A Deep Context-Based Factorization Machines for Contextaware Recommender Systems

Abstract

Context-aware recommender systems (CARS) aim to offer personalized recommendations by incorporating user contextual information through analysis. By analyzing these contextual cues, CARS can better understand the preferences and needs of users in different situations, thereby improving the relevance and effectiveness of the recommendations they provide. However, integrating contextual information such as time, Location and into a recommendation system presents challenges due to the potential increase in the sparsity and dimensionality. Recent studies have demonstrated that representing user context as a latent vector can effectively address these kinds of issues. In fact, models such as Factorization Machines (FMs) have been widely used due to their effectiveness and their ability to tackle sparsity and to reduce feature space into a condensed latent space. In this article we introduce a Context-aware recommender model called Deep Context-Based Factorization Machines (DeepCBFM). The DeepCBFM combines the power of deep learning with an extended version of Factorization Machines (FMs) to model nonlinear feature interactions among user, item, and contextual dimensions. Moreover, it addresses certain limitations of FMs, in order to improve the accuracy of recommendations. We implemented our method using two datasets that incorporate contextual information, each having distinct context dimensions. The experimental results indicate that the DeepCBFM model outperforms baseline models and validates its effectiveness.