Maurizio Ferrari Dacrema
4 أوراق في مجموعة PaperMetrix
أوراق هذا المؤلف
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Methodological Issues in Recommender Systems Research (Extended Abstract)
2020
The development of continuously improved machine learning algorithms for personalized item ranking lies at the core of today's research in the area of recommender systems. Over the years, the research community has developed widely-agreed best …
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From Sequences to Profiles: Generating Universal Behavioral Profiles exploiting Recurrent Neural Networks
2025
This paper presents the solution developed by the EmbedNBreakfast team for the ACM RecSys Challenge 2025, for the construction of Universal Behavioral Profiles: general-purpose user representations derived from historical interactions. We propose a representation-learning framework …
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Movie genome: alleviating new item cold start in movie recommendation
2019 · User Modeling and User-Adapted Interaction
As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies …
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Are we really making much progress? A worrying analysis of recent neural recommendation approaches
2019
Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine learning in general, it has, as a result, become difficult …