Fine-grained Interest Matching for Neural News Recommendation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
News RecommendationSimilar Papers 제목 키워드 기반
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
User interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually learn a single user embedding for each user from their previous behaviors to represent their overall i…
News RecommendationUNBERT: User-News Matching BERT for News Recommendation
Nowadays, news recommendation has become a popular channel for users to access news of their interests. How to represent rich textual contents of news and precisely match users' interests and candidate news lies in the c…
News RecommendationFUM: Fine-grained and Fast User Modeling for News Recommendation
User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user embedding. However, the word-level intera…
News RecommendationIs This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Personalized news recommendation aims to deliver news articles aligned with users' interests, serving as a key solution to alleviate the problem of information overload on online news platforms. While prior work has impr…
Learning to Select Historical News Articles for Interaction based Neural News Recommendation
The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently. Most existing approaches embed user interests into a representation vector then recommend by…
ArticlesNews Recommendation