A Study about The Impact of Music Recommender Systems on Online Digital Music Rankings

2014 ◽  
Vol 16 (3) ◽  
pp. 49-68
Author(s):  
HyunMo Kim ◽  
◽  
MinYong Kim ◽  
JaeHong Park
Keyword(s):  
Recommender Systems ◽  
Digital Music ◽  
Music Recommender Systems ◽  
The Impact

Second Workshop on the Impact of Recommender Systems at ACM RecSys ’20

2020 ◽  
Author(s):  
Oren Sar Shalom ◽  
Dietmar Jannach ◽  
Joseph A. Konstan
Keyword(s):  
Recommender Systems ◽  
The Impact

scholarly journals Symmetric Metric Learning with Adaptive Margin for Recommendation

2020 ◽  
Vol 34 (04) ◽  
pp. 4634-4641
Author(s):  
Mingming Li ◽  
Shuai Zhang ◽  
Fuqing Zhu ◽  
Wanhui Qian ◽  
Liangjun Zang ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Metric Space ◽  
State Of The Art ◽  
Metric Learning ◽  
Current User ◽  
Experimental Results ◽  
Negative Item ◽  
Positive Item ◽  
User Centric ◽  
The Impact

Metric learning based methods have attracted extensive interests in recommender systems. Current methods take the user-centric way in metric space to ensure the distance between user and negative item to be larger than that between the current user and positive item by a fixed margin. While they ignore the relations among positive item and negative item. As a result, these two items might be positioned closely, leading to incorrect results. Meanwhile, different users usually have different preferences, the fixed margin used in those methods can not be adaptive to various user biases, and thus decreases the performance as well. To address these two problems, a novel Symmetic Metric Learning with adaptive margin (SML) is proposed. In addition to the current user-centric metric, it symmetically introduces a positive item-centric metric which maintains closer distance from positive items to user, and push the negative items away from the positive items at the same time. Moreover, the dynamically adaptive margins are well trained to mitigate the impact of bias. Experimental results on three public recommendation datasets demonstrate that SML produces a competitive performance compared with several state-of-the-art methods.

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

scholarly journals Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems

2015 ◽  
Vol 219 ◽  
pp. 25-39 ◽  
Author(s):  
Ben Horsburgh ◽  
Susan Craw ◽  
Stewart Massie
Keyword(s):  
Recommender Systems ◽  
Music Recommender Systems

Evaluating Hybrid Music Recommender Systems

2013 ◽  
Author(s):  
Thomas Hornung ◽  
Cai-Nicolas Ziegler ◽  
Simon Franz ◽  
Martin Przyjaciel-Zablocki ◽  
Alexander Schatzle ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Music Recommender Systems
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