Diversity-Promoting Recommendation With Dual-Objective Optimization and Dual Consideration

Diversifying recommendations to broaden user horizons and explore potential interests has become a prominent research area in recommender systems. Although numerous efforts have been made to enhance diverse recommendations, the trade-off between diversity and accuracy remains a significant challenge. The primary causes lie in the following two aspects: (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i>) the inherent goals of diversity-promoting recommendation, which are to simultaneously deliver accurate recommendations and cater to a broader spectrum of users’ interests, have not been adequately explored; and (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i>) considering diversity only in the model training procedure cannot guarantee the provision of diversification services in recommender systems. In this work, we directly formulate the inherent goals of diversity-promoting recommendation as a dual-objective optimization problem by simultaneously minimizing the recommendation error and maximizing diversity. These proposed objectives are integrated into Generative Adversarial Nets (GANs) to guide the training process toward the orientation of boosting both diversification and accuracy. Additionally, we propose considering diversity in both training and serving phases. Experimental results demonstrate that our model outperforms others in both diversity and relevance. We extend DDPR to state-of-the-art CTR and re-ranking models, which also result in improved performance on these tasks, further demonstrating the applicability of our model in real-world scenarios.