In e-commerce sites, customer questions on the product details-page express the customers' information needs about the product. The answers to these questions often provide the necessary information. In this work, we present and address the novel task of generating product insights from community questions and answers (Q&A). These insights can be presented to customers to assist them in their shopping journey. Our method first generates concise, self-contained sentences based on the information in the Q&A. Then insights are selected based on the prominence of their associated questions. Empirical evaluation attests to the effectiveness of our approach in generating well-formed, objective, and helpful insights that are often not available in the product description or in summaries of customer reviews.

This paper focuses on the problem of scoring and ranking influential users of Instagram, a visual content sharing online social network (OSN). Instagram is the second largest OSN in the world with 700 million active Instagram accounts, 32% of all worldwide Internet users. Among the millions of users, photos shared by more influential users are viewed by more users than posts shared by less influential counterparts. This raises the question of how to identify those influential Instagram users. In our work, we present and discuss the lack of relevant tools and insufficient metrics for influence measurement, focusing on a network oblivious approach and show that the graph-based approach used in other OSNs is a poor fit for Instagram. In our study, we consider user statistics, some of which are more intuitive than others, and several regression models to measure users' influence.

We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes. The second algorithm does not modify structure, but only the parameter (thresholds) associated with decision nodes. We also propose to combine both methods by considering an ensemble that contains the union of the two forests. The proposed methods exhibit impressive experimental results over a range of problems.