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

Abstract

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 Single-user recommender systems are no longer valid in this situation, and group recommender systems are needed to recommend music to groups of users, taking into account their individual preferences and the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.

Application of recall methods in recommendation systems

“…Those traditional recall methods are also fitting music recommendation system, for which this section will mainly discuss special recommendation strategies for music. It can be known that people's current mood will affect the choice of music [25]. The system recognizes the user as different emotional states (anger, surprise, sadness, boredom and happiness) according to the user's sitting posture, voice's signal, mouse clicks pattern and other signals while listening to music.…”

“…Recall is a very important part of the recommendation system and there are different recall methods in different recommendation systems. In this regard, various researchers worked on this topic, such as Paul Covington et al [1], M. Deshpande et al [2], Peter Brusilovsky et al [3], Adrián Valera et al [4], Yousefian Jazi et al [5], Farah Tawfiq Abdul Hussien et al [6], Singh Mahesh Kumar et al [7] and Zeqi Ruan et al [8].…”

Customer Profiling Using Internet of Things Based Recommendations

“…A recommender system strives to predict which items would stimulate user interest based on their behavior on a specific web site. In our research, we employ two types of approaches, namely collaborative filtering (CF) and content-based filtering (CBF), both of which are well-known methods for creating a recommender system [27][28][29].…”