  Context-aware recommendation systems improve upon classical recommender
systems by including, in the modelling, a user's behaviour. Research into
context-aware recommendation systems has previously only considered the
sequential ordering of items as contextual information. However, there is a
wealth of unexploited additional multi-modal information available in auxiliary
knowledge related to items. This study extends the existing research by
evaluating a multi-modal recommendation system that exploits the inclusion of
comprehensive auxiliary knowledge related to an item. The empirical results
explore extracting vector representations (embeddings) from unstructured and
structured data using data2vec. The fused embeddings are then used to train
several state-of-the-art transformer architectures for sequential user-item
representations. The analysis of the experimental results shows a statistically
significant improvement in prediction accuracy, which confirms the
effectiveness of including auxiliary information in a context-aware
recommendation system. We report a 4% and 11% increase in the NDCG score for
long and short user sequence datasets, respectively.
