  Nowadays, the recommendation systems are applied in the fields of e-commerce,
video websites, social networking sites, etc., which bring great convenience to
people's daily lives. The types of the information are diversified and abundant
in recommendation systems, therefore the proportion of unstructured multimodal
data like text, image and video is increasing. However, due to the
representation gap between different modalities, it is intractable to
effectively use unstructured multimodal data to improve the efficiency of
recommendation systems. In this paper, we propose an end-to-end Multimodal
Interest-Related Item Similarity model (Multimodal IRIS) to provide
recommendations based on multimodal data source. Specifically, the Multimodal
IRIS model consists of three modules, i.e., multimodal feature learning module,
the Interest-Related Network (IRN) module and item similarity recommendation
module. The multimodal feature learning module adds knowledge sharing unit
among different modalities. Then IRN learn the interest relevance between
target item and different historical items respectively. At last, the
multimodal data feature learning, IRN and item similarity recommendation
modules are unified into an integrated system to achieve performance
enhancements and to accommodate the addition or absence of different modal
data. Extensive experiments on real-world datasets show that, by dealing with
the multimodal data which people may pay more attention to when selecting
items, the proposed Multimodal IRIS significantly improves accuracy and
interpretability on top-N recommendation task over the state-of-the-art
methods.
