Learning Fuzzy User Models for News Recommender Systems

2018 ◽  
pp. 502-515
Author(s):  
Mauro Dragoni
Keyword(s):  
Recommender Systems ◽  
User Models

Mediation of user models for enhanced personalization in recommender systems

2007 ◽  
Vol 18 (3) ◽  
pp. 245-286 ◽  
Author(s):  
Shlomo Berkovsky ◽  
Tsvi Kuflik ◽  
Francesco Ricci
Keyword(s):  
Recommender Systems ◽  
User Models

An approach to controlling user models and personalization effects in recommender systems

2013 ◽  
Author(s):  
Fedor Bakalov ◽  
Marie-Jean Meurs ◽  
Birgitta König-Ries ◽  
Bahar Sateli ◽  
René Witte ◽  
...  
Keyword(s):  
Recommender Systems ◽  
User Models

scholarly journals Support the underground: characteristics of beyond-mainstream music listeners

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Dominik Kowald ◽  
Peter Muellner ◽  
Eva Zangerle ◽  
Christine Bauer ◽  
Markus Schedl ◽  
...  
Keyword(s):  
Popular Music ◽  
Recommender Systems ◽  
Demographic Characteristics ◽  
User Models ◽  
Recommendation Algorithms ◽  
Streaming Services ◽  
Music Recommender Systems ◽  
Recommendation Accuracy ◽  
Music Streaming

AbstractMusic recommender systems have become an integral part of music streaming services such as Spotify and Last.fm to assist users navigating the extensive music collections offered by them. However, while music listeners interested in mainstream music are traditionally served well by music recommender systems, users interested in music beyond the mainstream (i.e., non-popular music) rarely receive relevant recommendations. In this paper, we study the characteristics of beyond-mainstream music and music listeners and analyze to what extent these characteristics impact the quality of music recommendations provided. Therefore, we create a novel dataset consisting of Last.fm listening histories of several thousand beyond-mainstream music listeners, which we enrich with additional metadata describing music tracks and music listeners. Our analysis of this dataset shows four subgroups within the group of beyond-mainstream music listeners that differ not only with respect to their preferred music but also with their demographic characteristics. Furthermore, we evaluate the quality of music recommendations that these subgroups are provided with four different recommendation algorithms where we find significant differences between the groups. Specifically, our results show a positive correlation between a subgroup’s openness towards music listened to by members of other subgroups and recommendation accuracy. We believe that our findings provide valuable insights for developing improved user models and recommendation approaches to better serve beyond-mainstream music listeners.

Study and Comparative Analysis of Existing Recommender Systems

2019 ◽  
Vol 7 (1) ◽  
pp. 262-266
Author(s):  
Sanjay . ◽  
Yogesh Kumar ◽  
Rahul Rishi
Keyword(s):  
Comparative Analysis ◽  
Recommender Systems
Sign in / Sign up

Export Citation Format

Share Document