MINER : Multi-Interest Matching Network for News Recommendation (2022)

Abstract: Personalized news recommendation is an essential technique to help users find interested news. Accurately matching user’s interests and candidate news is the key to news recommendation. Most existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest. However, user interest is usually diverse and may not be adequately modeled by a single user embedding. In this paper, we propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. We further propose a disagreement regularization to make the learned interests vectors more diverse. Moreover, we design a category-aware attention weighting strategy that incorporates the news category information as explicit interest signals into the attention mechanism. Extensive experiments on the MIND news recommendation benchmark demonstrate that our approach significantly outperforms existing state-of-the-art methods.

## introduction online news platforms such as google news 1 and microsoft news 2 have become a prevalent way for users to access news information @xcite . the large amount of news generated every day make it hard for users to find their interested news. to alleviate information overload and improve reading experience, personalized news recommendation has become an essential part of these platforms @xcite . accurate matching between users' interests and candidate news is the key to personalized news recommendation. existing methods usually learn a single user interest vector by aggregating the previously browsed news via sequential or attentive models and then match it with candidate news vectors. for example, @xcite gru network to model user interest from the sequence of clicked news with the last hidden state of gru being the user interest representation. @xcite also use a gru network to aggregate clicked news sequence as an interest vector and combine it with the user id embedding. @xcite and @xcite apply attentive pooling on the sequence of clicked news vectors to obtain user representations. however, user interest is usually varying and diverse. as the example shown in figure 1 , a user may be interested in different types of news (with distinct background colors) such as finance, sports, and movies. therefore, it is insufficient for the above methods to accurately model user interest via a single user embedding, especially when the user has multiple interests with a long browsing history. in this paper, we propose a multi-interest matching network for news recommendation (namely miner), which can effectively capture the diverse nature of user's reading interests. specifically, we first employ pre-trained bert @xcite as the news encoder which is highly effective in modeling the text semantics. with the encoded news representation sequence, we propose a poly attention scheme to extract multiple interest vectors for each user. a matching score is calculated for each interest vector and the final matching score is aggregated by the individual scores. we study various aggregation methods including maximum, average, and weighted sum. furthermore, to make the extracted user interest representations more diverse, we propose a disagreement regularization @xcite which enlarges the distance among different interest vectors. in addition, news category information is usually available as shown in figure 1 , which reveals explicit user interest signals. to capture such signals, we propose a category-aware attention weighting strategy in the poly attention where historical news are re-weighted based on the category similarity to candidate news. we conduct extensive experiments and analysis on the realworld mind news recommendation dataset @xcite , and the results show that miner significantly outperforms the existing approaches. the main contributions of this work can be summarized as follows: ## our approach in this section, we first formulate the problem of personalized news recommendation. then we introduce our proposed miner in detail, whose overall framework is shown in figure 2 . ## conclusion in this paper, we propose a news recommendation method named miner to capture the diver user interests from the historical reading behaviors, rather than most existing methods that learn a single user embedding to represent the reading interest. specifically, we propose a poly attention scheme to learn multiple user interest vectors through soft attentions, which encode the different aspects of user interest. we further propose a disagreement regularization to improve the poly attention, that makes the learned interests vectors more diverse. moreover, we design a categoryaware attention weighting strategy to re-weight historical news according to the category similarity. extensive experiments on the mind news recommendation benchmark demonstrate the superiority of miner over existing state-of-the-art methods. in addition, miner ranked the first on the mind leaderboard in september 2021. future work includes extending miner to multimodal and multi-task scenarios @xcite .