Music recommendation using graph based quality model

Mao, Kuang; Chen, Gang; Hu, Yuxing; Zhang, Luming

doi:10.1016/j.sigpro.2015.03.026

Cited by 21 publications

(5 citation statements)

References 15 publications

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“…In the news domain, information is released and updated continuously, and many stories only remain online for a short period of time until new details emerge. This dynamism is not as relevant in other content domains, such as movies [33], music [34], or books [35], making the news domain quite specific and with special requirements. Dynamic profiles, that take time into consideration, may discriminate short-term and long-term interests [31].…”

Section: User Profilingmentioning

confidence: 99%

Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations

et al. 2020

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This paper presents an experiment on newsreaders’ behavior and preferences on the interaction with online personalized news. Different recommendation approaches, based on consumption profiles and user location, and the impact of personalized news on several aspects of consumer decision-making are examined on a group of volunteers. Results show a significant preference for reading recommended news over other news presented on the screen, regardless of the chosen editorial layout. In addition, the study also provides support for the creation of profiles taking into consideration the evolution of user’s interests. The proposed solution is valid for users with different reading habits and can be successfully applied even to users with small consumption history. Our findings can be used by news providers to improve online services, thus increasing readers’ perceived satisfaction.

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Section: User Profilingmentioning

confidence: 99%

Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations

et al. 2020

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“…Network analysis has been widely adopted for musical piece recommendation [24]- [34] and video recommendation [56]- [61]. The most-widely-used approach based on network analysis is construction of a graph regularization model that can rank music videos accurately [27]- [32]. Specifically, a hypergraph that can capture interactions among musical pieces, users and tags was analyzed for musical piece recommendation [27], [28].…”

Section: B Musical Piece Recommendation and Video Recommendation Basmentioning

confidence: 99%

Music Video Recommendation Based on Link Prediction Considering Local and Global Structures of a Network

et al. 2019

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A novel method for music video recommendation is presented in this paper. The contributions of this paper are twofold. (i) The proposed method constructs a network, which not only represents relationships between music videos and users but also captures multi-modal features of music videos. This enables collaborative use of multi-modal features such as audio, visual, and textual features, and multiple social metadata that can represent relationships between music videos and users on video hosting services. (ii) A novel scheme for link prediction considering local and global structures of the network (LP-LGSN) is newly derived by fusing multiple link prediction scores based on both local and global structures. By using the LP-LGSN to predict the degrees to which users desire music videos, the proposed method can recommend users' desired music videos. The experimental results for a real-world dataset constructed from YouTube-8M show the effectiveness of the proposed method.

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“…(Ostuni et al, 2015) took a different approach and suggested to use tags and sound description represented as a knowledge graph, from which similarity of nodes was extracted using a specific metric they defined. (Mao et al, 2016) suggested using graph representation for music tracks recommendations, where they represented by graphs the relative preferences of users, e.g., pair-wise preference of tracks. They used the graph as a representation for user preferences for tracks and calculated the probability of a user liking a track based on the probability that s/he likes the in-linked tracks.…”

Section: Graph-based Recommender Systemsmentioning

confidence: 99%

Graph-based recommendations: from data representation to feature extraction and application

Tiroshi

Kuflik

Berkovsky

et al. 2019

Big Data Recommender Systems - Volume 2: Application Paradigms

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Modeling users for the purpose of identifying their preferences and then personalizing services on the basis of these models is a complex task, primarily due to the need to take into consideration various explicit and implicit signals, missing or uncertain information, contextual aspects, and more. In this study, a novel generic approach for uncovering latent preference patterns from user data is proposed and evaluated. The approach relies on representing the data using graphs, and then systematically extracting graph-based features and using them to enrich the original user models. The extracted features encapsulate complex relationships between users, items, and metadata. The enhanced user models can then serve as an input to any recommendation algorithm. The proposed approach is domain-independent (demonstrated on data from movies, music, and business recommender systems), and is evaluated using several state-of-the-art machine learning methods, on different recommendation tasks, and using different evaluation metrics. The results show a unanimous improvement in the recommendation accuracy across tasks and domains. In addition, the evaluation provides a deeper analysis regarding the performance of the approach in special scenarios, including high sparsity and variability of ratings.

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Music recommendation using graph based quality model

Cited by 21 publications

References 15 publications

Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations

Consumer Attitudes toward News Delivering: An Experimental Evaluation of the Use and Efficacy of Personalized Recommendations

Music Video Recommendation Based on Link Prediction Considering Local and Global Structures of a Network

Graph-based recommendations: from data representation to feature extraction and application

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