  The widespread use and popularity of collaborative content sites (e.g., IMDB,
Amazon, Yelp, etc.) has created rich resources for users to consult in order to
make purchasing decisions on various products such as movies, e-commerce
products, restaurants, etc. Products with desirable tags (e.g., modern,
reliable, etc.) have higher chances of being selected by prospective customers.
This creates an opportunity for product designers to design better products
that are likely to attract desirable tags when published. In this paper, we
investigate how to mine collaborative tagging data to decide the attribute
values of new products and to return the top-k products that are likely to
attract the maximum number of desirable tags when published. Given a training
set of existing products with their features and user-submitted tags, we first
build a Naive Bayes Classifier for each tag. We show that the problem of is
NP-complete even if simple Naive Bayes Classifiers are used for tag prediction.
We present a suite of 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 set of approximation algorithms for larger problem
instances: a novel polynomial-time approximation algorithm with provable error
bound and a practical hill-climbing heuristic. We conduct detailed experiments
on synthetic and real data crawled from the web to evaluate the efficiency and
quality of our proposed algorithms, as well as show how product designers can
benefit by leveraging collaborative tagging information.
