  In recommender systems, a cold-start problem occurs when there is no past
interaction record associated with the user or item. Typical solutions to the
cold-start problem make use of contextual information, such as user demographic
attributes or product descriptions. A group of works have shown that social
media background can help predicting temporal phenomenons such as product sales
and stock price movements. In this work, our goal is to investigate whether
social media background can be used as extra contextual information to improve
recommendation models. Based on an existing deep neural network model, we
proposed a method to represent temporal social media background as embeddings
and fuse them as an extra component in the model. We conduct experimental
evaluations on a real-world e-commerce dataset and a Twitter dataset. The
results show that our method of fusing social media background with the
existing model does generally improve recommendation performance. In some cases
the recommendation accuracy measured by hit-rate@K doubles after fusing with
social media background. Our findings can be beneficial for future recommender
system designs that consider complex temporal information representing social
interests.
