Recently, Phd student PAN Yuchen at School of Economics and Mnagement of University of Chinese Academy of Sciences proposed a new online-to-offline (O2O) service recommendation method based on a novel customer network and service location (CNLRec) in order to help customer to choose the “ideal” O2O services from a large set of alternatives. Our customer network, based on the “co-used” behaviors obtained from the online rating matrix, captures customers’ online behaviors while service location reflects offline behavior characteristic of the customer. For a target customer, a ranking of candidate services based on their locations and this network is generated, in which customer scale usage bias is eliminated. Our experimental results show that: First, even though the rating matrix is sparse, most customers are connected to our proposed customer network, which largely addresses the problem of sparse data. Second, CNLRec outperforms widely-used and state-of-the-art recommendation methods. In addition, e-commerce recommendations that use CNLRec without including item location information (CNRec) has better performance than existing methods. Third, all attributes in CNLRec, including network attributes (relationship degree and customer attribute) and location attributes, play a significant role in recommendations. Specially, O2O service location plays an important role in O2O service selection. In our research, we find the optimal combinations of these attributes.
The study was published in Journal of Management Information Systems on June 16.
