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- W2611990305 abstract "We view the prediction of Facebook likes as a content suggestion problem and show that likes can be much better predicted considered post content or user engagement. Experiments presented are based on a dataset of over 4 million likes collected from over seventy thousands of users in fan pages. The proposed model adopts the similarity metric to appraise how a user may like a document given his or her liked documents, as well as the Restricted Boltzmann Machine (RBM) to estimate whether a user would like a document given the like records of all users. The model achieves a precision of 5–10% and a recall of 2–55%. The commonly used label propagation model is implemented and tested as a baseline. Different models and settings are compared and results show superiority of the proposed model." @default.
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- W2611990305 date "2017-01-01" @default.
- W2611990305 modified "2023-09-23" @default.
- W2611990305 title "How to Get Endorsements? Predicting Facebook Likes Using Post Content and User Engagement" @default.
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- W2611990305 doi "https://doi.org/10.1007/978-3-319-58484-3_15" @default.
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