Neural Network Approaches for Recommender Systems

Abstract

Recommender systems are special algorithms that allow users to receive personalized recommendations on topics that interest them. Systems of this kind are widely used in various fields, for example, in e-commerce, provider services, social networks, etc. Together with classical approaches, neural networks have also become popular in recommender systems in recent years, which are gradually replacing traditional methods of collaborative filtering and content-based algorithms. However, neural networks require large computing resources, which often raises questions on whether an increase in quality will be justified and whether there be one at all. The neural network approach in recommender systems—the self-attentive sequential recommendation (SASRec) transformer model from Microsoft Recommenders—is studied and compared with the classic algorithm, the LightFM hybrid model. For training and validation, the data taken from a housing search application are used. It is proposed to use the hit rate as the main metric for comparison. The results of the experiments will help to understand which algorithms have higher accuracy in terms of predictions and recommendations. As an additional part, the clustering of user and object embeddings is considered.