Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Context-aware design and interaction in computer systems
IBM Systems Journal
Usage patterns of collaborative tagging systems
Journal of Information Science
AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Determining Attributes to Maximize Visibility of Objects
IEEE Transactions on Knowledge and Data Engineering
A probabilistic model for personalized tag prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
LSA as ground truth for recommending "flickr-aware" representative tags
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Exploratory mining of collaborative social content
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
Generating informative snippet to maximize item visibility
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Rank-mediated collaborative tagging recommendation service using video-tag relationship prediction
Information Systems Frontiers
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The popularity of collaborative tagging sites has created new challenges and opportunities for designers of web items, such as electronics products, travel itineraries, popular blogs, etc. An increasing number of people are turning to online reviews and user-specified tags to choose from among competing items. This creates an opportunity for designers to build items that are likely to attract desirable tags when published. In this paper, we consider a novel optimization problem: given a training dataset of existing items with their user-submitted tags, and a query set of desirable tags, design the k best new items expected to attract the maximum number of desirable tags. We show that this problem is NP-Complete, even if simple Naive Bayes Classifiers are used for tag prediction. We present two principled algorithms for solving this problem: (a) an exact "two-tier" algorithm (based on top-k querying techniques), which performs much better than the naive brute-force algorithm and works well for moderate problem instances, and (b) a novel polynomial-time approximation algorithm with provable error bound for larger problem instances. We conduct detailed experiments on synthetic and real data crawled from the web to evaluate the efficiency and quality of our proposed algorithms.