Recommending More Suitable Music Based on Users' Real Context

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method.

[1]  Marco Tiemann,et al.  Towards ensemble learning for hybrid music recommendation , 2007, RecSys '07.

[2]  Fei Xiao,et al.  Content-based recommendation for podcast audio-items using natural language processing techniques , 2016, 2016 IEEE International Conference on Big Data (Big Data).

[3]  Yizhou Sun,et al.  LCARS: a location-content-aware recommender system , 2013, KDD.

[4]  Jian Dai,et al.  Personalized route recommendation using big trajectory data , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[5]  Xinhua Wang,et al.  Social Recommendation Algorithm Based on the Context of Time and Tags , 2015, 2015 Third International Conference on Advanced Cloud and Big Data.

[6]  Yulong Gu,et al.  Context Aware Matrix Factorization for Event Recommendation in Event-Based Social Networks , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[7]  Man-Kwan Shan,et al.  A personalized music filtering system based on melody style classification , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[8]  Chao Yang,et al.  HGeoHashBase: an optimized storage model of spatial objects for location-based services , 2018, Frontiers of Computer Science.

[9]  Hong Yang,et al.  Collaborative Social Group Influence for Event Recommendation , 2016, CIKM.

[10]  Liang Liu,et al.  Collaborative Filtering Fusing Label Features Based on SDAE , 2017, ICDM.

[11]  Raymond J. Mooney,et al.  Content-boosted collaborative filtering for improved recommendations , 2002, AAAI/IAAI.

[12]  Hyon Hee Kim,et al.  A Semantically Enhanced Tag-Based Music Recommendation Using Emotion Ontology , 2013, ACIIDS.

[13]  Praveena Mathew,et al.  Book Recommendation System through content based and collaborative filtering method , 2016, 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE).

[14]  Yuh-Min Chen,et al.  Mining Location-Based Service Data for Feature Construction in Retail Store Recommendation , 2017, ICDM.

[15]  Masataka Goto,et al.  Hybrid Collaborative and Content-based Music Recommendation Using Probabilistic Model with Latent User Preferences , 2006, ISMIR.

[16]  Nikos Mamoulis,et al.  Fairness in Package-to-Group Recommendations , 2017, WWW.

[17]  Yang Zhang,et al.  Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding , 2017, Knowl. Based Syst..

[18]  Tao Mei,et al.  Shop-Type Recommendation Leveraging the Data from Social Media and Location-Based Services , 2016, ACM Trans. Knowl. Discov. Data.

[19]  Gianni Fenu,et al.  Influence of Rating Prediction on Group Recommendation's Accuracy , 2016, IEEE Intelligent Systems.

[20]  Enric Plaza,et al.  Case-Based Sequential Ordering of Songs for Playlist Recommendation , 2006, ECCBR.

[21]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[22]  Justin Donaldson A hybrid social-acoustic recommendation system for popular music , 2007, RecSys '07.

[23]  Tim Pohle,et al.  Dynamic Playlist Generation Based on Skipping Behavior , 2005, ISMIR.

[24]  Qing Yang,et al.  Exploiting SDAE Model for Recommendations , 2018, SEKE.

[25]  Kostas Stefanidis,et al.  Fairness in Group Recommendations in the Health Domain , 2017, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[26]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[27]  Yanchun Zhang,et al.  Item Group Recommendation: A Method Based on Game Theory , 2017, WWW.