Music recommendation system for audio streaming and media service, uses various strategies to implement recommendation engine for suggesting songs to users, where system uses hybrid approaches that combine collaborative filtering and content based filtering together to leverage strengths of approach
	ETHIRAJULU V; VARUN D; SHIFNA S F N; TEJASWINI S; SARANYA K; SHAMLI R
	2023-10-16
专利权人	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
申请日期	2023-10-16
专利号	IN202341069610-A
成果简介	NOVELTY - The system uses various strategies to implement a recommendation engine for suggesting songs to the users. The system uses hybrid approaches that combine the collaborative filtering and content based filtering together to leverage the strengths and weaknesses of each approach. The system provides a recommendation network that introduces properties to the recommendation strategies, where playlist generation can be deemed as a variation of top recommendations, satisfying the needs specified by users. USE - Music recommendation system for Spotify(RTM: Audio streaming and media service), Netflix(RTM: subscription video on-demand over-the-top streaming service), and Amazon prime(RTM: subscription video on-demand over-the-top streaming TV service). ADVANTAGE - The system provides a user-friendly catalog version of music. The system detects the genre of music and classifies the music efficiently. The system provides music that is liberating, expressive, and spawn different sensations for different varieties of music, and provides different sensations to the user.
IPC 分类号	G06F-016/635 ; G06F-016/638 ; G06F-016/68 ; G06Q-030/06 ; H04N-021/466
国家	印度
专业领域	信息技术
语种	英语
成果类型	专利
文献类型	科技成果
条目标识符	http://119.78.100.226:8889/handle/3KE4DYBR/19889
专题	中国科学院新疆生态与地理研究所
作者单位	BHARATH HIGHER EDUCATION & RES INST (BHAR-Non-standard)
推荐引用方式 GB/T 7714	ETHIRAJULU V,VARUN D,SHIFNA S F N,et al. Music recommendation system for audio streaming and media service, uses various strategies to implement recommendation engine for suggesting songs to users, where system uses hybrid approaches that combine collaborative filtering and content based filtering together to leverage strengths of approach. IN202341069610-A[P]. 2023.

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