Traditional recommender systems generate personalized recommendations based on a profile that they create for each user. We argue here that such profiles are often too coarse to capture the current user's state of mind and desire. For example, a serious user that usually prefers documentary features may, at the end of a long and tiring conference, be in the mood for a lighter entertaining movie, not captured by her usual profile. As communicating one's state of mind to a system in (key)words may be difficult, we present in this demo Mood4 - a novel plug-in for recommender systems, which allows users to describe their current desire/mood through examples. Mood4 utilizes the user's examples to refine the recommendations generated by a given recommender system, considering several, possibly competing, desired properties of the recommended items set (rating, diversity, coverage). The system uses a novel algorithm, based on a simple geometric representation of the items, which allows for efficient processing and the generation of suitable recommendations even in the absence of semantic information.

Crowd-based data sourcing is a new and powerful data procurement paradigm that engages Web users to collectively contribute information. In this work, we target the problem of gathering data from the crowd in an economical and principled fashion. We present Ask It!, a system that allows interactive data sourcing applications to effectively determine which questions should be directed to which users for reducing the uncertainty about the collected data. Ask It! uses a set of novel algorithms for minimizing the number of probing (questions) required from the different users. We demonstrate the challenge and our solution in the context of a multiple-choice question game played by the ICDE'12 attendees, targeted to gather information on the conference's publications, authors and colleagues.

This paper considers a popular class of recommender systems that are based on Collaborative Filtering (CF) and proposes a novel technique for diversifying the recommendations that they give to users. Items are clustered based on a unique notion of priority-medoids that provides a natural balance between the need to present highly ranked items vs. highly diverse ones. Our solution estimates items diversity by comparing the rankings that different users gave to the items, thereby enabling diversification even in common scenarios where no semantic information on the items is available. It also provides a natural zoom-in mechanism to focus on items (clusters) of interest and recommending diversified similar items. We present DiRec a plug-in that implements the above concepts and allows CF Recommender systems to diversify their recommendations. We illustrate the operation of DiRec in the context of a movie recommendation system and present a thorough experimental study that demonstrates the effectiveness of our recommendation diversification technique and its superiority over previous solutions.

In this demo we present DiRec, a plug-in that allows Collaborative Filtering (CF) Recommender systems to diversify the recommendations that they present to users. DiRec estimates items diversity by comparing the rankings that different users gave to the items, thereby enabling diversification even in common scenarios where no semantic information on the items is available. Items are clustered based on a novel notion of priority-medoids that provides a natural balance between the need to present highly ranked items vs. highly diverse ones. We demonstrate the operation of DiRec in the context of a movie recommendation system. We show the advantage of recommendation diversification and its feasibility even in the absence of semantic information.

We consider in this paper a popular class of recommender systems that are based on Collaborative Filtering (CF for short). CF is the process of predicting customer ratings to items based on previous ratings of (similar) users to (similar) items, and is typically used by a single organization, using its own customer ratings.

This demonstration presents RMFinder (Related Messages Finder), a system that retains the simplicity and efficiency of topic-based P2P pub-sub, while providing a richer service where users can automatically receive all messages related to those in the topics to which they are subscribed. RMFinder is based on a novel, dynamic, distributed clustering algorithm, that takes advantage of similarities between topic messages to group topics together, into topic-clusters. The clusters adjust automatically to shifts in the focus of the messages published by the topics, as well as to changes in the users interest, and allow for an effective delivery of related messages with minimal overhead.