Leveraging collaborative tagging for web item design.

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.