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Existing recommendation methods mainly learn user preference from historical user-item interaction data, while ignoring the extent of interactions, i.e., diverse user experience and user intention. To be more specific, for new users with little or no experience, they may turn to popular items preferred by majority users. In terms of those medium-level users who already have interacted with some items, they may require and expect items to meet their personal preferences. As pro-active users are likely to leave rich behavior (action) feedback (e.g., view, like) on the items they interacted with, the system will have good chance to better interpret users' intention, and thus generate more accurate and elaborate recommendations to hit their preferences. In this paper, we propose a generic M ulti-facet U ser P reference L earning (MUPL) framework for fine-grained item recommendation. By considering diverse user experience and intention, MUPL captures user preference in the level of group-, individual- and action-facet. Besides, the importance of user preference in different facets can be automatically learnt by MUPL. Extensive experiments on two real-world datasets (Xing, Sobazaar) demonstrate the superiority of our proposed approach over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Personalized item recommendation has become an essential target of Web applications, but it suffers from the efficiency problem due to a large volume of data. In particular, feature-based factorization machine models are generally limited by the vast number of feature dimensions, leading to catastrophic computation time. In this paper, we propose a Fast Discrete Factorization Machine (FDFM) method to resolve these issues by applying the hash coding technologies to factorization machine models. Specifically, it discretizes the real-valued feature vectors in the parameter model during the process of learning personalized item rankings, whereby the overall computational time can be greatly reduced. Besides, we propose convergence update rules to optimize the quantization loss of the binarization problem, which can be used in personalized ranking scenarios efficiently. Based on the evaluation in two real-world datasets, our proposed approach consistently shows better performance than other baselines, especially when using shorter binary codes. [ABSTRACT FROM AUTHOR]