Personalized and Context-Aware TV Program Recommendations Based on Implicit Feedback

The current explosion of the number of available channels is making the choice of the program to watch an experience more and more difficult for TV viewers. Such a huge amount obliges the users to spend a lot of time in consulting TV guides and reading synopsis, with a heavy risk of even missing what really would have interested them. In this paper we confront this problem by developing a recommender system for TV programs. Recommender systems have been widely studied in the video-on-demand field, but the TV domain poses its own challenges which make the traditional video-on-demand techniques not suitable. In more detail, we propose recommendation algorithms relying exclusively on implicit feedback and leveraging context information. An extensive evaluation on a real TV dataset proves the effectiveness of our approach, and in particular the importance of the context in providing TV program recommendations

Context-Aware Recommendations for Televisions Using Deep Embeddings with Relaxed N-Pairs Loss Objective

This paper studies context-aware recommendations in the television domain by proposing a deep learning-based method for learning joint context-content embeddings (JCCE). The method builds on recent developments within recommendations using latent representations and deep metric learning, in order to effectively represent contextual settings of viewing situations as well as available content in a shared latent space. This embedding space is used for exploring relevant content in various viewing settings by applying an N -pairs loss objective as well as a relaxed variant introduced in this paper. Experiments on two datasets confirm the recommendation ability of JCCE, achieving improvements when compared to state-of-the-art methods. Further experiments display useful structures in the learned embeddings that can be used to gain valuable knowledge of underlying variables in the relationship between contextual settings and content properties