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Article overview
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Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach | | Diego Sánchez-Moreno
; Yong Zheng
; María N. Moreno-García
; | | Date: |
26 Aug 2020 | | Abstract: | Online streaming services have become the most popular way of listening to
music. The majority of these services are endowed with recommendation
mechanisms that help users to discover songs and artists that may interest them
from the vast amount of music available. However, many are not reliable as they
may not take into account contextual aspects or the ever-evolving user
behavior. Therefore, it is necessary to develop systems that consider these
aspects. In the field of music, time is one of the most important factors
influencing user preferences and managing its effects, and is the motivation
behind the work presented in this paper. Here, the temporal information
regarding when songs are played is examined. The purpose is to model both the
evolution of user preferences in the form of evolving implicit ratings and user
listening behavior. In the collaborative filtering method proposed in this
work, daily listening habits are captured in order to characterize users and
provide them with more reliable recommendations. The results of the validation
prove that this approach outperforms other methods in generating both
context-aware and context-free recommendations | | Source: | arXiv, 2008.11432 | | Services: | Forum | Review | PDF | Favorites | |
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