Exploring Contextualized Tag-based Embeddings for Neural Collaborative Filtering

Neural collaborative filtering approaches are mainly based on learning user-item interactions. Since in collaborative systems, there are several contents surrounding users and items, essentially user reviews or user tags these personal contents are valuable information that can be leveraged with collaborative filtering approaches. In this context, we address the problem of integrating such content into a neural collaborative filtering model for rating prediction. Such content often represented using the bag of words paradigm is subject to ambiguity. Recent approaches suggest the use of deep neuronal architectures as they attempt to learn semantic and contextual word representations. In this paper, we extended several neural collaborative filtering models for rating prediction that were initially intended to learn user-item interaction by adding textual content. We describe an empirical study that evaluates the impact of using static or contextualized word embeddings with a neural collaborative filtering strategy. The presented models use dense tag-based user and item representations extracted from pre-trained static Word2vec and contextual BERT. The Models were adapted using MLP and Autoencoder architecture and evaluated on several MovieLens datasets. The results showed good improvements when integrating contextual tag embeddings into such neural collaborative filtering architectures.