References
[1]. N D Almalis, G A Tsihrintzis and N Karagiannis. A content based approach for recommending personnel for job positions [C]. The 5th International Conference on Information, Intelligence, Systems and Applications, 2014:45-49.
[2]. Xue, Feng,He.Deep Item-based Collaborative Filtering for Top-N Recommendation[J].ACM transactions on information systems.2019,37(3).33.1~33.25.doi:10.1145/3314578.
[3]. Ibrahim, Othman, Nilashi.A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques[J]. Expert Systems with Application.2018,92(Feb.).507-520.
[4]. Deger Ayata,Yusuf Yaslan,Mustafa E. Kamasak.Emotion Based Music Recommendation System Using Wearable Physiological Sensors[J].IEEE Transactions on Consumer Electronics.2018,64(2).196-203.
[5]. Cano, Erion, Morisio,.Hybrid recommender systems: A systematic literature review[J].Intelligent data analysis.2017,21(6).1487-1524.
[6]. Yong Wang, Jiangzhou Deng, Jerry Gao,.A hybrid user similarity model for collaborative filtering[J].Information Sciences: An International Journal.2017.418/419102~118.doi:10.1016/j.ins.2017.08.008.
[7]. Sattar Asma, Ghazanfar Mustansar Ali, Iqbal Misbah.Building Accurate and Practical Recommender System Algorithms Using Machine Learning Classifier and Collaborative Filtering[J]. Arabian journal for science & engineering.2017,42(8).3229-3247.doi:10.1007/s13369-016-2410-1.
[8]. Nilashi, Mehrbakhsh, Jannach.Clustering-and regression-based multi-criteria collaborative filtering with incremental updates[J]. Information Sciences: An International Journal.2015.293
[9]. Yue Shi, Martha Larson, Alan Hanjalic.Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges[J].ACM computing surveys.2014,47(1).
[10]. Min-Ling Zhang,Zhi-Hua Zhou.ML-KNN: A lazy learning approach to multi-label learning[J].Pattern Recognition.2007,40(7).2038-2048.doi:10.1016/j.patcog.2006.12.019.
Cite this article
Li,X. (2023). Visualization of data analysis platform — Taking QQ music recommendation system as an example. Applied and Computational Engineering,4,63-69.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. N D Almalis, G A Tsihrintzis and N Karagiannis. A content based approach for recommending personnel for job positions [C]. The 5th International Conference on Information, Intelligence, Systems and Applications, 2014:45-49.
[2]. Xue, Feng,He.Deep Item-based Collaborative Filtering for Top-N Recommendation[J].ACM transactions on information systems.2019,37(3).33.1~33.25.doi:10.1145/3314578.
[3]. Ibrahim, Othman, Nilashi.A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques[J]. Expert Systems with Application.2018,92(Feb.).507-520.
[4]. Deger Ayata,Yusuf Yaslan,Mustafa E. Kamasak.Emotion Based Music Recommendation System Using Wearable Physiological Sensors[J].IEEE Transactions on Consumer Electronics.2018,64(2).196-203.
[5]. Cano, Erion, Morisio,.Hybrid recommender systems: A systematic literature review[J].Intelligent data analysis.2017,21(6).1487-1524.
[6]. Yong Wang, Jiangzhou Deng, Jerry Gao,.A hybrid user similarity model for collaborative filtering[J].Information Sciences: An International Journal.2017.418/419102~118.doi:10.1016/j.ins.2017.08.008.
[7]. Sattar Asma, Ghazanfar Mustansar Ali, Iqbal Misbah.Building Accurate and Practical Recommender System Algorithms Using Machine Learning Classifier and Collaborative Filtering[J]. Arabian journal for science & engineering.2017,42(8).3229-3247.doi:10.1007/s13369-016-2410-1.
[8]. Nilashi, Mehrbakhsh, Jannach.Clustering-and regression-based multi-criteria collaborative filtering with incremental updates[J]. Information Sciences: An International Journal.2015.293
[9]. Yue Shi, Martha Larson, Alan Hanjalic.Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges[J].ACM computing surveys.2014,47(1).
[10]. Min-Ling Zhang,Zhi-Hua Zhou.ML-KNN: A lazy learning approach to multi-label learning[J].Pattern Recognition.2007,40(7).2038-2048.doi:10.1016/j.patcog.2006.12.019.