DeepAPF: Deep Attentive Probabilistic Factorization for Multi-site Video
Recommendation
Abstract
Existing web video systems recommend videos according to users’ viewing history from its own website. However, since many users watch videos in
multiple websites, this approach fails to capture
these users’ interests across sites. In this paper, we
investigate the user viewing behavior in multiple
sites based on a large scale real dataset. We find that
user interests are comprised of cross-site consistent
part and site-specific part with different degrees of
the importance. Existing linear matrix factorization
recommendation model has limitation in modeling
such complicated interactions. Thus, we propose
a model of Deep Attentive Probabilistic Factorization (DeepAPF) to exploit deep learning method to
approximate such complex user-video interaction.
DeepAPF captures both cross-site common interests and site-specific interests with non-uniform
importance weights learned by the attentional network. Extensive experiments show that our proposed model outperforms by 17.62%, 7.9% and
8.1% with the comparison of three state-of-the-art
baselines. Our study provides insight to integrate
user viewing records from multiple sites via the
trusted third party, which gains mutual benefits in
video recommendation