An Improved Video Recommendations Based on the Hyperlink-Graph Model

Shang, Wenqian; Shang, Songtao; Feng, Shuchao; Shi, Minjia

doi:10.1109/acit-csii-bcd.2016.079

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…(iii) Multilayer networks: while most recommendation algorithms do not consider interactions that can take place between users and items, recent works have proposed to encode them in multilayer networks. Shang et al proposed a video recommendation algorithm based on a hyperlink multilayer graph model [28]. Yasami proposed a new knowledge-based link recommendation approach using a nonparametric multilayer model of dynamic complex networks [29].…”

Section: Related Workmentioning

confidence: 99%

Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism

et al. 2020

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The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. While the literature of recommendation algorithms is vast, most collaborative filtering recommendation approaches attain low recommendation accuracies and are also unable to track temporal changes of preferences. Additionally, previous differential clustering evolution processes relied on a single-layer network and used a single scalar quantity to characterise the status values of users and items. To address these limitations, this paper proposes an effective collaborative filtering recommendation algorithm based on a double-layer network. This algorithm is capable of fully exploring dynamical changes of user preference over time and integrates the user and item layers via an attention mechanism to build a double-layer network model. Experiments on Movielens, CiaoDVD, and Filmtrust datasets verify the effectiveness of our proposed algorithm. Experimental results show that our proposed algorithm can attain a better performance than other state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%

Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism

et al. 2020

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“…Methods that use basic technologies of network analysis such as graph clustering [33], [57], [58], random walk [33], [59], community detection [60], [61] and graph embedding [34], [61] have been also proposed. Pang et al [58] realize accurate video recommendation by graph clustering that uses visual features.…”

Section: B Musical Piece Recommendation and Video Recommendation Basmentioning

confidence: 99%

“…Pang et al [58] realize accurate video recommendation by graph clustering that uses visual features. Also, Shang et al [57] constructed a graph based on Web page links and performed clustering based on Jaccard coefficients for video recommendation. Furthermore, the method in [59] realizes accurate recommendation by learning similarities of the network structure via CNN.…”

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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Recommender System for Generic User Preferences for Online Content

Raju

Poravi

2018

2018 3rd International Conference for Convergence in Technology (I2CT)

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An Improved Video Recommendations Based on the Hyperlink-Graph Model

Cited by 3 publications

References 16 publications

Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism

Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism

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

Recommender System for Generic User Preferences for Online Content

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