## 3 Modeling the Time Effect on Music Recommendations
### Time-Aware Music Recommendation Method
#### 3.2.1 Modeling the Evolution of Preferences
In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, USA, 25-30 January 2015; pp. 23-29. * _[PERSON] et al. (2016)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Recommendation for Repeat Consumption from User Implicit Feedback. _IEEE Trans. Knowl. Data Eng._**2016**, _28_, 3083-3097. [CrossRef]
* _[PERSON] and [PERSON] (2020)_ [PERSON]; [PERSON] Personalized recommendation via network-based inference with time. _Phys. A_**2020**, _550_, 123917. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON] Collaborative location recommendations with dynamic time Periods. _Pervasive Mob. Comput._**2017**, _35_, 1-14. [CrossRef]
* _[PERSON] and [PERSON] (2005)_ [PERSON]; [PERSON] Time weight collaborative filtering. In Proceedings of the 14 th ACM International Conference on Information and Knowledge Management, Bremen, Germany, 31 October-5 November 2005; ACM: New York, NY, USA, 2005; pp. 485-492. * _[PERSON] et al. (2010)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Simple time-biased KNN-based recommendations. In Proceedings of the Workshop on Context-Aware Movie Recommendation, Barcelona, Spain, 30 September 2010; pp. 20-23. * _[PERSON] and [PERSON] (2007)_ [PERSON]; [PERSON] Time filtering for better recommendations with small and sparse rating matrices. In _Proc. WISE_; Springer: Berlin/Heidelberg, Germany, 2007; pp. 171-183. * _[PERSON] and [PERSON] (2010)_ [PERSON]; [PERSON] A collaborative filtering algorithm based on time period partition. In Proceedings of the IEEE 2010 Third IITSI, Jinggangshan, China, 2-4 April 2010; pp. 777-780. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modeling Item-Specific Temporal Dynamics of Repeat Consumption for Recommender Systems. In Proceedings of the World Wide Web Conference, San Francisco, CA, USA, 13-17 May 2019; pp. 1977-1987. * _[PERSON] (2010)_ [PERSON] Time-Based Recommendations for Lecture Materials. In Proceedings of the ED-MEDIA, Toronto, ON, Canada, 29 June 2010; pp. 1028-1033. * _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Incorporating temporal dynamics into LDA for one-class collaborative filtering. _Knowl.-Based Syst._**2018**, _150_, 49-56. [CrossRef]* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] TCARS: Time- and Community-Aware Recommendation System. _Future Gener. Comp. Syst._**2018**, _78_, 419-429. [CrossRef]
* _[PERSON] (2009)_ [PERSON] Collaborative Filtering with Temporal Dynamics. In Proceedings of the 15 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, 28 June-1 July 2009; pp. 447-456. * _[PERSON] et al. (2010)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Temporal collaborative filtering with bayesian probabilistic tensor factorization. In Proceedings of the 2010 SIAM International Conference on Data Mining, Columbus, OH, USA, 29 April-1 May 2010; pp. 211-222. * _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Learning recency based comparative choice towards point-of-interest recommendation. _Expert Syst. Appl._**2015**, _42_, 4274-4283. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] Modelling and analysis of temporal preference drifts using a component-based factorised latent approach. _Expert Syst. Appl._**2019**, _116_, 186-208. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]. TimeTrustSVD: A collaborative filtering model integrating time, trust and rating information. _Future Gener. Comp. Syst._**2019**, _93_, 933-941. [CrossRef]
* _[PERSON] et al. (2011)_ [PERSON]; [PERSON]; [PERSON] Yahoo! Music Recommendations: Modeling Music Ratings with Temporal Dynamics and Item Taxonomy. In Proceedings of the Fifth ACM Conference on Recommender Systems, Chicago, IL, USA, 23-27 October 2011; pp. 165-172. * _[PERSON] et al. (2014)_ [PERSON]; [PERSON]; [PERSON] Modeling the Interest-Forgetting Curve for Music Recommendation.