| # TIM Rec: Sparse Feedback on Multi-Item Upselling Recommendations in an Industrial Dataset of Telco Calls |
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| **[RECSYS 2025] TIM Rec: Sparse Feedback on Multi-Item Upselling Recommendations in an Industrial Dataset of Telco Calls** |
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| **Authors: Alessandro Sbandi, Federico Siciliano, Fabrizio Silvestri** |
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| **GitHub**: https://github.com/ashba93/tim-rec/tree/main |
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| ## Citation |
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| If you use this code or find it helpful for your research, please cite: |
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| ```` |
| @inproceedings{sbandi2025tim, |
| title={TIM-Rec: Explicit Sparse Feedback on Multi-Item Upselling Recommendations in an Industrial Dataset of Telco Calls}, |
| author={Sbandi, Alessandro and Siciliano, Federico and Silvestri, Fabrizio}, |
| booktitle={Proceedings of the Nineteenth ACM Conference on Recommender Systems}, |
| pages={865--873}, |
| year={2025} |
| } |
| ```` |
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| ## Overview |
| This dataset introduces a real-world **telecommunications upselling dataset** with **multi-item recommendations and sparse feedback**. The dataset captures customer interactions from a real customer care service, where multiple items can be recommended in a single session, and only a small fraction of offers are accepted. Our goal is to provide a resource for evaluating **Learning-to-Rank (LTR)** models and recommendation systems in real-world settings. |
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| ## Key Features |
| - **Real-World Telco Data**: Collected from customer care interactions in a live telecommunications setting. |
| - **Multi-Item Recommendations**: Each session contains multiple recommendations, reflecting real-world upselling scenarios. |
| - **Sparse Feedback**: A low acceptance rate, emphasizing the challenge of learning from limited positive signals. |
| - **Benchmark Baselines**: We provide results from various recommendation models, ranging from classical approaches to deep learning-based solutions. |
| - **Rich Feature Set**: Includes user contextual features, recommendation metadata, and interaction timestamps. |
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| ## Dataset Statistics |
| | Characteristic | Value | |
| |--------------|------| |
| | Total interactions | 1494061 | |
| | Time range | ∼6 months | |
| | Acceptance sparsity (%) | ∼5% | |
| | Avg. recommendations per session | ∼2.4 | |
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| _For more details on the dataset schema and preprocessing, refer to the documentation._ |
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| ## Benchmark Models |
| We evaluate several Learning-to-Rank and recommendation models, including: |
| - **Traditional Models**: Matrix Factorization Algorithm |
| - **Neural Models**: Neural Network based approaches |
| - **Graph-Based Models**: GNN-based recommendation approaches |
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| Results and evaluation metrics such as **Precision@K, NDCG@K, and Recall@K** are included in the paper. |
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| ## Contact |
| For questions or collaborations, please open an issue or contact us at **alessandro.sbandi@gmail.com**. |
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| --- |
| license: cc-by-nc-4.0 |
| size_categories: |
| - 1M<n<10M |
| --- |
