TIM-Rec: Explicit Sparse Feedback on Multi-Item Upselling Recommendations in an Industrial Dataset of Telco Calls

Upselling recommendations play a critical role in improving customer engagement and maximizing revenue in the telecommunications industry. However, real-world data on such interactions often presents unique challenges, including multiple recommendations per call and sparse customer feedback, which complicates the evaluation of recommender systems. Our review of the existing literature reveals a critical gap in publicly available datasets that reflect these challenges, limiting progress in developing and evaluating upselling strategies.This work introduces a novel dataset that captures these complexities, offering valuable insights into customer behavior and recommendation effectiveness. The dataset, derived from real-world interactions between customers and service providers, contains multiple recommendations provided in individual calls and sparse feedback, reflecting typical user behavior where interest may be low or unrecorded.To aid in the development of more effective recommendation systems, we provide detailed statistics on recommendation distributions, user engagement, and feedback patterns. Furthermore, we benchmark various recommendation models, from classical approaches to state-of-the-art neural networks, allowing for a comprehensive assessment of their recommendation accuracy in this challenging setting.