H ie R ec: Hierarchical User Interest Modeling for Personalized News Recommendation (2021)

Abstract: 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 interest. However, user interest is usually diverse and multi-grained, which is difficult to be accurately modeled by a single user embedding. In this paper, we propose a news recommendation method with hierarchical user interest modeling, named HieRec. Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news. We use a three-level hierarchy to represent 1) overall user interest; 2) user interest in coarse-grained topics like sports; and 3) user interest in fine-grained topics like football. Moreover, we propose a hierarchical user interest matching framework to match candidate news with different levels of user interest for more accurate user interest targeting. Extensive experiments on two real-world datasets validate our method can effectively improve the performance of user modeling for personalized news recommendation.

## introduction recently, massive people are habituated to reading news articles on online news platforms, such as google news and microsoft news @xcite . to help users efficiently obtain their interested news information, personalized news recommendation technique that aims to recommend news according to user interests, is widely used by these platforms @xcite @xcite . user interest modeling is a critical step for personalized news recommendation @xcite @xcite . existing methods usually learn a single representation vector to model overall user interests from users' clicked news @xcite @xcite . for example, @xcite used a gru network to model user interests from clicked news. they used the latest hidden state of gru as the user interest representation. @xcite used multi-head self-attention network to capture user interests, and used an attentive pooling network to obtain a unified user representation. however, user interest is usually diverse and multigrained. for example, as shown in fig. 1 , a user may have interest in movies, sports, finance and health at the same time. in addition, for users who are interested in sports, some of them may have general interest in this area, while other users like the example user in fig. 1 may only have interest in a specific sport like football. however, it is difficult for these methods to accurately model the diverse and multi-grained user interest for news recommendation via a single user embedding. in this paper, we propose a personalized news recommendation approach with hierarchical user interest modeling, named hierec, which can effectively capture the diverse and multi-grained user interest. our approach contains three levels of user interest representations to model user interests in different aspects and granularities. the first one is subtopic-level, which contains multiple interest representations to model fine-grained user interests in different news subtopics (e.g., interest in football and golf). they are learned from embeddings of subtopics and the clicked news in the correspond-ing subtopics. the second one is topic-level, which contains multiple interest representations to capture coarse-grained user interests in major news topics (e.g., interest in sports and finance). they are learned from embeddings of news topics and their subordinate subtopic-level interest representations. the third one is user-level, which contains an interest representation to model overall user interests. it is learned from topic-level interest representations. besides, we propose a hierarchical user interest matching framework to match candidate news with different levels of interest representations to target user interests more accurately. extensive experiments on two real-world datasets show that hierec can effectively improve the accuracy of user interest modeling and news recommendation. ## related work personalized news recommendation is an important intelligent application and is widely studied in recent years @xcite @xcite . existing methods usually model news from its content, model user interest from user's clicked news, and recommend candidate news based on their relevance with user interests @xcite . for example, @xcite utilized an auto-encoder to learn news representations from news bodies. they applied a gru network to capture user interests from the sequence of users' historical clicks and used the last hidden state vector of gru as user interest representation. besides, they proposed to model relevance between user interest and candidate news based on the dot product of their representations. @xcite learned news representations from news titles, bodies, categories, and subcategories based on an attentive multi-view learning framework. they build user interest representation based on the attentive aggregation of clicked news representations. @xcite used a cnn network to learn news representations from news titles and categories. they applied a gru network to user's clicked news to build a shortterm user interest representation and applied user id embedding to learn long-term user interest representation. they further learned a unified user interest representation based on the aggregation of short-and long-term user interest representation. @xcite proposed to learn news representations from news titles and entities via a knowledge graph attention network. they also obtained user interest representation from representations of clicked news via an attention network. besides, all of these three methods adopted the inner product for matching candidate news. most existing methods learn a single user embedding to represent the overall user interests @xcite ). however, user interests are usually very diverse and multi-grained, which are difficult to be accurately modeled by a single user embedding. different from these methods, we propose a hierarchical user interest modeling framework to model user interests in different aspects and granularities. in addition, we propose a hierarchical user interest matching framework to understand user interest in candidate news from different interest granularities for more accurate user interest targeting. ## hierec in this section, we first give a problem formulation of personalized news recommendation. then we introduce our hierec method in detail. ## conclusion in this paper, we propose a personalized news recommendation method named hierec for hierarchical user interest modeling, which can effectively model diverse and multi-grained user interests. hi-erec learns a three-level hierarchy to represent user interest in different aspects and granularity. first, we learn multiple subtopic-level interest representations to model fine-grained user interests in different news subtopics. second, we learn multiple topic-level interest representations to model coarse-grained user interests in several major news topics. third, we learn a user-level interest representation to model overall user interests. besides, we propose a hierarchical user interest matching framework to match candidate news with user interest from different granularity for more accurate user interest targeting. extensive experiments on two real-world datasets show the effectiveness of hierec in user interest modeling. in this paper, we present hierec to model diverse and multi-grained user interest. hierec can be applied to online news platforms for personalized news recommendation, which can help platforms improve user experience and help users find interested news information. although hierec can bring many benefits, it may also have several potential risks, which we will discuss in detail. accuracy although hierec outperforms baseline methods in term of recommendation accuracy ( privacy in hierec, we rely on user behavior data centrally stored on the news platform for model training and online services. user behavior data is usually privacy-sensitive, and its centralized storage may lead to privacy concerns and risks. in the future, we will explore to train and deploy hi-erec in a more privacy-preserving way based on some effective privacy protection techniques like federated learning @xcite . diversity filter bubbles and echo chambers are the common problem for many recommender systems @xcite , which harms user experience. improving recommendation diversity has the potential to alleviate the problem of filter bubbles and echo chambers. through experiments in fig. 7 , we find that hierec can outperform many news recommendation methods in term of recommendation diversity. thus, compared with existing methods, hierec has the potential to alleviate filter bubble problem to some extent. besides, in order to further improve recommendation diversity, hierec can be combined with some existing methods in this field like dpp @xcite . fake news and clickbait there may be some fake news and clickbait in some online platforms. in order to handle the negative social impact and the user experience harm brought by these fake news and clickbait, online news platforms can use some existing fake news detection and clickbait detection techniques such as @xcite to filter these kinds of news before applying hierec for personalized recommendation. fairness like many other recommender systems, hierec relies on user behavior data for model training and online service. the bias in user behavior data may lead to some specific groups of users not be able to receive news information with sufficient accuracy and diversity, and the recommendation results may be more suitable for some major populations. recently, some fairness-aware recommendation methods like fairrec @xcite have been proposed to eliminate bias and unfairness in recommender systems. we can combine hierec with these methods to improve the fairness of the recommendation results and mitigate the harms for marginalized populations. misuse the proposed hierec method works in a data-driven way. it trains the model from the user logs and makes personalized recommendations to users based on their interest inferred from their clicked news. however, in some extreme cases, the recommendation results may be maliciously manipulated to influence users. to avoid the potential misuse, the usage of hierec should comply with the regulations and laws, and intentional manipulation should be prohibited.