: high utility query recommendation by mining users' search behaviors
Proceedings of the 21st ACM international conference on Information and knowledge management
Recommending high utility query via session-flow graph
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Concept based query recommendation
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
| Hi-index | 0.00 |
Query recommendation is considered an effective assistant in enhancing keyword based queries in search engines and Web search software. Conventional approach to query recommendation has been focused on query-term based analysis over the user access logs. In this paper, we argue that utilizing the connectivity of a query-URL bipartite graph to recommend relevant queries can significantly improve the accuracy and effectiveness of the conventional query-term based query recommendation systems. We refer to the Query-URL Bipartite based query reCommendation approach as QUBIC. The QUBIC approach has two unique characteristics. First, instead of operating on the original bipartite graph directly using biclique based approach or graph clustering, we extract an affinity graph of queries from the initial query-URL bipartite graph. The affinity graph consists of only queries as its vertices and its edges are weighted according to a query-URL vector based similarity (distance) measure. By utilizing the query affinity graph, we are able to capture the propagation of similarity from query to query by inducing an implicit topical relatedness between queries. We devise a novel rank mechanism for ordering the related queries based on the merging distances of a hierarchical agglomerative clustering. We compare our proposed ranking algorithm with both naïve ranking that uses the query-URL similarity measure directly, and the single-linkage based ranking method. In addition, we make it possible for users to interactively participate in the query recommendation process, to bridge the gap between the determinacy of actual similarity values and the indeterminacy of users' information needs, allowing the lists of related queries to be changed from user to user and query to query, thus personalizing the query recommendation on demand. The experimental results from two query collections demonstrate the effectiveness and feasibility of our approach.