INES – National Institute of Science and Technology for Software Engineering

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  • INES at RecSys 2015 – Austria

    Publicado em July 27th, 2015Uncategorized

    Professor Leandro Balby Marinho will attend the 9th ACM Recommender Systems Conference to present the papers: “Context-Aware Event Recommendation in Event-Based Social Networks” and “Are Real-World Place Recommendarion Algorithms Useful in Virtual Worlds?“.

    Context-Aware Event Recommendation in Event-Based Social Networks


    The Web has grown into one of the most important channels to communicate social events nowadays. However, the sheer volume of events available in event-based social networks (EBSNs) often undermines the users’ ability to choose the events that best fit their interests. Recommender systems appear as a natural solution for this problem, but differently from classic recommendation scenarios (e.g. movies, books), the event recommendation problem is intrinsically cold-start. Indeed, events published in EBSNs are typically short-lived and, by definition, are always in the future, having little or no trace of historical attendance. To overcome this limitation, we propose to exploit several contextual signals available from EBSNs. In particular, besides contentbased signals based on the events’ description and collaborative signals derived from users’ RSVPs, we exploit social signals based on group memberships, location signals based on the users’ geographical preferences, and temporal signals derived from the users’ time preferences. Moreover, we combine the proposed signals for learning to rank events for personalized recommendation. Thorough experiments using a large crawl of demonstrate the effectiveness of our proposed contextual learning approach in contrast to state-of-the-art event recommenders from the literature.

    Are Real-World Place Recommendarion Algorithms Useful in Virtual Worlds?


    Bug tracker systems have been used to facilitate maintenance and evolution of software. However, duplicate entries of bug reports in such systems impact on the development productivity. It happens mainly because developers must carefully analyze incoming bug reports to check their validity, which some can take more than 20 minutes to be analyzed. In this sense, this work presents a tool for bug reports search and analysis, in order to improve such tasks.

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