Issue No. 02 - Feb. (2018 vol. 30)
Le Wu , School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China
Qi Liu , School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
Richang Hong , School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China
Enhong Chen , School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China
Yong Ge , Department of Management Information Systems, University of Arizona, Tucson, AZ
Xing Xie , Microsoft Research, Beijing, China
Meng Wang , School of Computer and Information, Hefei University of Technology, Hefei, Anhui, China
As the worlds of commerce and the Internet technology become more inextricably linked, a large number of user consumption series become available for online market intelligence analysis. A critical demand along this line is to predict the future product adoption state of each user, which enables a wide range of applications such as targeted marketing. Nevertheless, previous works only aimed at predicting if a user would adopt a particular product or not with a binary buy-or-not representation. The problem of tracking and predicting users’ adoption rates, i.e., the frequency and regularity of using each product over time, is still under-explored. To this end, we present a comprehensive study of product adoption rate prediction in a competitive market. This task is nontrivial as there are three major challenges in modeling users’ complex adoption states: the heterogeneous data sources around users, the unique user preference and the competitive product selection. To deal with these challenges, we first introduce a flexible factor-based decision function to capture the change of users’ product adoption rate over time, where various factors that may influence users’ decisions from heterogeneous data sources can be leveraged. Using this factor-based decision function, we then provide two corresponding models to learn the parameters of the decision function with both generalized and personalized assumptions of users’ preferences. We further study how to leverage the competition among different products and simultaneously learn product competition and users’ preferences with both generalized and personalized assumptions. Finally, extensive experiments on two real-world datasets show the superiority of our proposed models.
Smart devices, Social network services, Recommender systems, Predictive models, Electronic mail, Time-frequency analysis
L. Wu et al., "Product Adoption Rate Prediction in a Competitive Market," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 2, pp. 325-338, 2018.