Multi-interest Extraction Joint with Contrastive Learning for News Recommendation

News recommendation techniques aim to recommend candidate news to target user that he may be interested in, according to his browsed historical news. At present, existing works usually tend to represent user reading interest using a single vector during the modeling procedure. Actually, it is obviously that users usually have multiple and diverse interest in reality, such as sports, entertainment and so on. Thus it is irrational to represent user sophisticated semantic interest simply utilizing a single vector, which may conceal fine-grained information. In this work, we propose a novel method combining multi-interest extraction with contrastive learning, named MIECL, to tackle the above problem. Specifically, first, we construct several interest prototypes and design a multi-interest user encoder to learn multiple user representations under different interest conditions simultaneously. Then we adopt a graph-enhanced user encoder to enrich user corresponding semantic representation under each interest background through aggregating relevant information from neighbors. Finally, we contrast user multi-interest representations and interest prototype vectors to optimize the user representations themselves, in order to promote dissimilar semantic interest away from each other. We conduct experiments on two real-world news recommendation datasets MIND-Large, MIND-Small and empirical results demonstrate the effectiveness of our approach from multiple perspectives.