Improving Context-Aware Music Recommender Systems with a Dual Recurrent Neural Network

2021 ◽  
pp. 304-314
Author(s):  
Igor André Pegoraro Santana ◽  
Marcos Aurélio Domingues
Keyword(s):  
Neural Network ◽  
Recommender Systems ◽  
Recurrent Neural Network ◽  
Context Aware ◽  
Music Recommender Systems

scholarly journals User models for multi-context-aware music recommendation

2020 ◽  
Author(s):  
Martin Pichl ◽  
Eva Zangerle
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Consumption ◽  
Current Context ◽  
Music Recommender Systems ◽  
Context Features ◽  
Content Information ◽  
Music Streaming

Abstract In the last decade, music consumption has changed dramatically as humans have increasingly started to use music streaming platforms. While such platforms provide access to millions of songs, the sheer volume of choices available renders it hard for users to find songs they like. Consequently, the task of finding music the user likes is often mitigated by music recommender systems, which aim to provide recommendations that match the user’s current context. Particularly in the field of music recommendation, adapting recommendations to the user’s current context is critical as, throughout the day, users listen to different music in numerous different contexts and situations. Therefore, we propose a multi-context-aware user model and track recommender system that jointly exploit information about the current situation and musical preferences of users. Our proposed system clusters users based on their situational context features and similarly, clusters music tracks based on their content features. By conducting a series of offline experiments, we show that by relying on Factorization Machines for the computation of recommendations, the proposed multi-context-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems.

scholarly journals Predicting Activity and Location with Multi-task Context Aware Recurrent Neural Network

2018 ◽  
Author(s):  
Dongliang Liao ◽  
Weiqing Liu ◽  
Yuan Zhong ◽  
Jing Li ◽  
Guowei Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Location Based Services ◽  
Sequential Dependency ◽  
Context Aware ◽  
Task Context ◽  
Spatial Activity ◽  
Two Factors ◽  
Public Datasets ◽  
Modeling Strategy

Predicting users’ activity and location preferences is of great significance in location based services. Considering that users’ activity and location preferences interplay with each other, many scholars tried to figure out the relation between users’ activities and locations for improving prediction performance. However, most previous works enforce a rigid human-defined modeling strategy to capture these two factors, either activity purpose controlling location preference or spatial region determining activity preference. Unlike existing methods, we introduce spatial-activity topics as the latent factor capturing both users’ activity and location preferences. We propose Multi-task Context Aware Recurrent Neural Network to leverage the spatial activity topic for activity and location prediction. More specifically, a novel Context Aware Recurrent Unit is designed to integrate the sequential dependency and temporal regularity of spatial activity topics. Extensive experimental results based on real-world public datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.

scholarly journals Context-Aware Music Recommender Systems for Groups: A Comparative Study

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 506
Author(s):  
Adrián Valera ◽  
Álvaro Lozano Murciego ◽  
María N. Moreno-García
Keyword(s):  
Comparative Study ◽  
Recommender Systems ◽  
Digital Libraries ◽  
Context Aware ◽  
The Past ◽  
Single User ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Music Streaming ◽  
Group Recommender

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, not just physically, as in the past, but virtually in rooms or messaging groups created for specific purposes, such as studying, training, or meeting friends. Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the context of the group (when listening to music). In this paper, a group recommender system in the music domain is proposed, and an extensive comparative study is conducted, involving different collaborative filtering algorithms and aggregation methods.

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