Fine-grained Interest Matching for Neural News Recommendation (2020)

Abstract: Personalized news recommendation is a critical technology to improve users’ online news reading experience. The core of news recommendation is accurate matching between user’s interests and candidate news. The same user usually has diverse interests that are reflected in different news she has browsed. Meanwhile, important semantic features of news are implied in text segments of different granularities. Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation. In this paper, we propose FIM, a Fine-grained Interest Matching method for neural news recommendation. Instead of aggregating user’s all historical browsed news into a unified vector, we hierarchically construct multi-level representations for each news via stacked dilated convolutions. Then we perform fine-grained matching between segment pairs of each browsed news and the candidate news at each semantic level. High-order salient signals are then identified by resembling the hierarchy of image recognition for final click prediction. Extensive experiments on a real-world dataset from MSN news validate the effectiveness of our model on news recommendation.

## introduction recently, people's news reading habits have gradually shifted to digital content services. many online news websites, such as google news 1 and msn news 2 , aim to collect news from various sources and distribute them for users @xcite . however, the overwhelming number of newly-sprung news makes it difficult for users to find their interested content @xcite . therefore, personalized news recommendation becomes an important technology to 1 https://news.google.com/ 2 https://www.msn.com/news alleviate information overload and improve users' online reading experience @xcite . ## related works with the explosive growth of digital news, building personalized news recommender systems has drawn more attentions in both natural language processing and data mining fields @xcite @xcite . conventional news recommendation methods focus on utilizing manual feature engineering to build news and user representations for matching @xcite @xcite @xcite . for example, @xcite used topic categories and interest features generated by a bayesian model to build news and user representations. @xcite extracted topic and location features from wikipedia pages to build news representations for locationbased news recommendation. in recent years, deep learning based models have achieved better performance than traditional methods for news recommendation, due to their capabilities of distilling implicit semantic features in news content @xcite @xcite . for example, okura et al. (2017) learned news representations via denoising auto-encoders, then used recurrent neural networks to aggregate historical browsed ... ... ... ... 3d cnn word embedding multi-grained news representation news-by-news matching matching matrices aggregation news representation module cross matching module click prediction module historical browsed news candidate news 3d matching image q ... hdc, @xmath0, @xmath1, @xmath2, @xmath3, @xmath4, @xmath5, @xmath6, @xmath7, @xmath8, @xmath9, @xmath10, @xmath11. wang et al. (2018) enhanced the representation of news by exploiting the embeddings of extracted entities in a knowledge graph as a separate channel of the cnn input. wu et al. (2019e) leveraged multi-head selfattentions to construct news representations based on the interactions between words, and constructed user representations based on the relatedness between news. an et al. (2019) proposed to learn long-term user preferences from the embeddings of their ids, and learn short-term user interests from their recently browsed news via gru network. (wu et al., 2019a) proposed an attentive multi-view learning model to learn unified news representations from titles, bodies and topic categories by regarding them as different views of news. different from these existing methods, in fim, the representations of user's multiple browsed news are not fused into an abstract user vector before matching with the candidate news. instead, we perform matching between each pair of segments in the news texts from multiple semantic levels. therefore, more fine-grained information can be distilled for the final recommendation. ## analysis in this section, we further investigate the impacts of different parameters and inputs on the model performance, and discuss the contribution of multigrained representation and matching architecture. ## conclusion and future work in this paper, we propose a new architecture for neural news recommendation based on multi-grained representation and matching. different from previous work that first integrates user's reading history into a single representation vector and then matches the candidate news representation, our model can capture more fine-grained interest matching signals by performing interactions between each pair of news at multi-level semantic granularities. extensive experiments on a real-world dataset collected from msn news show that our model significantly outperforms the state-of-the-art methods. in the future, we will do more tests and surveys on the improvement of business objectives such as user experience, user engagement and service revenue.