Fixed-parameter tractability and completeness II: on completeness for W[1]
Theoretical Computer Science
Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Fast generation of result snippets in web search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query biased snippet generation in XML search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Leveraging collaborative tagging for web item design
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the VLDB Endowment
Comprehension-based result snippets
Proceedings of the 21st ACM international conference on Information and knowledge management
Learning to question: leveraging user preferences for shopping advice
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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The widespread use and growing popularity of online collaborative content sites has created rich resources for users to consult in order to make purchasing decisions on various items such as e-commerce products, restaurants, etc. Ideally, a user wants to quickly decide whether an item is desirable, from the list of items returned as a result of her search query. This has created new challenges for producers/manufacturers (e.g., Dell) or retailers (e.g., Amazon, eBay) of such items to compose succinct summarizations of web item descriptions, henceforth referred to as snippets, that are likely to maximize the items' visibility among users. We exploit the availability of user feedback in collaborative content sites in the form of tags to identify the most important item attributes that must be highlighted in an item snippet. We investigate the problem of finding the top-k best snippets for an item that are likely to maximize the probability that the user preference (available in the form of search query) is satisfied. Since a search query returns multiple relevant items, we also study the problem of finding the best diverse set of snippets for the items in order to maximize the probability of a user liking at least one of the top items. We develop an exact top-k algorithm for each of the problem and perform detailed experiments on synthetic and real data crawled from the web to to demonstrate the utility of our problems and effectiveness of our solutions.