Rank-Aware Subspace Clutering for Structured Datasets

In online applications such as Yahoo! Personals and Trulia.com users define structured profiles in order to find potentially interesting matches. Typically, profiles are evaluated against large datasets and produce thousands of matches. In addition to filtering, users also specify ranking in their profile, and matches are returned in the form of a ranked list. Top results in ranked lists are typically homogeneous, which hinders data exploration. For example, a user looking for 1- or 2-bedroom apartments sorted by price will see a large number of cheap 1-bedrooms in undesirable neighborhoods before seeing any apartment with different characteristics. An alternative to ranking is to group matches on common attribute values (e.g., cheap 1-bedrooms in good neighborhoods, 2-bedrooms with 2 baths). However, not all groups will be of interest to the user given the ranking criteria. We argue here that neither single-list ranking nor attribute-based grouping is adequate for effective exploration of ranked datasets. We formalize rank-aware clustering and develop a novel rank-aware bottom-up subspace clustering algorithm. We evaluate the performance of our algorithm over large datasets from a leading online dating site, and present an experimental evaluation of its effectiveness

Enhanced Web Search Engines with Query-Concept Bipartite Graphs

With rapid growth of information on the Web, Web search engines have gained great momentum for exploiting valuable Web resources. Although keywords-based Web search engines provide relevant search results in response to users’ queries, future enhancement is still needed. Three important issues include (1) search results can be diverse because ambiguous keywords in queries can be interpreted to different meanings; (2) indentifying keywords in long queries is difficult for search engines; and (3) generating query-specific Web page summaries is desirable for Web search results’ previews. Based on clickthrough data, this thesis proposes a query-concept bipartite graph for representing queries’ relations, and applies the queries’ relations to applications such as (1) personalized query suggestions, (2) long queries Web searches and (3) query-specific Web page summarization. Experimental results show that query-concept bipartite graphs are useful for performance improvement for the three applications