Personalized recommendation of linear content on interactive TV platforms: beating the cold start and noisy implicit user feedback

Recommender systems in TV applications mostly focus on the recommendation of video-on-demand (VOD) content, although the major part of users’ content consumption is realized on linear channel programs (live or recorded), termed EPG programs. The accurate collaborative filtering algorithms suitable for VOD recommendation cannot be directly carried over for EPG program recommendation. First, EPG program recommendation features the cold start problem; a significant part of EPG programs are new in the system. Second, and more importantly, without explicit user feedbacks (ratings) the algorithms have to model user preference based on the noisy and less directly interpretable implicit user feedbacks. In this paper, we present several approaches that overcome these difficulties, by applying pre-filtering on noisy low-level data and taking into account channel preferences of users and program metadata if available to cope with the cold start. Using time-dependent tensor factorization approaches, the temporal preferences of users are also reflected in recommendation, that also hints on the person watching the TV. Experiments were performed on a dataset of SaskTel, a Canadian IPTV service provider using Microsoft Mediaroom middleware.