User behavior analysis and commodity recommendation for point-earning apps

Yu Ching Chen, Chia Ching Yang, Yan Jian Liau, Chia Hui Chang, Pin Liang Chen, Ping Che Yang, Tsun Ku

研究成果: 書貢獻/報告類型會議論文篇章同行評審

6 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面170-177
頁數8
ISBN(電子)9781509057320
DOIs
出版狀態已出版 - 16 3月 2017
事件2016 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016 - Hsinchu, Taiwan
持續時間: 25 11月 201627 11月 2016

出版系列

名字TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings

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???event.eventtypes.event.conference???2016 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016
國家/地區Taiwan
城市Hsinchu
期間25/11/1627/11/16

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