Current user interfaces for online video consumption are mostly browser based, lean forward, require constant interaction and provide a fragmented view of the total content available. For easier consumption, the user interface and interactions need to be redesigned for less interruptive and lean back experience. In this paper, we describe Personalized Video, an application that converts the online video experience into a personalized lean back experience. It has been implemented on the Windows platform and integrated with intuitive user interactions like gesture and face recognition. It also supports group personalization for concurrent users.

Many real life datasets have skewed distributions of events when the probability of observing few events far exceeds the others. In this paper, we observed that in skewed datasets the state of the art collaborative filtering methods perform worse than a simple probabilistic model. Our test bench includes a real ad click stream dataset which is naturally skewed. The same conclusion is obtained even from the popular movie rating dataset when we pose a binary prediction problem of whether a user will give maximum rating to a movie or not.

Subscribers to the popular news or blog feeds (RSS/Atom) often face the problem of information overload as these feed sources usually deliver large number of items periodically. One solution to this problem could be clustering similar items in the feed reader to make the information more manageable for a user. Clustering items at the feed reader end is a challenging task as usually only a small part of the actual article is received through the feed. In this paper, we propose a method of improving the accuracy of clustering short texts by enriching their representation with additional features from Wikipedia. Empirical results indicate that this enriched representation of text items can substantially improve the clustering accuracy when compared to the conventional bag of words representation.