A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search

TL;DR

This paper introduces PODM-MI, a re-ranking model that balances accuracy and diversity in e-commerce search results by modeling user preferences with mutual information, leading to improved search outcomes.

Contribution

The paper presents a novel preference-oriented diversity re-ranking model using mutual information and variational inference to better align with user preferences in e-commerce search.

Findings

Significant improvement in search result diversity and relevance.

Successful deployment on a real-world e-commerce platform.

Enhanced user satisfaction through balanced ranking.

Abstract

Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enhance the precision of search results, often at the expense of diversity, leading to outcomes that may not fulfill the varied needs of users. Conversely, methods designed to promote diversity might compromise the precision of the results, failing to satisfy the users' requirements for accuracy. To alleviate the above problems, this paper proposes a Preference-oriented Diversity Model Based on Mutual-information (PODM-MI), which consider both accuracy and diversity in the re-ranking process. Specifically, PODM-MI adopts Multidimensional Gaussian distributions based on variational inference to capture users' diversity preferences with uncertainty. Then we maximize the mutual information between the diversity preferences of the users and the candidate items using the maximum variational inference lower bound to enhance their correlations. Subsequently, we derive a utility matrix based on the correlations, enabling the adaptive ranking of items in line with user preferences and establishing a balance between the aforementioned objectives. Experimental results on real-world online e-commerce systems demonstrate the significant improvements of PODM-MI, and we have successfully deployed PODM-MI on an e-commerce search platform.