Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2017

Abstract

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The “conversion” here refers to actually a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms.The challenging question: what products should be recommended for a given time period to maximize conversion—is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model (rTCP), which considers both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accommodate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high goodness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for advertising and product recommendation.

Keywords

conversion time, survival analysis, product recommendation, advertising

Discipline

Databases and Information Systems | Data Storage Systems

Research Areas

Data Science and Engineering

Publication

Frontiers of Computer Science

Volume

11

Issue

4

First Page

702

Last Page

716

ISSN

2095-2228

Identifier

10.1007/s11704-016-5546-y

Publisher

Springer

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s11704-016-5546-y

Download

Share

COinS