Uncovering the structure of hypergraphs through tensor decomposition: an application to folksonomy analysis

Folksonomies - shared vocabularies generated by users through collective annotation (tagging) of web-based content, which are formally hypergraphs connecting users, tags and objects, are beginning to play an increasingly important role in social media. Effective use of folksonomies for organizing and locating web content, discovering and organizing user communities in order to facilitate the contact and collaboration between users who share parts of their interests and attitudes calls for effective methods for discovering coherent groupings of users, objects, and tags. We empirically compare the results of several folksonomy clustering methods using tensor decompositions such as PARAFAC, Tucker3 and HOSVD which are generalizations of principal component analysis and singular value decomposition with standard methods that use 2-dimensional projections of the original 3-way relationships. Our results suggest that the proposed methods overcome some of the limitations of 2-way decomposition methods in clustering folksonomies

Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions

One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions. An unaddressed problem in previous approaches is the existence of highly non-uniform misprediction costs. While for model evaluation these costs have been taken into account through recently proposed business-aware offline metrics -- such as the Utility metric which measures the impact on advertiser profit -- this is not the case when training the models themselves. In this paper, to bridge the gap, we formally analyze the relationship between optimizing the Utility metric and the log loss, which is considered as one of the state-of-the-art approaches in conversion modeling. Our analysis motivates the idea of weighting the log loss with the business value of the predicted outcome. We present and analyze a new cost weighting scheme and show that significant gains in offline and online performance can be achieved.Comment: First version of the paper was presented at NIPS 2015 Workshop on E-Commerce: https://sites.google.com/site/nips15ecommerce/papers Third version of the paper will be presented at AdKDD 2017 Workshop: adkdd17.wixsite.com/adkddtargetad201