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Please predict whether this patent is acceptable.PATENT ABSTRACT: Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. Previously, item content representation (mostly in textual form) has been used as auxiliary information for learning latent factor representations. Embodiments learn latent factor representation for content items based on modelling an emotional connection between user and content item and based on implicit user feedback regarding the content item.