How can we e ectively recommend items to a user about whom we have no information? is is the problem we focus on in this paper, known as the cold-start problem. In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information? Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming that a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS. In comparison with three baselines, PdMS improves the performance as measured by the nDCG. ese improvements are demonstrated on real, public datasets.
Continuous Integration (CI) implies that a whole developer team works together on the mainline of a software project. CI systems automate the builds of a software. Sometimes a developer checks in code, which breaks the build. A broken build might not be a problem by itself, but it has the potential to disrupt co-workers, hence it affects the performance of the team. In this study, we investigate the interplay between nonfunctional requirements (NFRs) and builds statuses from 1,283 software projects. We found significant differences among NFRs related-builds statuses. Thus, tools can be proposed to improve CI with focus on new ways to prevent failures into CI, specially for efficiency and usability related builds. Also, the time required to put a broken build back on track indicates a bimodal distribution along all NFRs, with higher peaks within a day and lower peaks in six weeks. Our results suggest that more planned schedule for maintainability for Ruby, and for functionality and reliability for Java would decrease delays related to broken builds.
In recent years, there has been an explosion of social recommender systems (SRS) research. However, the dominant trend of these studies has been towards designing new prediction models. The typical approach is to use social information to build those models for each new user. Due to the inherent complexity of this prediction process, for full cold-start user in particular, the performance of most SRS fall a great deal. We, rather, propose that new users are best served by models already built in system. Selecting a prediction model from a set of strong linked users might offer better results than building a personalized model for full cold-start users. We contribute to this line of work comparing several matrix factorization based SRS under full cold-start user scenario; and proposing a general model selection approach, called ToSocialRec, that leverages existing recommendation models to offer items for new users. Our framework is not only able to handle several social network connection weight metrics, but any metric that can be correlated with preference similarity among users, named here as Preference-like score. We perform experiments on real life datasets that show this technique is as efficient or more than current state-of-the-art techniques for cold-start user. Our framework has also been designed to be easily deployed and leveraged by developers to help create a new wave of SRS.
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