Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting knowledge from evaluative text
Proceedings of the 3rd international conference on Knowledge capture
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Utility scoring of product reviews
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
OPINE: extracting product features and opinions from reviews
HLT-Demo '05 Proceedings of HLT/EMNLP on Interactive Demonstrations
Designing novel review ranking systems: predicting the usefulness and impact of reviews
Proceedings of the ninth international conference on Electronic commerce
Opinion integration through semi-supervised topic modeling
Proceedings of the 17th international conference on World Wide Web
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Maximizing a Submodular Set Function Subject to a Matroid Constraint (Extended Abstract)
IPCO '07 Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Modeling and Predicting the Helpfulness of Online Reviews
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Rated aspect summarization of short comments
Proceedings of the 18th international conference on World wide web
How opinions are received by online communities: a case study on amazon.com helpfulness votes
Proceedings of the 18th international conference on World wide web
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Proceedings of the forty-first annual ACM symposium on Theory of computing
Efficient Computation of Diverse Query Results
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Automatically assessing review helpfulness
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Redundancy, diversity and interdependent document relevance
ACM SIGIR Forum
Exploiting social context for review quality prediction
Proceedings of the 19th international conference on World wide web
Diversifying web search results
Proceedings of the 19th international conference on World wide web
Detecting high log-densities: an O(n¼) approximation for densest k-subgraph
Proceedings of the forty-second ACM symposium on Theory of computing
Latent aspect rating analysis on review text data: a rating regression approach
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient confident search in large review corpora
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Toward a fair review-management system
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Estimating entity importance via counting set covers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting a characteristic set of reviews
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Fake reviews: the malicious perspective
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Measuring the coverage and redundancy of information search services on e-commerce platforms
Electronic Commerce Research and Applications
A framework for evaluating the smoothness of data-mining results
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Have you done anything like that?: predicting performance using inter-category reputation
Proceedings of the sixth ACM international conference on Web search and data mining
Graph-based informative-sentence selection for opinion summarization
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Using micro-reviews to select an efficient set of reviews
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Online user reviews play a central role in the decision-making process of users for a variety of tasks, ranging from entertainment and shopping to medical services. As user-generated reviews proliferate, it becomes critical to have a mechanism for helping the users (information consumers) deal with the information overload, and presenting them with a small comprehensive set of reviews that satisfies their information need. This is particularly important for mobile phone users, who need to make decisions quickly, and have a device with limited screen real-estate for displaying the reviews. Previous approaches have addressed the problem by ranking reviews according to their (estimated) helpfulness. However, such approaches do not account for the fact that the top few high-quality reviews may be highly redundant, repeating the same information, or presenting the same positive (or negative) perspective. In this work, we focus on the problem of selecting a comprehensive set of few high-quality reviews that cover many different aspects of the reviewed item. We formulate the problem as a maximum coverage problem, and we present a generic formalism that can model the different variants of review-set selection. We describe algorithms for the different variants we consider, and, whenever possible, we provide approximation guarantees with respect to the optimal solution. We also perform an experimental evaluation on real data in order to understand the value of coverage for users.