Predicting e-book ranking based on the implicit user feedback

Bin Cao, Chenyu Hou, Hongjie Peng, Jing Fan, Jian Yang, Jianwei Yin, Shuiguang Deng

Abstract: In this paper, we plan to predict a ranking on e-books by analyzing the implicit user behavior, and the goal of our work is to optimize the ranking results to be close to that of the ground truth ranking where e-books are ordered by their corresponding reader number. As far as we know, there exist little work on predicting the future e-book ranking. To this end, through analyzing various user behavior from a popular e-book reading mobile APP, we construct three groups of features that are related to e-book ranking, where some features are created based on the popular metrics from the e-commerce, e.g., conversion rates. Then, we firstly propose a baseline method by using the idea of learning to rank (L2R), where we train the ranking model for each e-book by taking all its past user feedback within a time interval into consideration. Then we further propose TDLR: a Time Decay based Learning to Rank method, where we separately train the ranking model on each day and combine these models by gradually decaying the importance of them over time. Through extensive experimental studies on the real-world dataset, our approach TDLR is proved to significantly improve the e-book ranking quality more than 10% when compared with the L2R method where no time decay is considered.