Target-aware Aggregate Diversification in Recommendation

Businesses often deploy recommenders to help users fulfil their needs and to provide a better user experience with their platform. However, they may also need to guide users towards a certain content depending on time, situation, and business needs. For example, giving a fair exposure opportunity to less popular items provides a healthier business model in which more suppliers can survive and thrive. In this work, we explore target-aware diversification as an approach to mitigate exposure bias in a calibrated way. Provided with a target ratio for each category’s exposure, we balance this objective with the relevance of the recommended items through two main approaches: 1) diversification based only on the system’s predefined target and 2) diversification based on system’s target while taking into account the user’s tolerance for diversity. We explore the effectiveness of our proposed models on a publicly available dataset. Experimental results show that our approach can systematically diversify the recommendations towards a pre-defined target while maintaining the relevance of the recommendations to a good extent. We also conclude that the trade-off between achieving the target and maintaining relevance has a close connection with the feasibility of the defined target given the previous users’ consumption.