MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System

Abstract

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

A TDF-WNSP-WLFM algorithm for product recommendation based on multiple types of implicit user behavior

“…In 2014, Christopher et al proposed a logistic matrix factorization (logistic FM) [ 7 ] method to cope with performing recommendations based on implicit feedback datasets, which is a probabilistic model based on the matrix factorization method. Zhao J et al proposed MFMAP (maximize MAP with matrix factorization), a matrix factorization method focusing on Maximizing MAP [ 8 ]. He et al proposed the state-of-the-art structure of matrix factorization in the format of the neural network [ 9 ].…”