BroadCastRec: A Large-Scale Dataset for TV Content Recommendation

The widespread adoption of smart TVs has made content overload in television programming increasingly severe, and high-quality recommender systems are critical for improving user experience. However, the broadcast-television-recommendation field has long lacked large-scale public datasets that realistically reflect its scenario-specific characteristics, which has constrained the development and validation of recommendation algorithms for broadcast television. To address this gap, this paper constructs and releases BroadCastRec, a large-scale real-world dataset designed specifically for broadcast television recommendation. The dataset is derived from 12 months of IPTV viewing records from a province in China. After data cleaning and anonymization, it contains more than 360,000 users, 44,000 programs, and 113 million user–program interaction records. BroadCastRec provides timestamps, user payment-tier information, and rich program metadata, thereby supporting large-scale program-level analysis of television viewing while leaving complete channel-level EPG reconstruction to a future release. Finally, experiments on benchmark models for click-through rate prediction, sequential recommendation, and multi-task learning show that existing models achieve substantially lower performance on BroadCastRec than on general-domain datasets. These results not only confirm the unique challenges of broadcast television recommendation, but also demonstrate the reliability of BroadCastRec as a new benchmark dataset.