About: Herding Effect based Attention for Personalized Time-Sync Video Recommendation

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Herding Effect based Attention for Personalized Time-Sync Video Recommendation
Creator	Gao, Wenyuan Jia, Weijia Luo, Yutao Yang, Wenmian Zhang, Shaohua Zhou, Xiaojie
Source	ArXiv
abstract	Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78/% higher than the state-of-the-art method upon F1-score in baselines.
has issue date	2019-05-02(xsd:dateTime)
bibo:doi	10.1109/icme.2019.00085
has license	arxiv
sha1sum (hex)	d935fed1ee9d00a0532973ecdd9e28a73eea4502
schema:url	https://doi.org/10.1109/icme.2019.00085
resource representing a document's title	Herding Effect based Attention for Personalized Time-Sync Video Recommendation
resource representing a document's body	covid:d935fed1ee9d00a0532973ecdd9e28a73eea4502#body_text
is schema:about of	named entity 'FILTERING' named entity 'ITF' named entity 'USE' named entity 'PREDICT' named entity 'collaborative filtering' named entity 'semantic' named entity 'real-time' named entity 'TIME' named entity 'image information' named entity 'semantic similarity' named entity 'Latent Dirichlet Allocation' named entity 'LSTM' named entity 'timestamp' named entity 'timestamp' named entity 'Bilibili' named entity 'timestamp' named entity 'LDA' named entity 'Section 3.1' named entity 'Herding Effect' named entity 'CNN' named entity 'LSTM' named entity 'image information' named entity 'feature extraction' named entity 'binary classification' named entity 'herding effect' named entity 'natural language processing' named entity 'tsc2' named entity 'timestamp' named entity 'LSTM' named entity 'video-sharing websites' named entity 'neural network' named entity 'herding effect' named entity '3, 4' named entity 'user ID' named entity 'semantic similarity' named entity 'timestamp' named entity 'herding effect' named entity 'ImageNet' named entity 'cross-entropy' named entity 'timestamp' named entity 'LSTM' named entity 'validation set' named entity 'Bilibili' named entity 'sentiment analysis' named entity 'neural network' named entity 'vsl' named entity 'validation set' named entity 'recommendation systems' named entity 'herding effect' named entity 'herding effect' named entity 'real-time' named entity 'Herding Effect' named entity 'HERDING EFFECT' named entity 'validation set' named entity 'timestamp' named entity 'softmax' named entity 'vsl' named entity 'Top5' named entity 'Herding Effect' named entity 'prei' named entity 'state-of-the-art' named entity 'IGNORE' named entity 'EXPERIMENTS' named entity 'CONSIDERING' named entity 'PAPER' named entity 'FUSION MODEL' named entity 'CONTAINS' named entity 'METHODS' named entity 'USER' named entity 'SYNC' named entity 'TIME' named entity 'China' named entity 'topic modeling' named entity 'LSTM' named entity 'Collaborative Filtering' named entity 'activation function' named entity 'loss function' named entity 'image information' named entity 'prei' named entity 'Herding Effect'

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