Workshop on information heterogeneity and fusion in recommender systems (HetRec 2010)

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RecSys '10 Proceedings of the fourth ACM conference on Recommender systems , http://dx.doi.org/10.1145/1864708.1864796

A Distributed and Accountable Approach to Offline Recommender Systems Evaluation

Different software tools have been developed with the purpose of performing offline evaluations of recommender systems. However, the results obtained with these tools may be not directly comparable because of subtle differences in the experimental protocols and metrics. Furthermore, it is difficult to analyze in the same experimental conditions several algorithms without disclosing their implementation details. For these reasons, we introduce RecLab, an open source software for evaluating recommender systems in a distributed fashion. By relying on consolidated web protocols, we created RESTful APIs for training and querying recommenders remotely. In this way, it is possible to easily integrate into the same toolkit algorithms realized with different technologies. In details, the experimenter can perform an evaluation by simply visiting a web interface provided by RecLab. The framework will then interact with all the selected recommenders and it will compute and display a comprehensive set of measures, each representing a different metric. The results of all experiments are permanently stored and publicly available in order to support accountability and comparative analyses.Comment: REVEAL 2018 Workshop on Offline Evaluation for Recommender System

Towards Recommender Systems with Community Detection and Quantum Computing

After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems. This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection. Community detection, by partitioning users and items into densely connected clusters, can boost the accuracy of non-personalized recommendation by assuming that users within each community share similar tastes. However, community detection is a computationally expensive process. The recent availability of Quantum Annealers as cloud-based devices, constitutes a new and promising direction to explore community detection, although effectively leveraging this new technology is a long-term path that still requires advancements in both hardware and algorithms. This work aims to begin this path by assessing the quality of community detection formulated as a Quadratic Unconstrained Binary Optimization problem on a real recommendation scenario. Results on several datasets show that the quantum solver is able to detect communities of comparable quality with respect to classical solvers, but with better speedup, and the non-personalized recommendation models built on top of these communities exhibit improved recommendation quality. The takeaway is that quantum computing, although in its early stages of maturity and applicability, shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves

The other side of the social web: A taxonomy for social information access

The power of the modern Web, which is frequently called the Social Web or Web 2.0, is frequently traced to the power of users as contributors of various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide explore what we call social information access techniques that help users get to the right information using "collective wisdom" distilled from actions of those who worked with this information earlier. This invited talk offers a brief introduction into this important research stream and reviews recent works on social information access performed at the University of Pittsburgh's PAWS Lab lead by the author. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM)

A distributed and accountable approach to offline recommender systems evaluation

Different software tools have been developed with the purpose of performing offline evaluations of recommender systems. However, the results obtained with these tools may be not directly comparable because of subtle differences in the experimental protocols and metrics. Furthermore, it is difficult to analyze in the same experimental conditions several algorithms without disclosing their implementation details. For these reasons, we introduce RecLab, an open source software for evaluating recommender systems in a distributed fashion. By relying on consolidated web protocols, we created RESTful APIs for training and querying recommenders remotely. In this way, it is possible to easily integrate into the same toolkit algorithms realized with different technologies. In details, the experimenter can perform an evaluation by simply visiting a web interface provided by RecLab. The framework will then interact with all the selected recommenders and it will compute and display a comprehensive set of measures, each representing a different metric. The results of all experiments are permanently stored and publicly available in order to support accountability and comparative analyses