Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation
Yaokun Liu, Xiaowang Zhang, Minghui Zou, Zhiyong Feng · 2023
Multi-interest recommendation methods extract multiple interest vectors to represent the user comprehensively. Despite their success in the matching stage, previous works overlook the long-tail problem. This results in the model excelling at suggesting head items, while the performance for tail items, which make up more than 70% of all items, remains suboptimal. Hence, enhancing the tail item recommendation capability holds great potential for improving the performance of the multi-interest model.