Proceedings of the Fourth ACM Conference on Recommender Systems 2010
DOI: 10.1145/1864708.1864786
|View full text |Cite
|
Sign up to set email alerts
|

Music recommendations with temporal context awareness

Abstract: We present a system capable of recommending music playlists that take into account the temporal context of the user, i.e. they select user preferences as learned for the concrete time situation of the request.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
14
0

Year Published

2012
2012
2018
2018

Publication Types

Select...
5
2
2

Relationship

0
9

Authors

Journals

citations
Cited by 25 publications
(14 citation statements)
references
References 3 publications
0
14
0
Order By: Relevance
“…A summary and categorization of corresponding scientific works can be found in Table 1. The majority of approaches rely solely on one or a few aspects (temporal features in Cebrián et al 2010, listening history and weather conditions in Lee and Lee 2007, for instance), whereas more comprehensive user models are rare in MIR. One of the few exceptions is Cunningham et al's study (Cunningham et al 2008) that investigates if and how various factors relate to music taste (e.g., human movement, emotional status, and external factors such as temperature and lightning conditions).…”
Section: What About the User In Mir?mentioning
confidence: 99%
See 1 more Smart Citation
“…A summary and categorization of corresponding scientific works can be found in Table 1. The majority of approaches rely solely on one or a few aspects (temporal features in Cebrián et al 2010, listening history and weather conditions in Lee and Lee 2007, for instance), whereas more comprehensive user models are rare in MIR. One of the few exceptions is Cunningham et al's study (Cunningham et al 2008) that investigates if and how various factors relate to music taste (e.g., human movement, emotional status, and external factors such as temperature and lightning conditions).…”
Section: What About the User In Mir?mentioning
confidence: 99%
“…For instance, Cebrián et al (2010), Pohle et al (2007), and Nürnberger and Detyniecki (2003) seemingly do not perform any kind of evaluation involving real users, or at least do not report it. Some approaches are evaluated on user-generated data, but do not request feedback from real users during the evaluation experiments.…”
Section: What About the User In Mir?mentioning
confidence: 99%
“…Some approaches consider only temporal features [5] or weather Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.…”
Section: Motivation and Related Workmentioning
confidence: 99%
“…Music tastes vary heavily along with time, an aspect that has been studied in [22] with a special focus on the user situation. The notion of time is captured in our system via the time profile of each track over the year, which we encode as a feature vector for computing our time similarity (cf.…”
Section: Related Workmentioning
confidence: 99%