Collaborative Recommendations with Deep FeedForward Networks: An Approach to Service Personalization. The aim of this article is to discuss an advanced approach to recommendation systems, based on the adoption of Deep FeedForward Neural Networks. Recommendation engines are datadriven infrastructures designed to help customers in their decisionmaking process, and nowadays represent the xe2x80x9cstate of the artxe2x80x9d in designing smart and personalized services, in accordance with the new customercentric perspective. For this purpose, we followed a quantitative methodological approach, comparing the predictive ability of traditional xe2x80x9cCollaborativexe2x80x9d recommendation algorithms, like the kNearest Neighbors (kNN) and the Singular Value Decomposition (SVD), with FeedForward Neural Networks; given these assumptions, we finally demonstrated that a xe2x80x9cDeepxe2x80x9d Neural architecture could achieve better results in terms of xe2x80x9clossxe2x80x9d generated by the model, laying the foundations for a new, innovative paradigm in service recommendation science.

Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering As the core of recommender system, collaborative filtering (CF) models the affinity between a user and an item from historical useritem interactions, such as clicks, purchases, and so on. Benefited from the strong representation power, neural networks have recently revolutionized the recommendation research, setting up a new standard for CF. However, existing neural recommender models do not explicitly consider the correlations among embedding dimensions, making them less effective in modeling the interaction function between users and items. In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF. We propose a novel and general neural collaborative filtering framework, namely ConvNCF, which is featured with two designs: 1) applying outer product on user embedding and item embedding to explicitly model the pairwise correlations between embedding dimensions, and 2) employing convolutional neural network above the outer product to learn the highorder correlations among embedding dimensions. To justify our proposal, we present three instantiations of ConvNCF by using different inputs to represent a user and conduct experiments on two realworld datasets. Extensive results verify the utility of modeling embedding dimension correlations with ConvNCF, which outperforms several competitive CF methods.