TY - GEN
T1 - Query result clustering for object-level search
AU - Lee, Jongwuk
AU - Hwang, Seung Won
AU - Nie, Zaiqing
AU - Wen, Ji Rong
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70350686725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350686725&partnerID=8YFLogxK
U2 - 10.1145/1557019.1557149
DO - 10.1145/1557019.1557149
M3 - Conference contribution
AN - SCOPUS:70350686725
SN - 9781605584959
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1205
EP - 1213
BT - KDD '09
T2 - 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
Y2 - 28 June 2009 through 1 July 2009
ER -