Recommender systems are intelligent tools to extract useful information from a large collection of online data. They have been widely used in various fields, including the recommendation of music, movies, documents, tourism attraction, e-learning and e-commerce. Many approaches, such as content-based filtering and collaborative filtering, have been proposed to run the recommender system, but they are not completely compatible with the m-commerce context. Therefore, this paper focuses on how to develop a recommender model that can be applied to the mobile environment. In addition, this paper also presents the methods to preprocess the data. Through applying the model to a real-world data supported by Alibaba Group, it is shown that our model works effectively in m-commerce.
CITATION STYLE
Chen, S., Qian, H., & Gu, J. (2015). Multi-disciplinary Trends in Artificial Intelligence. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9426, 415–428. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84952319789&partnerID=tZOtx3y1
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