Towards True Multi-interest Recommendation: Enhanced Scheme for Balanced Interest Training

How can we accurately capture users’ diverse interests to provide more relevant recommendations based on their historical interactions? Recent advancements in recommender systems have led to the development of multi-interest recommendation models that attempt to capture the diverse interests of users through multiple interest vectors. While theoretically promising, existing implementations frequently struggle with oversimplifying user interests, where models tend to focus on a single dominant vector and overlook the relationships between multiple interests, failing to represent the full complexity of users’ interests. This limits the models’ ability to truly personalize and diversify the recommendations provided to users. In response to this challenge, we propose BaM (Ba lanced Interest Learning for Multi-interest Recommendation), a versatile training scheme tailored for multi-interest recommendation models that ensures the full utilization of all interest vectors, leading to more effective recommendations. Instead of prioritizing an interest vector with the highest similarity to the ground-truth item for loss computation, BaM exploits a soft-selection approach, ensuring balanced training across multiple interest vectors. Furthermore, BaM trains all interest representations simultaneously through a multi-interest loss function that accounts for the contributions of every interest. This allows for a broader consideration of multiple interest vectors which are also related to the users’ diverse preferences with varying degrees of relevance. Extensive experiments with real-world datasets show that BaM achieves up to 31.43% higher accuracy in sequential recommendation compared to the best competitor, resulting in the state-of-the-art performance.