Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF) model as a unified solution for both problems. AMP-CF breaks down the user to several latent 'personas' (profiles) that identify and discern the different tastes and inclinations of the user. Then, the revealed personas are used to generate and explain the final recommendation list for the user. AMP-CF models users as an attentive mixture of personas, enabling a dynamic user representation that changes based on the item under consideration. We demonstrate AMP-CF on five collaborative filtering datasets from the domains of movies, music, video games and social networks. As an additional contribution, we propose a novel evaluation scheme for comparing the different items in a recommendation list based on the distance from the underlying distribution of "tastes" in the user's historical items. Experimental results show that AMP-CF is competitive with other state-of-the-art models. Finally, we provide qualitative results to showcase the ability of AMP-CF to explain its recommendations. CCS Concepts: • Computing methodologies → Learning latent representations; • Information systems → Collaborative filtering.

Elevated serum lactic dehydrogenase (LDH) levels, 595 to 615 μm/ml (normal < 225 μm/ml) with predominance of LDH isoenzymes 2 and 3 was the early and only sign of occult malignant lymphoma in three patients. In the first patient, overt lymphoma appeared clinically only 2 months after the finding of elevated serum LDH levels, whereas in the other two asymptomatic patients, pathologic LDH levels were the only clues to the need for further diagnostic investigation. It is concluded that LDH may have a diagnostic value in the preclinical stage of malignant lymphoma. Thus, a patient with no apparent cause for elevated serum LDH levels warrants a thorough work‐up including abdominal CT scan and even explorative laparotomy.

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