Query result clustering for object-level search

Jongwuk Lee, Seung Won Hwang, Zaiqing Nie, Ji Rong Wen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)

Abstract

Query result clustering has recently attracted a lot of attention to provide users with a succinct overview of relevant results. However, little work has been done on organizing the query results for object-level search. Object-level search result clustering is challenging because we need to support diverse similarity notions over object-specfic features (such as the price and weight of a product) of heterogeneous domains. To address this challenge, we propose a hybrid subspace clustering algorithm called Hydra. Algorithm Hydra captures the user perception of diverse similarity notions from millions of Web pages and disambiguates different senses using feature-based subspace locality measures. Our proposed solution, by combining wisdom of crowds and wisdom of data, achieves robustness and efficiency over existing approaches. We extensively evaluate our proposed framework and demonstrate how to enrich user experiences in object-level search using a real-world product search scenarios.

Original languageEnglish
Title of host publicationKDD '09
Subtitle of host publicationProceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages1205-1213
Number of pages9
DOIs
Publication statusPublished - 2009
Event15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris, France
Duration: 2009 Jun 282009 Jul 1

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
CountryFrance
CityParis
Period09/6/2809/7/1

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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    Lee, J., Hwang, S. W., Nie, Z., & Wen, J. R. (2009). Query result clustering for object-level search. In KDD '09: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1205-1213). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/1557019.1557149