Candidate News Research Articles

Personalized News Recommendation Method with Double-Layer Residual Connections and Double Multi-Head Self-Attention Mechanisms

In today’s society, there is an urgent need to help users better access information that they are interested in, as there is an increasing amount of news and messages available with the development of the Internet. Many existing methods involve directly inputting text into a pre-trained model, which limits the effectiveness of text feature extraction. The personalized news recommendation model discussed in this article is a model that can enhance feature extraction from news articles. It consists of a candidate news module, a historically accessed news module, and an access prediction module. Using news titles that accurately summarize news content, a model with double multi-head attention mechanisms and double residual structures (DDM) is utilized to better capture the features of news articles historically accessed by users, thereby achieving an improved recommendation functionality. The candidate news module aims to help the model learn representations of news that users are likely to select from the news titles. The user historical click news module primarily serves to enable the model to learn personalized representations of users from news they have previously browsed. The model has been tested on MIND-small. The AUC reached 0.6665, the MRR reached 0.3205, the nDCG@5 reached 0.3532, and the nDCG@10 reached 0.4158. The results indicate this model has achieved good results in the downstream tasks of preprocessing news-title texts.

Explaining Neural News Recommendation with Attributions onto Reading Histories

An important aspect of responsible recommendation systems is the transparency of the prediction mechanisms. This is a general challenge for deep-learning-based systems such as the currently predominant neural news recommender architectures which are optimized to predict clicks by matching candidate news items against users’ reading histories. Such systems achieve state-of-the-art click-prediction performance, but the rationale for their decisions is difficult to assess. At the same time, the economic and societal impact of these systems makes such insights very much desirable. In this paper, we ask the question to what extent the recommendations of current news recommender systems are actually based on content-related evidence from reading histories. We approach this question from an explainability perspective. Building on the concept of integrated gradients, we present a neural news recommender that can accurately attribute individual recommendations to news items and words in input reading histories while maintaining a top scoring click-prediction performance. Using our method as a diagnostic tool, we find that: (a), a substantial number of users’ clicks on news are not explainable from reading histories, and many history-explainable items are actually skipped; (b), while many recommendations are based on content-related evidence in histories, for others the model does not attend to reasonable evidence, and recommendations stem from a spurious bias in user representations. Our code is publicly available 1 .

Covering the Campaign: Computational Tools for Measuring Differences in Candidate and Party News Coverage With Application to an Emerging Democracy

How does media coverage of electoral campaigns distinguish parties and candidates in emerging democracies? To answer, we present a multi-step procedure that we apply in South Africa. First, we develop a theoretically informed classification of election coverage as either “narrow” or “broad” from within the entire corpus of news coverage during an electoral campaign. Second, to deploy our classification scheme, we use a supervised machine learning approach to classify news as “broad,” “narrow,” or “not election-related.” Finally, we combine our supervised classification with a topic modeling algorithm (BERTTopic) that is based on Bidirectional Encoder Representations from Transformers (BERT), in addition to other statistical and machine learning methods. The combination of our classification scheme, BERTTopic, and associated methods allows us to identify the main election-related themes among broad and narrow election-related coverage, and how different candidates and parties are associated with these themes. We provide an in-depth discussion of our method for interested users in the social sciences. We then apply our proposed techniques on text from nearly 100,000 news articles during South Africa’s 2014 campaign and test our empirical predictions about candidate and party coverage of corruption, the economy, health, public infrastructure, and security. The application of our method highlights a nuanced campaign environment in South Africa; candidates and parties frequently receive distinct and substantive coverage on key campaign themes.

Dual-view hypergraph attention network for news recommendation

News Recommendation (NR) helps users to quickly find their mostly interested information. Recently, some NR systems based on graph neural networks has achieved significant performance improvement in that they use a graph structure to pair-wisely link two nodes (e.g., user, news, topic node) for representation learning. However, such kind of pairwise edges may be not enough to describe multifaceted relations involving more than two nodes; While a hypergraph structure with one hyperedge connecting two and more nodes might be a better choice. Considering this, we propose a dual-view hypergraph attention network for news recommendations (Hyper4NR) in this paper. In particular, we design a dual-view hypergraph structure to model users’ click history, containing both topic-view hyperedges and semantic-view hyperedges. On the constructed hypergraph, we use hyperedge-specific attention network (HSAN) to pass messages in between hyperedges and nodes to encode their representations based on a self-supervised learning approach. Furthermore, we construct another kind of candidate hypergraph on which we apply the HyperGAT to obtain enhanced candidate news encoding. Extensive experiments on the widely used MIND news dataset and Adressa dataset show that our Hyper4NR outperforms these state-of-the-art NR methods.

A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.

SIIR: Symmetrical Information Interaction Modeling for News Recommendation.

Accurate matching between user and candidate news plays a fundamental role in news recommendation. Most existing studies capture fine-grained user interests through effective user modeling. Nevertheless, user interest representations are often extracted from multiple history news items, while candidate news representations are learned from specific news items. The asymmetry of information density causes invalid matching of user interests and candidate news, which severely affects the click-through rate prediction for specific candidate news. To resolve the problems mentioned above, we propose a symmetrical information interaction modeling for news recommendation (SIIR) in this article. We first design a light interactive attention network for user (LIAU) modeling to extract user interests related to the candidate news and reduce interference of noise effectively. LIAU overcomes the shortcomings of complex structure and high training costs of conventional interaction-based models and makes full use of domain-specific interest tendencies of users. We then propose a novel heterogeneous graph neural network (HGNN) to enhance candidate news representation through the potential relations among news. HGNN builds a candidate news enhancement scheme without user interaction to further facilitate accurate matching with user interests, which mitigates the cold-start problem effectively. Experiments on two realistic news datasets, i.e., MIND and Adressa, demonstrate that SIIR outperforms the state-of-the-art (SOTA) single-model methods by a large margin.

Framing Analysis of Presidential Candidate and Vice Presidential Candidate News From 2024 in Mainstream Media

Tahun 2024 merupakan tahun politik, dimana masyarakat Indonesia pada tahun 2024 ini kembali memilih presiden dan wakil presiden. Pemberitaan pada media massa juga tak kalah heboh menjelang Pemilu 2024 ini. Pada saat ini seluruh media massa, khususnya media elektronik, turut serta mensukseskan Pilpres dengan mensosialisasikan dan memperkenalkan kandidat-kandidat capres dan cawapres kepada khalayak. Penelitian ini bertujuan untuk menganalisis terhadap isi berita. Salah satu cara adalah dengan melakukan framing analysis dari Robert N, Entman. Penelitian ini menggunakan penelitian kualitatif yang bersifat diskriptif analitik dengan jenis penelitian document study. Data diperoleh dari artikel-artikel berita yang berhubungan dengan calon presiden RI 2024 baik dari media Metro TV maupun Kompas TV. Hasil penelitian ini, Metro Tv dalam membingkai berita “Golkar-PAN dukung Prabowo” sebagai bacapres, maka partai-partai yang mendukung Prabowo, diharapkan akan semakin berkurang dukungannya kepada Ganjar Pranowo karena masing-masing partai tersebut akan memiliki bacawapres sendiri-sendiri. Metro TV dalam membingkai berita tentang hasil survei, berpatokan pada “Indikator Politik Indonesia” dan “SRI” memperkirakan bacawapres ada empat kandidat. Kompas TV membingkai berita “Golkar-PAN dukung Prabowo” sebagai bacapres, merupakan hal yang biasa dalam proses demokrasi dimana setiap saat bisa terjadi dinamika politik yang berubah. Kompas TV dalam membingkai berita tentang hasil survei, mereka melakukan survei secara mandiri menyatakan bahwa kandidat bacawapres yang unggul hanya ada dua.

MIAR: Interest-Activated News Recommendation by Fusing Multichannel Information

The different news clicked by users reflects the diverse interests of users. Most of the existing news recommendation methods do not consider the interaction with candidate news in the process of modeling user interest representation. This method makes it challenging to precisely match candidate news to specific user interests. We propose a user interest activation recommendation method that fuses multichannel information—MIAR. It utilizes the word embedding of the user’s historical clicked news and the news title embedding generated by aggregation and interacts with the candidate news, respectively, to better match the candidate news with the user’s interests. Our proposed method contains two frameworks (interactive framework and distributed framework). In the interactive framework, we propose a user multichannel interest modeling framework MIF from the word embedding level of news headlines to capture more semantic cues related to user interests. In the distributed framework, we design a candidate-aware interest activation module TAR from the news embedding representation level obtained by attention aggregation. It uses different candidate news vectors to adjust the user representations learned from the user’s historical reading records. This allows the model to build candidate-guided user representations to accurately match candidate news to parts of user interests that are relevant to the candidate news. Finally, we effectively assign the weights of the two frame scores so that the models can fuse better. Extensive experiments on the MIND news recommendation dataset demonstrate the effectiveness of our method.

HCURec: Hierarchical candidate-aware user modeling for news recommendation

Matching candidate news with user interests is critical for news recommendation. Current studies on news recommendation mainly model a single user interest embedding from the user’s clicked news. One of the major challenges that affects the recommendation is to match candidate news with the user’s multi-field and multi-grained interests. To confront this difficulty, we investigate the multi-grained correlation of user interests and candidate news to obtain their matching features. We propose a hierarchical candidate-aware user modeling framework for news recommendation that matches users’ multi-field and multi-grained interests with candidate news. The framework incorporates candidate news into the modeling of user interests at different levels, namely subcategory-level, category-level and global-level, which learn fine-grained, coarse-grained and overall user interests, respectively. The user interest is finally hierarchically matched at different levels with candidate news to achieve accurate targeting. A collection of experiments were carried out on four real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the existing state-of-the-art methods owing to its highly effective and efficient performance of news recommendation.

MNN4Rec: A relation-aware approach based on multi-view news network for news recommendation

Personalised news recommendation comprises two crucial components: news understanding and user modelling. Previous studies have attempted to model news understanding and user interests using various internal news information and external knowledge graphs (KG). However, they have overlooked the collaborative function of the external KG and the internal information among diverse news and user behaviours, resulting in serious news cold-start problems and poor interpretability of user interests. To address these issues, this article proposes a novel approach called Relation-Aware Approach based on Multi-view News Network for News Recommendation (MNN4Rec). Specifically, MNN4Rec first constructs a Multi-view News Network (MNN), which includes candidate news and user-clicked news, and represents their exclusive multi-view information as heterogeneous nodes. Furthermore, we develop explicit and implicit news relationships and design a special sampling algorithm to search for news co-neighbours. We then use a novel dual-channel graph attention mechanism to obtain the fine-grained news understanding representation. Moreover, we construct explainable user interests by modelling the interaction of user-clicked news through the multi-headed self-attention mechanism in both semantic and relation levels. Finally, we match candidate news understanding with user interests to generate a prediction score for recommendation. Experimental results on Microsoft’s news data set MIND demonstrate that MNN4Rec outperforms existing news-recommendation methods while also mitigating the cold-start problem and enhancing the interpretability of user interests. Our code is available at https://github.com/JiangHaoPG11/MNN4Rec_code .

Studying Representation and Equity-Seeking Candidates in Canadian Elections

Every election, as Canadians sit down at the polls to cast their votes, they ask: how truly representative are our elected officials? And how can we make politicians more representative of the public? As part of the USSRF, I provided important data to answer these questions. I studied how the barriers to elected office differ among equity-seeking groups, including women, BIPOC, and 2SLGBTQ+ people, and how parties can increase their representativeness. This research falls in three streams: data collection on federal candidates, data collection on provincial candidates, and a literature review of the role of voluntary targets to increase diversity and representation in political parties. The research on federal candidates has gathered important data on candidate race, 2SLGBTQ+ identities, and political experience from archived candidate biographies and news sources. The research on provincial candidates uses similar sources to extend analyses based on federal politics to provincial politics. The literature review draws on international examples to explore the impact of voluntary targets for representation within parties. These targets have historically not been a focus of study because the presumption is that voluntary targets will not be effective, but there is evidence that they have been increasingly effective in Canadian federal and provincial parties. By assessing the factors contributing to the representativeness (or under representativeness) of our elected officials, the research builds the groundwork for future work that compares party and voter biases across under-represented groups. The research engages with public debates in popular news media, social media, and non-profit/citizen groups surrounding the representation of women, BIPOC, and 2SLGBTQ+ people. The implications of this research are tangible for people, especially those belonging to marginalized groups, because the way we relate to the systems of power that govern us are crucial in understanding relationships of trust between citizens and the state.

Group-Based Personalized News Recommendation with Long- and Short-Term Fine-Grained Matching

Personalized news recommendation aims to help users find news content they prefer, which has attracted increasing attention recently. There are two core issues in news recommendation: learning news representation and matching candidate news with user interests. In this context, “candidate” indicates potential for interest. Due to the superior ability to understand natural language demonstrated by Pretrained Language Models (PLMs), recent works utilize PLMs (e.g., BERT) to strengthen news modeling, obtaining more accurate user interest matching and achieving notable improvement in news recommendation. However, the existing PLM-based methods are usually incapable of fully exploring the fine-grained (i.e., word-level) relatedness between user behaviors and candidate news due to the heavy computational cost brought by PLMs. In this article, we propose a group-based personalized news recommendation method with long- and short-term matching mechanisms between users and candidate news based on PLMs to learn fine-grained matching efficiently and effectively. In our approach, we design to group user historical clicked news into chunks with quite shorter news sequences according to their clicked timestamps, which could alleviate the computation issues of PLMs. PLMs are applied in each group jointly with the candidate news to capture their word-level interaction, and global group-level matching is learned across different groups. In addition, the group-based mechanism could be naturally adapted for long- and short-term user representation learning, in which we build users’ long preferences from the representations of all groups and treat the last group as short interests, respectively. Finally, we employ a gate network to dynamically unify the group-level, long- and short-term representations, yielding comprehensive user-news matching effectively. Extensive experiments are conducted on two real-world datasets. The results show that our proposed method achieves superior performance in news recommendations.

Aspect-Driven User Preference and News Representation Learning for News Recommendation

Intelligent human-device interfaces play key roles in fully automated vehicles (FAVs), ensuring smooth interactions and improving the driving experience. Listening to news is a popular method of relaxing during a journey; as a result, travelers require automatic recommendations of preferred news programs. Most existing news recommender systems usually learn topic-level representations of users and news for recommendations while neglecting to learn more informative aspect-level features, resulting in limited recommendation performance. To bridge this significant gap, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preferences and news representation learning. In ANRS, a news aspect-level encoder and a user aspect-level encoder are devised to learn the fine-grained aspect-level representations of users’ preferences and news characteristics respectively. These representations are subsequently fed into a click predictor to predict the probability of a given user clicking on the candidate news item. Extensive experiments demonstrate the superiority of our method over state-of-the-art baseline methods.

CupMar: A deep learning model for personalized news recommendation based on contextual user-profile and multi-aspect article representation

In modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user’s historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user’s browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach.

Graph Neural News Recommendation with User Existing and Potential Interest Modeling

Personalized news recommendations can alleviate the information overload problem. To enable personalized recommendation, one critical step is to learn a comprehensive user representation to model her/his interests. Many existing works learn user representations from the historical clicked news articles, which reflect their existing interests. However, these approaches ignore users’ potential interests and pay less attention to news that may interest the users in the future. To address this problem, we propose a novel G raph neural news R ecommendation model with user E xisting and P otential interest modeling, named GREP. Different from existing works, GREP introduces three modules to jointly model users’ existing and potential interests: (1) Existing Interest Encoding module mines user historical clicked news and applies the multi-head self-attention mechanism to capture the relatedness among the news; (2) Potential Interest Encoding module leverages the graph neural network to explore the user potential interests on the knowledge graph; and (3) Bi-directional Interaction module dynamically builds a news-entity bipartite graph to further enrich two interest representations. Finally, GREP combines the existing and potential interest representations to represent the user and leverages a prediction layer to estimate the clicking probability of the candidate news. Experiments on two real-world large-scale datasets demonstrate the state-of-the-art performance of GREP.

Election Guidelines: Know the Rules

Election Guidelines: Know the Rules

Adapted transformer network for news recommendation

Online news recommendation aims to provide personalized news for users according to their interests. Existing methods usually learn user preference from their historical reading records in a static and independent way, which ignore the dynamic interaction with the target candidate news. In fact, it is important to fully capture the semantic interaction between user?s historical news and candidate news since the user?s interests would be different in terms of different candidate news. In this paper, we propose a novel news recommendation model with an adapted transformer network. There are three parts in our approach, i.e., a news encoder to learn the semantic features of news, a user encoder to learn the initial representations of users, an adapted transformer module to learn the deep interaction between users and candidate news. The core is that we effectively integrate the historical clicked news and the candidate news into the transformer framework to capture their inherent relatedness. Besides, an additive attention layer is proposed to learn different informativeness of words since different words are differently useful for news or users? representation. We conduct extensive experiments on a real-world dataset from MSN news and the results indicates the effectiveness of our proposed approach.

Neural news recommendation with negative feedback

News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually rely on the implicit feedback of users like news clicks to model user interest. However, news click may not necessarily reflect user interests because users may click a news due to the attraction of its title but feel disappointed at its content. The dwell time of news reading is an important clue for user interest modeling, since short reading dwell time usually indicates low and even negative interest. Thus, incorporating the negative feedback inferred from the dwell time of news reading can improve the quality of user modeling. In this paper, we propose a neural news recommendation approach which can incorporate the implicit negative user feedback. We propose to distinguish positive and negative news clicks according to their reading dwell time, and respectively learn user representations from positive and negative news clicks via a combination of Transformer and additive attention network. In addition, we propose to compute a positive click score and a negative click score based on the relevance between candidate news representations and the user representations learned from the positive and negative news clicks. The final click score is a combination of positive and negative click scores. Besides, we propose an interactive news modeling method to consider the relatedness between title and body in news modeling. Extensive experiments on real-world dataset validate that our approach can achieve more accurate user interest modeling for news recommendation.

Research on fused sorting based on logical regression in news recommendation system

A fusion sorting model based on logical regression is used to solve the problem of information overload in the news recommendation system and meet the personalized needs of users. First, the fusion sorting model is trained based on the article data and user data, and the original candidate news set is obtained by multi-channel recall method. Then the original candidate set is filtered by the fusion sorting model, and the recommendation list is generated. Finally, the experiment is carried out based on the Wuli News Recommendation System. Under the evaluation indexes of precision, recall and ILS, the fusion sorting model adopted significantly improves the recommendation ability of the system, and achieves the goal of making multiple single recommendation strategies make up for each other’s shortcomings.

Trump, Twitter, and news media responsiveness: A media systems approach

How populists engage with media of various types, and are treated by those media, are questions of international interest. In the United States, Donald Trump stands out for both his populism-inflected campaign style and his success at attracting media attention. This article examines how interactions between candidate communications, social media, partisan media, and news media combined to shape attention to Trump, Clinton, Cruz, and Sanders during the 2015–2016 American presidential primary elections. We identify six major components of the American media system and measure candidates’ efforts to gain attention from them. Our results demonstrate that social media activity, in the form of retweets of candidate posts, provided a significant boost to news media coverage of Trump, but no comparable boost for other candidates. Furthermore, Trump tweeted more at times when he had recently garnered less of a relative advantage in news attention, suggesting he strategically used Twitter to trigger coverage.