Using Social Media Background to Improve Cold-start Recommendation Deep Models

TL;DR

This paper explores how incorporating social media background as temporal contextual information can significantly enhance deep learning-based recommender systems, especially in cold-start scenarios, by improving accuracy and relevance.

Contribution

It introduces a novel method to represent social media background as embeddings and fuse them into existing recommendation models, demonstrating improved performance on real-world datasets.

Findings

Recommendation accuracy improved, with hit-rate@K doubling in some cases.

Fusing social media background generally enhances recommendation performance.

Temporal social media data provides valuable context for recommender systems.

Abstract

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.