A Novel Recommendation Model for Online-to-Offline Service Based on the Customer Network and Service Location

ABSTRACT

We propose 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.

KEYWORDS:

• Online-to-offline model
• O2O service recommendation
• customer network
• service location
• rating matrix
• sparse data

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant 71825007, in part by the Chinese Academy of Sciences Frontier Scientific Research Key Project under Grant QYZDB-SSW-SYS021, in part by the Marianne and Marcus Wallenberg Foundation under Grant MMW 2015.0007, in part by the Strategic Priority Research Program of CAS under Grant XDA23020203, in part by the supported by the International Partnership Program of Chinese Academy of Sciences, Grant No.211211KYSB20180042, and supported by the Junior Fellowships of CAST Advanced S&T Think-tank Programs-Doctoral Programs (Grant CXY-ZKQN-2019-042).

Notes on contributors

Yuchen Pan

Yuchen Pan ([email protected]/[email protected]) is working toward the Ph.D. degree at the School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China. His research interests include recommender systems and data mining.

Desheng Wu

Desheng Wu ([email protected]) is with the School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China, and also with the Stockholm Business School, Stockholm University, Sweden. His research interests include enterprise risk management in operations, performance evaluation in financial industry, and decision sciences. Dr. Wu has authored or coauthored more than 100 papers in refereed journals, such as Production and Operations Management, Decision Support Systems, Decision Sciences, Risk Analysis, IEEE Transactions on Systems Man and Cybernetics, and others. He has served as Associate or Guest Editor of several journals.