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.
