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.

Highlights

  • People’s news reading habits have gradually shifted to digital content services

  • We propose a Fine-grained Interest Matching network (FIM), which is a new architecture for news recommendation that can tackle the above challenges

  • The reason might be that handcrafted features are usually not optimal, and deep neural networks take the advantages of extracting implicit semantic features and modeling latent relationships between user and news representations

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Summary

Introduction

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 (Das et al, 2007; Lavie et al, 2010). The overwhelming number of newly-sprung news makes it difficult for users to find their interested content (Wu et al, 2019c). Personalized news recommendation becomes an important technology to. D2 This woman lost 245 pounds over 5 years. D3 Watch: Philip Rivers hilariously trolls Chiefs fans after win

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