  Many practical recommender systems provide item recommendation for different
users only via mining user-item interactions but totally ignoring the rich
attribute information of items that users interact with. In this paper, we
propose an attribute-augmented graph neural network model named Murzim. Murzim
takes as input the graphs constructed from the user-item interaction sequences
and corresponding item attribute sequences. By combining the GNNs with node
aggregation and an attention network, Murzim can capture user preference
patterns, generate embeddings for user-item interaction sequences, and then
generate recommendations through next-item prediction. We conduct extensive
experiments on multiple datasets. Experimental results show that Murzim
outperforms several state-of-the-art methods in terms of recall and MRR, which
illustrates that Murzim can make use of item attribute information to produce
better recommendations. At present, Murzim has been deployed in MX Player, one
of India's largest streaming platforms, and is recommending videos for tens of
thousands of users.
