Abstract
Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intrinsically lack the capability of recommending novel tracks that the target user has not listened to in the past. Collaborative filtering strategies, on the contrary, provide novel recommendations by leveraging past collective behaviors but are often limited in their ability to provide explanations. To narrow this gap, we propose four hybrid algorithms that integrate collaborative filtering with the cognitive architecture ACT-R. We compare their performance in terms of accuracy, novelty, diversity, and popularity bias, to baselines of different types, including pure ACT-R, kNN-based, and neural-networks-based approaches. We show that the proposed algorithms are able to achieve the best performances in terms of novelty and diversity, and simultaneously achieve a higher accuracy of recommendation with respect to pure ACT-R models. Furthermore, we illustrate how the proposed models can provide explainable recommendations.
Original language | English |
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Title of host publication | RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems |
Publisher | Association of Computing Machinery |
Pages | 840-847 |
Number of pages | 8 |
ISBN (Electronic) | 9798400702419 |
DOIs | |
Publication status | Published - 14 Sept 2023 |
Event | 17th ACM Conference on Recommender Systems: RecSys 2023 - Singapore, Singapore Duration: 18 Sept 2023 → 22 Sept 2023 https://recsys.acm.org/recsys23/ |
Conference
Conference | 17th ACM Conference on Recommender Systems |
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Abbreviated title | RecSys'23 |
Country/Territory | Singapore |
City | Singapore |
Period | 18/09/23 → 22/09/23 |
Internet address |
Keywords
- Adaptive Control Thought-Rational (ACT-R)
- Collaborative Filtering
- Explainability
- Music Recommender Systems
- Psychology-Informed Recommender Systems
- Sequential Recommendation
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
- Software
- Control and Systems Engineering