Music recommendation systems have become essential for streaming services, helping users discover new songs and re-listen to their favorites. These systems use algorithms that analyze users’ listening patterns and make personalized song recommendations. A key type of algorithm used in these services is sequential recommendation systems, which predict the next song a user will enjoy based on previous listening sessions. Unlike traditional static models, sequential systems focus on users’ dynamic preferences, which evolve and allow users to explore new content while enjoying familiar songs.
A major challenge in these systems is accurately reflecting users’ repetitive listening behaviors. Music consumption often involves listening to the same songs multiple times, but many existing systems need to adequately account for this behavior. Failure to model repeated listening patterns can result in recommendations that miss key aspects of the user’s music experience. This is particularly problematic in music, where users often return to the same tracks, albums, or artists and thus require a system that can effectively predict new and repeated content.
Current methods, such as collaborative filtering and deep learning models like recurrent neural networks, have been widely used to model user preferences. These models effectively capture the dynamic evolution of tastes over time, but overlook the repetitive nature of music listening. While some models attempt to integrate past interactions to inform future recommendations, they often need to provide a robust solution for sequential music recommendations, especially to recognize when users are likely to repeat their listening patterns. These limitations have sparked interest in developing more refined models to handle the complexity of repetitive behavior in music consumption.
Researchers at Deezer have introduced a novel system called PISA (Psychology-Informed Session embedding using ACT-R), specifically designed to improve sequential listening recommendations by incorporating repetitive listening behavior into the predictive model. The system leverages insights from cognitive psychology, specifically the ACT-R (Adaptive Control of Thought-Rational) framework, to simulate how human memory processes information, in particular how users remember and re-listen to songs. By modeling these memory dynamics, PISA aims to deliver more accurate recommendations, balancing the suggestion of new songs with those already enjoyed. The work of the researchers at Deezer provides a practical application of cognitive theory to improve user experiences on a global music streaming platform.
PISA works through a Transformer-based architecture that captures dynamic and repetitive patterns in user behavior. The system creates integrated representations of listening sessions and users, allowing it to effectively model sequences of sessions. It uses attention weights influenced by ACT-R components, including base-level activation, which reflects how recently and how often a song has been listened to, and spreading activation, which captures relationships between songs in the same session. This combination allows PISA to predict which songs users are likely to listen to again while also being able to introduce new content. The ACT-R framework also incorporates partial matching, which helps the system recommend songs with similar characteristics, even if they have not been listened to together before.
PISA’s performance has been validated using two large-scale datasets: one from the public music website Last.fm and one from the proprietary Deezer dataset. In experiments, the system outperformed traditional models on several key metrics. For example, when it comes to NDCG (Normalized Discounted Cumulative Gain), PISA scored 12.16% on Last.fm, demonstrating a superior ability to rank relevant songs higher in the recommendation list than other models. Furthermore, PISA’s recall score, which measures how many of the recommended songs the user has listened to, was significantly better, reaching as high as 12.09% in some cases. These improvements reflect PISA’s ability to accurately model users’ preferences for songs they have heard before and for new songs.
In particular, PISA demonstrated its ability to handle repetitive music listening behaviors. On Deezer, the system achieved a repeat accuracy of 88.27%, which closely matched users’ listening behaviors, which involved frequently playing their favorite songs. The system’s repeat bias, which measures whether the system overemphasizes repeated songs, was significantly lower than other models, indicating that PISA strikes a good balance between recommending repeated and new songs. Furthermore, PISA outperformed models such as RepeatNet and SASRec in exploratory tasks, introducing users to new songs they had not heard before, improving the discovery experience on music platforms.
In conclusion, the PISA system addresses a crucial gap in music recommendation by incorporating cognitive psychology into the design of a sequential recommender. By taking into account both repetitive and evolutionary listening behaviors, it offers a more accurate and user-friendly recommendation experience. Deezer researchers have shown that combining dynamic user models with memory-based repetition models can significantly improve the performance of music recommendation systems. PISA provides more relevant recommendations and helps users discover new music while continuing to enjoy their favorite songs, ensuring a balanced and engaging listening experience.
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Nikhil is a Consultant Intern at Marktechpost. He is pursuing an integrated dual degree in Materials from Indian Institute of technology, Kharagpur. Nikhil is an ai and Machine Learning enthusiast who is always researching applications in fields like Biomaterials and Biomedical Science. With a strong background in Materials Science, he is exploring new advancements and creating opportunities to contribute.
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