Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Context-aware design and interaction in computer systems
IBM Systems Journal
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
It takes variety to make a world: diversification in recommender systems
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Determining Attributes to Maximize Visibility of Objects
IEEE Transactions on Knowledge and Data Engineering
Speak the same language with your friends: augmenting tag recommenders with social relations
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Connecting users and items with weighted tags for personalized item recommendations
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Topic-based personalized recommendation for collaborative tagging system
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Of categorizers and describers: an evaluation of quantitative measures for tagging motivation
Proceedings of the 21st ACM conference on Hypertext and hypermedia
A probabilistic model for personalized tag prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging collaborative tagging for web item design
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Who tags what?: an analysis framework
Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment
Comprehension-based result snippets
Proceedings of the 21st ACM international conference on Information and knowledge management
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The widespread use and growing popularity of online collaborative content sites (e.g., Yelp, Amazon, IMDB) has created rich resources for users to consult in order to make purchasing decisions on various items such as restaurants, e-commerce products, movies, etc. It has also created new opportunities for producers of such items to improve business by designing better products, composing succinct advertisement snippets and building smart personalized recommendation systems. This motivates us to develop a framework for exploratory mining of user feedback on items in collaborative content sites. Typically, the amount of user feedback associated with item(s) can easily reach hundreds or thousands of ratings, tags or reviews, resulting in an overwhelming amount of information, which users may find difficult to cope with. For example, popular restaurants listed in the review site Yelp routinely receive several thousand ratings and reviews. Moreover, most online activities involve interactions between multiple items and different users, and interpreting such complex user-item interactions becomes intractable too. My PhD research concerns developing novel data mining and exploration algorithms, that account for the above-mentioned challenges, for performing aggregate analytics over available user feedback. Our analysis goal is focused towards helping (a) content consumers make more informed judgment (e.g., if a user will enjoy eating at a particular restaurant), as well as (b) content producers conduct better business (e.g., a re-designed menu to attract more people of a certain demographic group to a restaurant). My dissertation identifies a family of mining tasks, and proposes a suite of algorithms - exact, approximation with theoretical properties, and efficient heuristics - for solving the problems. We conduct a comprehensive set of experiments on the proposed techniques over both synthetic and real data crawled from the web to validate the effectiveness of our framework.