AANMF: Attribute-Aware Attentional Neural Matrix Factorization

Abstract

Matrix Factorization (MF) is one of the most intuitive and effective methods in the Recommendation System domain. It projects sparse (user, item) interactions into dense feature products which endues strong generality to the MF model. To leverage this interaction, recent works use auxiliary information of users and items. Despite effectiveness, irrationality still exists among these methods, since almost all of them simply add the feature of auxiliary information in dense latent space to the feature of the user or item. In this work, we propose a novel model named AANMF, short for Attribute-aware Attentional Neural Matrix Factorization. AANMF combines two main parts, namely, neural-network-based factorization architecture for modeling inner product and attention-mechanism-based attribute processing cell for attribute handling. Extensive experiments on two real-world data sets demonstrate the robust and stronger performance of our model. Notably, we show that our model can deal with the attributes of user or item more reasonably. Our implementation of AANMF is publicly available at https://github.com/Holy-Shine/AANMF.

Dual attention recommendation algorithm based on item attributes

“…ACAM [27] learned associations between different attributes through a coattention module as a way to improve recommendation performance. AANMF [28] employed the attention mechanism in learning the weights between different attributes,and obtained the user-item matching degree through the neural matrix decomposition network. Considering that the attributes of users and items also have complex interactions with each other, FG-RS [29] adaptively learned the interactions between different attributes through self-attention networks.…”