Prediction of members’ repurchase rates with time weight function

Customer relationship management (CRM) leverages historical users’ behaviors to dawn effort of enhancing customer satisfaction and loyalty. Thus, constructing a successful customer profile plays a critical role in CRM. In this study, we are expected to predict the repurchase rates for the registered members at the specific category of e-shop. However, customers’ preferences change over time. To capture the preference drifts of the members, we propose a novel and simple time function to increase/decrease the weight of the old data in evaluating various members’ past behaviors. Then, we construct a repurchase index with time factor (RIT) model to effectively predict repurchase rates. The marketers of e-shop can thus target the members with high repurchase rates. Experimental results with a real dataset have demonstrated that this RIT model can be practically implemented and provide satisfactory results.

[1]  Donald H. Kraft,et al.  User profiles and fuzzy logic for web retrieval issues , 2002, Soft Comput..

[2]  Amir Albadvi,et al.  A hybrid recommendation technique based on product category attributes , 2009, Expert Syst. Appl..

[3]  Chih-Fong Tsai,et al.  Cluster ensembles in collaborative filtering recommendation , 2012, Appl. Soft Comput..

[4]  A. Garrido Moreno,et al.  ANALYZING THE IMPACT OF KNOWLEDGE MANAGEMENT ON CRM SUCCESS: THE MEDIATING EFFECTS OF ORGANIZATIONAL FACTORS , 2011 .

[5]  Salvatore Sessa,et al.  Fuzzy transforms method and attribute dependency in data analysis , 2010, Inf. Sci..

[6]  Fabrice Guillet,et al.  Domain driven data mining to improve promotional campaign ROI and select marketing channels , 2009, CIKM.

[7]  Lipika Dey,et al.  A new customized document categorization scheme using rough membership , 2005, Appl. Soft Comput..

[8]  Edith Schonberg,et al.  Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising , 2004, Data Mining and Knowledge Discovery.

[9]  Irina Perfilieva,et al.  Fuzzy transforms: Theory and applications , 2006, Fuzzy Sets Syst..

[10]  Salvatore Sessa,et al.  A segmentation method for images compressed by fuzzy transforms , 2010, Fuzzy Sets Syst..

[11]  Ingoo Han,et al.  Detection of the customer time-variant pattern for improving recommender systems , 2005, Expert Syst. Appl..

[12]  R S Duboff.  Marketing to maximize profitability. , 1992, The Journal of business strategy.

[13]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[14]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[15]  Yoon Ho Cho,et al.  Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce , 2004, Expert Syst. Appl..

[16]  Randall B. Smith,et al.  From Kansas to Oz: collaborative debugging when a shared world breaks , 1997, CACM.

[17]  Y.-Q. Zhang,et al.  Web shopping expert using new interval type-2 fuzzy reasoning , 2007, Soft Comput..

[18]  Katsutoshi Yada.  CODIRO: A New System for Obtaining Data Concerning Consumer Behavior Based on Data Factors of High Interest Determined by the Analyst , 2007, Soft Comput..

[19]  Maria E. Orlowska,et al.  A Recommender System with Interest-Drifting , 2007, WISE.

[20]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.

[21]  Tony Cheng-Kui Huang,et al.  Mining the change of customer behavior in fuzzy time-interval sequential patterns , 2012, Appl. Soft Comput..

[22]  Jorge García Duque,et al.  Incentivized provision of metadata, semantic reasoning and time-driven filtering: Making a puzzle of personalized e-commerce , 2010, Expert Syst. Appl..

[23]  You-Jin Park,et al.  Individual and group behavior-based customer profile model for personalized product recommendation , 2009, Expert Syst. Appl..

[24]  Qing Yang,et al.  Time-Dependent Models in Collaborative Filtering Based Recommender System , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[25]  Salvatore Sessa,et al.  Fuzzy transforms method in prediction data analysis , 2011, Fuzzy Sets Syst..

[26]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[27]  P. Preckel,et al.  Customer Lifetime Value: An application in the rural petroleum market , 1997 .

[28]  Xin Yao,et al.  The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift , 2010, IEEE Transactions on Knowledge and Data Engineering.