News Recommendation Algorithms Research Articles

The investigation and application of Muti-Armed algorithms in recommendation systems

In the contemporary Internet recommendation systems in various fields, muti-armed algorithms will show high accuracy and practicability. Internet users can always get abundant positive feedback through the recommendation system via utilizing these algorithms. In this paper, there are three most typical muti-armed algorithms as examples are provided. Explore-Then-Commit (ETC) algorithm is the first to mentioned. The physical meaning of this algorithm is that in the exploration stage, the action will be selected in a certain order, and after a certain number of rounds, the action with the largest average reward will be directly selected. Moreover, Upper Confidence Bond (UCB) algorithm is also a type of pivotal tool. The main function of UCB algorithm is to make the selection by calculating the upper bound of each arm confidence interval instead of the expected reward of the slot machine. Thus, it is an optimistic algorithm. First, select each arm in random order, then calculate the value of the upper bound of each arm confidence interval, and finally, select the arm with the largest value. The last mentioned Thompson Sampling (TS) Algorithm take Bayesian optimization as the theoretical basis. This algorithm takes out the candidate parameters, generates a random number, and selects the maximum value for input. This paper also introduced two fields related to the topic of the application of muti-armed algorithms in the recommendation systems in modern fields. News recommendation algorithms and personalized recommendation systems can be more comprehensive and representative to illustrate the practicality of these algorithms. Therefore, muti-armed algorithms reflect the importance in the recommendation fields via the balance of exploration and exploitation.

Towards a News Recommendation System to increase Reader Engagement through Newsletter Content Personalization

In the big data era, recommendation systems (RS) play a pivotal role to overcome information overload. In the digital landscape publishers need to optimize their editorial strategies to increase reader engagement and digital revenue. Newsletters emerged as an important conversion channel to engage readers as they provide a personalized experience by building habits. However, the lack of human resources and the need for more content assertiveness per reader lead publishers to search for an advanced analytics solution. We address this problem by proposing a research agenda on news recommendation algorithms inspired in the table d’hôte approach and the concept of ‘personalized diversity’. Thus, the reader receives a personalized newsletter where he can discover informative and surprising content. The goal is to offer a self-contained package that retains readers, increases loyalty and consequently, the propensity to subscribe. A live controlled experiment with readers from the Portuguese newspaper Público was performed and a new approach is proposed. We study the effects of content recommendations on the behavior of newsletter subscribers. Findings reveal that serendipitous content tends to increase reader engagement. Finally, we propose a table d’hôte approach and new challenges are identified for future research.

Research of News Recommendation Algorithms based on Deep Learning

Deep learning is the key development trend of news recommendation technology, which has been thoroughly studied by most researchers. In today's social, news recommendation has become a very essential way for people to acquire news. The fundamental idea and procedure of news modeling are covered in this paper's main body, and one of our research interests is how to leverage convolutional neural networks to create news recommendation technology. We also looked at the news recommendation evaluation index, which looks at things like satisfaction, accuracy, diversity, and innovation. Additionally, this paper analyses numerous traditional algorithms and contrast the benefits and drawbacks of each. We also outlined a number of the challenges that the current study has faced. To contribute to this research, we looked into the expected future evolution of news recommendation technology.

Personalized News Recommendation Algorithm for Event Network

In order to improve the level of personalized news recommendation efficiently and accurately, an event network-oriented personalized news recommendation algorithm is proposed. First, the event network is used to analyze, predict users’ interests and preferences, and actively push information content to meet users’ personalized needs, so as to build a personalized news recommendation model. Under the mobile Internet technology, combined with the characteristics of the Internet, through the position and title similarity of sentences in the document and other features, the combined features are formed to calculate the sentence weight. Finally, the sentences are extracted according to the weight ranking to generate the news summary, so as to realize the research on personalized news recommendation algorithm for event network. The experimental results show that the proposed algorithm has high recall and coverage, short time, good recommendation effect, and strong recommendation performance.

Research on the Impact of Embedded Intelligent Robots on English News Dissemination

With the rapid development of the Internet, although users can obtain information conveniently and quickly, they still cannot accurately obtain the target information they need in a short period of time in the face of massive information content. The use of embedded intelligent robots in the English news industry promotes the “Internet +” strategy, the goal of which is to connect everything. This article is aimed at studying the impact of embedded intelligent robots on the dissemination of English news. This article first analyzes embedded intelligent robots and news dissemination and then analyzes the collaborative filtering algorithm and text analysis technology of the recommendation system. Then, this article discusses the impact of embedded intelligent robot writing on English news dissemination and news media in the context of artificial intelligence. Direction of Development. Finally, in the experimental part, this paper implements a news recommendation system based on item recommendation algorithm and text analysis technology. Experiments have proved that the news recommendation algorithm of this system has a high hit rate and has great practical application value. This article sets each recommended page to 15 news, then randomly selects 5 users, and counts the number of news they visit, and the average number of news visited is 9.22. The results show that the embedded robot is helpful for English news dissemination, increasing the dissemination breadth by 20% and the dissemination accuracy by 30%.

Knowledge Graph and GNN-Based News Recommendation Algorithm With Edge Computing Support

The current news information from different media websites has posed a serious problem, i.e., it is very difficult to obtain the satisfactory news contents from the measured data information. There have been some researches on news recommendation to improve the experience of users. In spite of this, they always need the further improvement because the news information has showed the explosive increasing way. Therefore, this paper studies knowledge graph and graph neural network (GNN) based news recommendation algorithm with edge computing consideration. At first, the knowledge graph is used for the knowledge extraction. Then, GNN is used to train the extracted features to complete the news recommendation algorithm. Finally, the edge computing is used to offload the high volumes of traffic to the edge server for the news recommendation computation. Compared with two baselines, the proposed algorithm is more efficient, increasing accuracy rate by 2.73% and 9.94% respectively, and decreasing response time by 84.27% and 87.58 respectively.

News Recommendation Algorithms and Attitudes toward Democracy: Evidence from an App Patient-Preferred Trial in Italy

News Recommendation Algorithms and Attitudes toward Democracy: Evidence from an App Patient-Preferred Trial in Italy

A Hybrid News Recommendation Algorithm Based On K-means Clustering and Collaborative Filtering

In the era of paper media, the information channels and the information content were integrated. With the birth of the Internet, they tended to be separated while the information channels continued to expand, which brought a massive amount of news information to process. Therefore, it’s essential for us to adopt new methods and new models to deal with all the information. This paper gives a brief overview of news recommendation technology, and proposes a hybrid news recommendation algorithm, which combines content-based recommendation algorithm and collaborative filtering, using TF-IDF method and K-means clustering technology to extract and process the features of news content, meanwhile, this paper applies SVD technology to solving the matrix sparse problem in the traditional collaborative filtering algorithm. Moreover, news popularity is taken into consideration in this paper then it combines the candidate recommendation results of each approach. At last, this algorithm achieves a better result compared to traditional recommendation algorithm’s result.

News Recommendation Systems in the Era of Information Overload

The internet has reconstructed information boundaries in the modern world, and along with mobile internet has become the most important source of information for the public. Simultaneously, the internet has brought humanity into an era of information overload. In response to this information overload, recommendation systems backed by big data and smart algorithms have become highly popular on information platforms on the internet. There have already been many studies that attempted to improve and upgrade recommendation algorithms from a technical perspective, but the field lacks a comprehensive reflection on news recommendation algorithms. In our study, we summarize the principles and characteristics of current news recommendation algorithms and discuss “unexpected consequences” that might arise from these algorithms. In particular, technical bottlenecks include cold starts and data sparsity, and moral bottlenecks are presented in the form of information imbalance and manipulation. These problems may cause new recommendation systems to become a “warped mirror”.

How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance

Although algorithms have been widely used to deliver useful applications and services, it is unclear how users actually experience and interact with algorithm-driven services. This ambiguity is even more troubling in news recommendation algorithms, where thorny issues are complicated. This study investigates the user experience and usability of algorithms by focusing on users' cognitive process to understand how qualities/features are received and transformed into experiences and interaction. This work examines how users perceive and feel about issues in news recommendations and how they interact and engage with algorithm-recommended news. It proposes an algorithm experience model of news recommendation integrating the heuristic process of cognitive, affective, and behavioral factors. The underlying algorithm can affect in different ways the user's perception and trust of the system. The heuristic affect occurs when users' subjective feelings about transparency and accuracy act as a mental shortcut: users considered transparent and accurate systems convenient and useful. The mediating role of trust suggests that establishing algorithmic trust between users and NRS could enhance algorithm performance. The model illustrates the users' cognitive processes of perceptual judgment as well as the motivation behind user behaviors. The results highlight a link between news recommendation systems and user interaction, providing a clearer conceptualization of user-centered development and the evaluation of algorithm-based services.

Session-aware news recommendations using random walks on time-evolving heterogeneous information networks

Traditional news media Web sites usually provide generic recommendations that are not personalized to the preferences of their users. Typically, news recommendation algorithms mainly rely on the long-term preferences of users and do not adjust their model to the continuous stream of short-lived incoming stories to capture short-term intentions revealed by users’ sessions. In this paper, we therefore study the problem of session-aware recommendations by running random walks on dynamic heterogeneous graphs. Concretely, we construct a heterogeneous information network consisting of users, news articles, news categories, locations and sessions. By using different (1) sliding time window sizes, (2) sub-graphs for model learning, (3) sequential article weighting strategies and (4) more diversified random walks, we perform recommendations in a second step. Our algorithm proposal is evaluated on three real-life data sets, and we demonstrate that our method outperforms state-of-the-art methods by delivering more accurate and diversified recommendations.

Personalizing the war: Perspectives for the adoption of news recommendation algorithms in the media coverage of the conflict in Eastern Ukraine

The use of algorithmically tailored individual news feeds is increasingly viewed as an important strategy for accommodating consumers’ information needs by legacy media. However, growing personalization of news distribution also raises normative concerns about the societal function of legacy media, in particular when dealing with personalization of traumatic and polarizing content. To extend the discussion of these concerns beyond the current focus on the role of news personalization in Western democracies, this article offers a conceptual assessment of perspectives for adopting personalization for conflict coverage in Ukraine and Russia, where media systems enjoy a lesser degree of press freedom. Using the coverage of the conflict in Eastern Ukraine as a case study, the article offers a conceptual framework for assessing the impact of personalization on the distribution of conflict-related news in a non-Western context.

基于主题模型的热点新闻推荐算法研究

基于主题模型的热点新闻推荐算法研究

Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem.

The deployment of various forms of AI, most notably of machine learning algorithms, radically transforms many domains of social life. In this paper we focus on the news industry, where different algorithms are used to customize news offerings to increasingly specific audience preferences. While this personalization of news enables media organizations to be more receptive to their audience, it can be questioned whether current deployments of algorithmic news recommenders (ANR) live up to their emancipatory promise. Like in various other domains, people have little knowledge of what personal data is used and how such algorithmic curation comes about, let alone that they have any concrete ways to influence these data-driven processes. Instead of going down the intricate avenue of trying to make ANR more transparent, we explore in this article ways to give people more influence over the information news recommendation algorithms provide by thinking about and enabling possibilities to express voice After differentiating four ideal typical modalities of expressing voice (alternation, awareness, adjustment and obfuscation) which are illustrated with currently existing empirical examples, we present and argue for algorithmic recommender personae as a way for people to take more control over the algorithms that curate people's news provision.This article is part of a theme issue 'Governing artificial intelligence: ethical, legal, and technical opportunities and challenges'.

Time-ordered collaborative filtering for news recommendation

Faced with hundreds of thousands of news articles in the news websites, it is difficult for users to find the news articles they are interested in. Therefore, various news recommender systems were built. In the news recommendation, these news articles read by a user is typically in the form of a time sequence. However, traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors. Therefore, the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read. To solve this problem, this paper proposes a time-ordered collaborative filtering recommendation algorithm (TOCF), which takes the time sequence characteristic of user behaviors into account. Besides, a new method to compute the similarity among different users, named time-dependent similarity, is proposed. To demonstrate the efficiency of our solution, extensive experiments are conducted along with detailed performance analysis.

Personalized Financial News Recommendation Algorithm Based on Ontology

Abstract To deal with the challenge of information overload, in this paper, we propose a financial news recommendation algorithm which help users find the articles that are interesting to read. To settle the ambiguity problem, a new presented OF-IDF method is employed to represent the unstructured text data in the form of key concepts, synonyms and synsets which are all stored in the domain ontology. For users, the recommendation algorithm build the profiles based on their behaviors to detect the genuine interests and predict current interests automatically and in real time by applying the thinking of relevance feedback. Finally, the experiment conducted on a financial news dataset demonstrates that the proposed algorithm significantly outperforms the performance of a traditional recommender.

A Method based on One-class SVM for News Recommendation

In order to provide intelligent recommendation and personalized service for users on news website, this paper presents a method based on One-Class SVM for news recommendation algorithm. By analyzing the news webpages and user's browsing history, and by building One-Class SVM model, this algorithm can recommend news for user. The main work of this paper is to study this news recommendation algorithm and to show its experimental results under Dot NET platform. First, this algorithm preprocesses the webpages from Sogou Labs, each of which has its inherent domain and builds One-Class SVM models for these domains. Next, it builds user interest models for each user by analyzing their browsing histories. Then it finds the user's most interested domains by comparing each domain models and user interest model. Finally, it utilizes the webpages of these domains and user's browsing history to build One-Class SVM model to calculate the most relevant webpages to user interest, and recommends these webpages to user. This algorithm takes the lead in calculate the similarity between user interests and webpages using One-Class SVM model and apply hierarchical model to make the results more accurate. From the results, we can find that this algorithm is running pretty well.