Query result clustering for object-level search

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-specific 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 featurebased 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. ...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Jongwuk Lee, Seung-won Hwang, Zaiqing Nie, Ji-Rong Wen
• Keywords: Data Mining, Diverse Similarity Notions, KDD 2009, Keywords Object-level Search, Subspace Clustering Algorithm