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- W2603442909 abstract "In recent years, due to the rapid development of e-commerce, personalized recommendation systems have prevailed in product marketing. However, recommendation systems rely heavily on big data, creating a difficult situation for businesses at initial stages of development. We design several methods - including a traditional classifier, heuristic scoring, and machine learning - to build a recommendation system and integrate content-based collaborative filtering for a hybrid recommendation system using Co-Clustering with Augmented Matrices (CCAM). The source, which include users' persona from action taken in the app & Facebook as well as product information derived from the web. For this particular app, more than 50% users have clicks less than 10 times in 1.5 year leading to insufficient data. Thus, we face the challenge of a cold-start problem in analyzing user information. In order to obtain sufficient purchasing records, we analyzed frequent users and used web crawlers to enhance our item-based data, resulting in F-scores from 0.756 to 0.802. Heuristic scoring greatly enhances the efficiency of our recommendation system." @default.
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- W2603442909 date "2016-11-01" @default.
- W2603442909 modified "2023-09-27" @default.
- W2603442909 title "User behavior analysis and commodity recommendation for point-earning apps" @default.
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- W2603442909 doi "https://doi.org/10.1109/taai.2016.7880109" @default.
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