document: 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 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 .
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