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Data sparsity is a well-recognized issue for Top-N item recommendation, which depends on user preference gathered from their historical behaviors (i.e., implicit feedback). However, only few works have considered multiple types of auxiliary implicit feedback (e.g, click, wanted) when building recommendation models. This paper aims to resolve the data sparsity problem by (a) generating target data (e.g., purchase) from a linear regression of auxiliary feedback, and from the nearest neighbors with a set of purchased items in multiple dimensions; (b) proposing a novel ranking model to accommodate both the original and generated data. We provide an intuitive comprehension regarding the relationship between one kind of auxiliary feedback and target feedback. A series of experiments are conducted on two real-world datasets and demonstrate the superiority of our approach to other counterparts. [ABSTRACT FROM AUTHOR]

Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different clusters simultaneously, we employ a support vector regression model to determine a prediction for a given item, based on user-, item- and prediction-related features. To accommodate (cold) users who cannot be clustered due to insufficient data, we propose a probabilistic method to derive a prediction from the views of both ratings and trust relationships. Experimental results on three real-world data sets demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation, moving clustering-based recommender systems closer towards practical use. [ABSTRACT FROM AUTHOR]