CONCERT: A Concept-Centric Web News Recommendation System

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.

Recent rises in political polarization across the globe are often ascribed to algorithmic content filtering on social media, news platforms, or search engines. The widespread usage of news recommendation systems (NRS) is theorized to drive users in homogenous information environments and, thereby, drive affective, ideological, and perceived polarization. To test this assumption, we conducted an online experiment ( n = 750) with running algorithms that enriches content-based NRS with negative or neutral sentiment. Our experiment finds only limited evidence for polarization effects of content-based NRS. Nevertheless, the time spent with an NRS and its recommended articles seems to play a crucial role as a moderator of polarization. The longer participants were using an NRS enriched with negative sentiment, the more they got affectively polarized, whereas participants using an NRS incorporating balanced sentiment ideologically depolarized over time. Implications for future research are discussed.

Using complex language models has been a common strategy as personalized news recommendation systems are adopted by online news sites more and more. Before the epoch of GPT-3, news recommendation systems underwent a progression from rule-based and collaborative filtering approaches in the pre-2010s, through the integration of neural networks in the 2010s, to the emergence of earlier iterations of large language models like GPT-2 in 2019. Pre-trained language models have ushered in a new era of recommendation paradigms, thanks to the emergence of huge language models like GPT-3 and T-5. With its easy-to-use interface, ChatGPT is becoming more and more popular for text-based jobs. Focusing on news provider fairness, individualized news recommendations, and fake news identification, this study starts an inquiry into ChatGPT's efficacy in news recommendations. We acknowledge that ChatGPT's output sensitivity to input phrasing is a limitation, and our goal is to investigate these limitations from each angle. We also investigate whether certain prompt formats can help to alleviate these constraints or if more research is needed. To go beyond set assessments, we create a webpage where ChatGPT's performance on the examined activities and prompts is tracked once a week. Through the use of big language models, this work seeks to improve news recommendation performance and stimulate more research in this area.

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

In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candidate news, and finally recommend the news with high scores to users. However, existing user models usually only consider users’ long-term interests and ignore users’ recent interests, which affects users’ usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network to capture users’ short-term interests and combines users’ short-term interests and long-term interests to characterize users. While existing models often only use the user’s browsing history and ignore the variability of different users’ interest in the same news, we introduce additional user’s ID information and apply the personalized attention mechanism for user representation. Thus, we achieve a more accurate user representation. We also consider the risk of compromising user privacy if the user model training is placed on the server side. To solve this problem, we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side. We further employ secure multi-party computation to request news representations from the server side, which protects privacy to some extent. Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics. Compared with the current state-of-the-art federated news recommendation models, our model has increased by 0.54% in AUC, 1.97% in MRR, 2.59% in nDCG@5%, and 1.89% in nDCG@10. At the same time, because we use a federated learning framework, compared with other centralized news recommendation methods, we achieve privacy protection for users.

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.

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.

In order to overcome the problems of poor operation function and low recommendation accuracy of the traditional high-quality curriculum resource recommendation system, this paper proposes and designs a new high-quality Curriculum Resource Recommendation System for educational technology specialty. With the support of the system framework, the system hardware module is designed, mainly including user registration and login module and high-quality course resource recommendation module, so as to realize the recommendation function of the system. The system software part constructs the user’s high-quality curriculum resources mining classification tree according to the user’s preference attribute value of high-quality curriculum resources, and obtains the recommended basic data. On this basis, the user’s preference for course resources is calculated, so as to complete the recommendation of high-quality course resources. The experimental results show that compared with the traditional recommendation system, the functions of the designed system have passed the test and have high recommendation accuracy.

News recommender systems rely on rich textual information from news articles to generate user-specific recommendations. This reliance may expose these systems to potential vulnerabilities through textual attacks. To explore this vulnerability, we propose LANCE, a LArge language model-based News Content rEwriting framework, designed to influence news rankings and highlight the unintended promotion of manipulated news. LANCE consists of two key components: an explorer and a reflector. The explorer first generates rewritten news using diverse prompts, incorporating different writing styles, sentiments, and personas. We then collect these rewrites, evaluate their ranking impact within news recommender systems, and apply a filtering mechanism to retain effective rewrites. Next, the reflector fine-tunes an open-source LLM using the successful rewrites, enhancing its ability to generate more effective textual attacks. Experimental results demonstrate the effectiveness of LANCE in manipulating rankings within news recommender systems. Unlike attacks in other recomendation domains, negative and neutral rewrites consistently outperform positive ones, revealing a unique vulnerability specific to news recommendation. Once trained, LANCE successfully attacks unseen news recommender systems (i.e., those for which LANCE received no information during training), highlighting its generalization ability and exposing shared vulnerabilities across different systems. Our work underscores the urgent need for research on textual attacks and paves the way for future studies on defense strategies.

In news recommendation system, the content-based method face the lack of diversity that recommended results will only contain the news similar with user's former read. The hybrid method face cold-start problem that the candidate news must wait enough click to recommend. We propose a fused method which is capable of handling the diversity problem of content-based and cold-start problem of hybrid approach. We use content-based method to extract user's existed interest firstly. Find out similar user set and predict target user's potential interest by collaborative filtering secondly. Fuse existed interest and potential interest to obtain the final user profile the third. Generate the recommended results after comparison the similarity of candidate news and final user profile lastly. This approach results in an offline news set and compared Diversity and F-score with other recommendation method. It decreases the lack of diversity compare with content-based approach and avoids the cold-start problem compare with hybrid method.

News recommendation systems must simultaneously address multiple challenges, including dynamic user interest modeling, nonlinear popularity patterns, and diversity recommendation in cold-start scenarios. We present a Popularity-Aware Dynamic Multi-Perspective Fusion Network (PAD-MPFN) that innovatively integrates three key components: adaptive subspace projection for multi-source interest fusion, logarithmic time-decay factors for popularity bias mitigation, and dynamic gating mechanisms for personalized recommendation weighting. The framework uniquely combines sequential behavior analysis, social graph propagation, and temporal popularity modeling through a unified architecture. Experimental results on the MIND dataset, an open-source version of MSN News, demonstrate that PAD-MPFN outperforms existing methods in terms of recommendation performance and cold-start scenarios while effectively alleviating information overload. This study offers a new solution for dynamic interest modeling and diverse recommendation.

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.

Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites most of users are anonymous and the only available data is sequences of items in anonymous sessions. Due to this, typical collaborative filtering methods, which are highly applied in many applications, are not effective in news recommendations. In this context, session-based recommenders are able to recommend next items given the sequence of previous items in the active session. Neighborhood-based session-based recommenders has been shown to be highly effective compared to more sophisticated approaches. In this study we propose scenarios to make these session-based recommender systems diversity-aware and to address the filter bubble phenomenon. The filter bubble phenomenon is a common concern in news recommendation systems and it occurs when the system narrows the information and deprives users of diverse information. The results of applying the proposed scenarios show that these diversification scenarios improve the diversity measures in these session-based recommender systems based on four news datasets.

Numerous recommendation approaches are in use today. However, comparing their effectiveness is a challenging task because evaluation results are rarely reproducible. In this article, we examine the challenge of reproducibility in recommender-system research. We conduct experiments using Plista's news recommender system, and Docear's research-paper recommender system. The experiments show that there are large discrepancies in the effectiveness of identical recommendation approaches in only slightly different scenarios, as well as large discrepancies for slightly different approaches in identical scenarios. For example, in one news-recommendation scenario, the performance of a content-based filtering approach was twice as high as the second-best approach, while in another scenario the same content-based filtering approach was the worst performing approach. We found several determinants that may contribute to the large discrepancies observed in recommendation effectiveness. Determinants we examined include user characteristics (gender and age), datasets, weighting schemes, the time at which recommendations were shown, and user-model size. Some of the determinants have interdependencies. For instance, the optimal size of an algorithms' user model depended on users' age. Since minor variations in approaches and scenarios can lead to significant changes in a recommendation approach's performance, ensuring reproducibility of experimental results is difficult. We discuss these findings and conclude that to ensure reproducibility, the recommender-system community needs to (1) survey other research fields and learn from them, (2) find a common understanding of reproducibility, (3) identify and understand the determinants that affect reproducibility, (4) conduct more comprehensive experiments, (5) modernize publication practices, (6) foster the development and use of recommendation frameworks, and (7) establish best-practice guidelines for recommender-systems research.

This study aims to explore the impact of personalized news recommendation systems based on big data and artificial intelligence algorithms on audience cognition and behavior. With the rapid development of information technology, news recommendation systems have been widely used in the media industry, but their impact on audiences has not been fully studied. This paper adopts a mixed research method, combining questionnaire surveys, in-depth interviews and experimental research, and conducts a three-month follow-up survey on 500 news app users. The results show that the personalized recommendation system significantly increases users' news reading volume and retention time, but also leads to the intensification of information cocoon effect and cognitive bias. In addition, the study finds that the transparency of the recommendation algorithm, the user's media literacy level and the diversity of news sources are key factors affecting audience cognition and behavior. Based on the research findings, this paper proposes suggestions for optimizing the news recommendation system, improving users' media literacy and promoting information diversification, in order to minimize the negative impact of personalized recommendations while improving user experience. This study has important theoretical and practical significance for understanding the characteristics of audience behavior in the intelligent media environment, improving the design of news recommendation systems and formulating relevant policies.