An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations

The effectiveness of product recommendations is previously assessed based on recommendation accuracy. Recently, individual diversity and aggregate diversity of product recommendations have been recognized as important dimensions in evaluating the recommendation effectiveness. However, the gain of either diversity is usually at the cost of accuracy and the increase of one diversity does not guarantee a significant improvement in the other. A few attempts have been made to achieve reasonable trade-offs either between recommendation accuracy and individual diversity or between recommendation accuracy and aggregate diversity. Little attention has been paid to obtain a balance among the three important aspects of product recommendations. To address this problem, we propose an adjustable re-ranking approach that incorporates two new ranking criteria for improving both diversities. Three ranking lists are generated to guarantee recommendation accuracy, individual diversity, and aggregate diversity, respectively. The three ranking lists are finally merged with tunable parameters to generate a recommendation list. To evaluate the proposed method, experiments are conducted on a data set obtained from Alibaba. The results show that the proposed method achieves much higher improvements in both diversities than the baseline methods when sacrificing the same amount of recommendation accuracy.