A Recommender System for Mobile Commerce Based on Relational Learning

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.

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