Recommendations and User-Profiling from Implicit Feedbacks

Recommendations and Feedbacks The vast majority of B2C services are quickly discovering the strategic importance of solid recommendation engines to improve the conversion rates and an establish a stronger fidelity with the customers. The most common strategies are based [3] on the segmentation of users according to their personal features…

A model-free collaborative recommendation system in 20 lines of Python code

Model-free collaborative filtering is a “lightweight” approach to recommendation systems. It’s always based on the implicit “collaboration” (in terms of ratings) among users, but it is computed in-memory without the usage of complex algorithms like ALS (Alternating Least Squares) that can be executed in parallel environment (like Spark). If we assume…

SVD Recommendations using Tensorflow

Recommendation system based on the user-item matrix factorization have become more and more important thanks to powerful and distributable algorithms like ALS, but sometimes the number of users and/or items is not so huge and the computation can be done using directly a SVD (Singular Value Decomposition) algorithm. In this…