Poster
Optimal Tagging with Markov Chain Optimization
Nir Rosenfeld · Amir Globerson

Tue Dec 6th 06:00 -- 09:30 PM @ Area 5+6+7+8 #164 #None

Many information systems use tags and keywords to describe and annotate content. These allow for efficient organization and categorization of items, as well as facilitate relevant search queries. As such, the selected set of tags for an item can have a considerable effect on the volume of traffic that eventually reaches an item. In tagging systems where tags are exclusively chosen by an item's owner, who in turn is interested in maximizing traffic, a principled approach for assigning tags can prove valuable. In this paper we introduce the problem of optimal tagging, where the task is to choose a subset of tags for a new item such that the probability of browsing users reaching that item is maximized. We formulate the problem by modeling traffic using a Markov chain, and asking how transitions in this chain should be modified to maximize traffic into a certain state of interest. The resulting optimization problem involves maximizing a certain function over subsets, under a cardinality constraint. We show that the optimization problem is NP-hard, but has a (1-1/e)-approximation via a simple greedy algorithm due to monotonicity and submodularity. Furthermore, the structure of the problem allows for an efficient computation of the greedy step. To demonstrate the effectiveness of our method, we perform experiments on three tagging datasets, and show that the greedy algorithm outperforms other baselines.

Author Information

Nir Rosenfeld (Hebrew University of Jerusalem)
Amir Globerson (Tel Aviv University)

Amir Globerson is senior lecturer at the School of Engineering and Computer Science at the Hebrew University. He received a PhD in computational neuroscience from the Hebrew University, and was a Rothschild postdoctoral fellow at MIT. He joined the Hebrew University in 2008. His research interests include graphical models and probabilistic inference, convex optimization, robust learning and natural language processing.

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