Modeling and Validating a News Recommender Algorithm in a Mainstream Medium-Sized News Organization: An Experimental Approach

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%.

Going Dark

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

The explosion of internet news data has led to frequent information overload, which in turn has caused the dual problems of low recommendation quality and poor user experience. The research combines machine learning with image matching algorithms to provide users with interesting news content. Firstly, the news recommendation algorithm based on multi-task learning (MTL) constructs the input sequence through the users click history, maps the inputs of different tasks to the shared space and extracts text features. The image matching algorithm is adopted to record users historical preferences and capture the changing trends of interests. Experimental data show that the accuracy rate of the content-based recommendation (CB) algorithm reaches 78.27%. The collaborative filtering (CF) recommendation algorithm reached 87.97%. The recommendation accuracy of the joint recommendation algorithm all exceeds 90%. Moreover, the accuracy of the joint recommendation algorithm significantly exceeds that of the two traditional recommendation methods, CB and CF.

With the popularization of e-readers, electronic reading rooms, digital libraries, and other new ways of reading in libraries and society, libraries have also entered a new stage of development because of “low-carbon” construction. The low-carbon development of intelligent libraries reduces the application of traditional literature carriers, increases the popularity and application of modern equipment, makes the replacement of paper materials, and reduces its own energy consumption. To achieve personalized recommendations in the lending system, this paper, inspired by biomechanical concepts, constructs a tree intelligent recommendation system via a collaborative filtering algorithm. This system functions like a neural network in a biological system, processing and analyzing data to make informed decisions. By verifying the system with actual borrowing data of students, it proves effective, much like how a biomechanical adaptation is tested and validated in nature. This approach offers a valuable reference for intelligent book management in universities, aligning library operations with the principles of efficient resource utilization and adaptation seen in the biomechanical world. book management in universities. In addition to these advancements, integrating biomechanics into the design and operation of smart libraries can enhance user experience and engagement. Understanding the biomechanics of reading—such as posture, hand movements, and eye tracking—can inform the development of ergonomic reading spaces and devices. For instance, optimizing seating arrangements and reading environments based on biomechanical principles can reduce physical strain and improve comfort for users. Moreover, incorporating biomechanical feedback into the recommendation system could personalize user interactions further. By analyzing how different users engage with reading materials—considering factors like reading speed, preferred formats, and physical interactions with devices—libraries can refine their recommendation algorithms. This approach not only enhances the effectiveness of title recommendations but also promotes a healthier reading experience, aligning with the low-carbon goals of reducing physical strain and energy consumption associated with inefficient reading practices.

A significant increase in news sources has been brought about by the growth of the online and the social web. The easy access to numerous news sources results in a flood of information that is frequently contradictory and confusing. When news circulates on the social web, it can be challenging to distinguish between legitimate and false reports. False news can be used for a variety of purposes, including as political influence, financial gain, and strict convictions to solve these problems, a new news aggregator was developed to help users distinguish between authentic, fake, and copy content. News from multiple news sources is reviewed, approved, and presented to the client in the suggested system. The news aggregator also suggests important news to the customer by anticipating consumer preference via a recommendation algorithm. It has been demonstrated that news recommendation systems can naturally handle lengthy articles and distinguish between them for readers who are taking into account established models.

The recommender systems can gain the needs and interests of users by analyzing the user history data and then help the users making decisions on appropriate choices in E-commerce. However, with the increasing of data volume and the popularization of information network, the participation of users in E-commerce activities is growing deeply. How to analyze the user preferences and make a user-centered efficient recommendation is an urgent problem to be further researched. In this paper, we first propose the user-centered recommendation based on dynamic graph model to express the user preferences and gain the user preference vectors for recommendation. Then, after gaining the user preferences vectors, we propose the user clustering algorithm using US-ELM to cluster the users into different clusters. Last, we provide two recommendation algorithms, which can present top-k recommendation, respectively the group recommendation and personal recommendation. With the extensive experiments, our recommendation algorithms can effectively express the user preferences and reach a good performance.

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

Introduction: Recommendation systems (RS) are pivotal in enhancing personalized user experiences, particularly in the educational domain, where tailored book recommendation systems (BRS) support individualized learning. Despite their importance, selecting an optimal recommendation algorithm remains challenging, especially in scenarios involving large and sparse datasets. Objectives: This study aims to evaluate the performance of four recommendation algorithms—Random Baseline, Popular, User-Based Collaborative Filtering (UBCF), and Singular Value Decomposition (SVD)—to identify their strengths and tradeoffs regarding accuracy and computational efficiency. The goal is to provide actionable insights for selecting appropriate algorithms for educational platforms and similar applications. Methods: The evaluation employed a dataset containing over 10,000 books, 53,424 users, and 981,756 ratings. The algorithms were assessed using Root Mean Square Error (RMSE) to measure predictive accuracy and training time to indicate computational efficiency. The analysis addressed challenges such as data sparsity and rating biases. Results: The results demonstrated that SVD achieved the highest accuracy (RMSE: 0.950), effectively uncovering latent relationships in sparse datasets. UBCF, with a slightly lower accuracy (RMSE: 1.020), offered a balance between accuracy and computational efficiency, making it suitable for real-time applications. Conversely, simpler algorithms like Random Baseline and Popular exhibited faster training times but significantly lower predictive accuracy, highlighting the limitations of non-personalized methods. Conclusions: This study underscores the tradeoffs between accuracy and efficiency among recommendation algorithms. While SVD is ideal for accuracy-driven applications, UBCF provides a practical alternative for scenarios requiring computational efficiency. The findings have substantial implications for educational platforms and e-commerce, where personalized recommendations enhance user satisfaction and engagement. Future research should focus on integrating deep learning models and expanding evaluation criteria, including user satisfaction and diversity, to further improve the performance of recommendation systems.

Since its debut in 2006, Twitter has established itself as a unique platform for news sharing with features, such as “@mentions” and “retweets,” popular among journalists and non-journalists alike. This study examined how journalists share news messages on Twitter. Specifically, the authors investigated whether there is any difference in linking and attribution behavior between journalists for online and prestige traditional news organizations. A content analysis was conducted of a sample of 1175 tweets from 40 journalists from eight news organizations (four prestige traditional and four online) posted during the US government shutdown from October 1 to 16, 2013. This study supports previous research that journalists are more likely to promote content from their own news sites than from other sources. The study also extends agenda-setting theory to journalists’ use of Twitter. When they were not linking to their own news organizations’ work, journalists in the sample were more likely to link to content on traditional news sites than on online news sites. Also, journalists were more likely to interact through retweets and @mentions with other journalists than with public officials or other citizens.

A location-based orientation-aware recommender system using IoT smart devices and Social Networks

Recommender systems are becoming an essential part of smart services. When building a news recommender system, we should consider special features different from other recommender systems. Hot news topics are changing every moment, thus it is important to recommend right news at the right time. This paper aims to propose a new model, based on deep neural network, to analyse user preference for news recommender system. The model extracts interest keywords to characterize the user preference from the set of news articles read by that particular user in the past. The model utilizes characterizing features for news recommendation, and applies those to the keyword classification for user preference. For the keyword classification, we use deep neural network for online preference analysis, because adaptive learning is necessary to track changes of hot topics sensitively. The usefulness of our model is validated through experiments. In addition, the accuracy and diversity of the recommendation results is also analysed.

Recommender systems are designed to help users quickly access large volumes of information according to their profiles. Most previous works in recommender systems have put their emphasis on the accuracy of finding the most similar items according to a user’s profile, while often ignoring other aspects that may affect users’ experiences in practice, e.g., the novelty and diversity issues within a recommendation list. In this paper, we focus on utilizing taxonomic knowledge extracted from an online encyclopedia to boost a content-based personalized news recommender system without much human involvement. Given a recommendation list, we improve a user’s satisfaction by introducing the taxonomy based novelty and diversity metrics to include novel, but potentially related items into the list, and filter out redundant ones. The experimental results show that the coarse grained knowledge resources can help a content-based news recommender system provides accurate as well as user-oriented recommendations.

A personalised news recommendation system extracts news set from multiple press releases and presents the recommended news to the user. In an effort to build a better recommender system with high accuracy, this paper proposes a personalised news recommendation framework named Hybrid Personalised NEws Recommendation (HYPNER). HYPNER combines both collaborative filtering-based and content-based filtering methods. The proposed framework aims at improving the accuracy of news recommendation by resolving the issues of scalability due to large news corpus, enriching the user's profile, representing the exact properties and characteristics of news items, and recommending diverse set of news items. Validation experiments showed that HYPNER achieved 81.56% improvement in F1 -score and 5.33% in diversity as compared to an existing recommender system, SCENE.

News is one of the earliest application domains of recommender systems, and recommending items from a virtually endless stream of news is still a relevant problem today. News recommendation is different from other application domains in a variety of ways, e.g., because new items constantly become available for recommendation. To be effective, news recommenders therefore have to continuously consider the latest items in the incoming stream of news in their recommendation models. However, today's public software libraries for algorithm benchmarking mostly do not consider these particularities of the domain. As a result, authors often rely on proprietary protocols, which hampers the comparability of the obtained results. In this paper, we present StreamingRec as a framework for evaluating streaming-based news recommenders in a replicable way. The open-source framework implements a replay-based evaluation protocol that allows algorithms to update the underlying models in real-time when new events are recorded and new articles are available for recommendation. Furthermore, a variety of baseline algorithms for session-based recommendation are part of StreamingRec. For these, we also report a number of performance results for two datasets, which confirm the importance of immediate model updates.