Design of news sentiment classification and recommendation system based on multi-model fusion and text similarity

Concerns about selective exposure and filter bubbles in the digital news environment trigger questions regarding how news recommender systems can become more citizen-oriented and facilitate – rather than limit – normative aims of journalism. Accordingly, this chapter presents building blocks for the construction of such a news algorithm as they are being developed by the Ghent University interdisciplinary research project #NewsDNA, of which the primary aim is to actually build, evaluate and test a diversity-enhancing news recommender. As such, the deployment of artificial intelligence could support the media in providing people with information and stimulating public debate, rather than undermine their role in that respect. To do so, it combines insights from computer sciences (news recommender systems), law (right to receive information), communication sciences (conceptualisations of news diversity), and computational linguistics (automated content extraction from text). To gather feedback from scholars of different backgrounds, this research has been presented and discussed during the 2019 IFIP summer school workshop on ‘co-designing a personalised news diversity algorithmic model based on news consumers’ agency and fine-grained content modelling’. This contribution also reflects the results of that dialogue. KeywordsNews personalisationAlgorithmsNews recommender systemsRight to receive diverse informationNews diversityNews content extraction#NewsDNA

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.

With the rapidly growing amount of items and news articles on the internet, recommender systems are one of the key technologies to cope with the information overload and to assist users in finding information matching the their individual preferences. News and domain-specific information portals are important knowledge sources on the Web frequently accessed by millions of users. In contrast to product recommender systems, news recommender systems must address additional challenges, e.g. short news article lifecycles, heterogonous user interests, strict time constraints, and context-dependent article relevance. Since news articles have only a short time to live, recommender models have to be continuously adapted, ensuring that the recommendations are always up-to-date, hampering the pre-computations of suggestions. In this paper we present our framework for providing real-time news recommendations. We discuss the implemented algorithms optimized for the news domain and present an approach for estimating the recommender performance. Based on our analysis we implement an agent-based recommender system, aggregation several different recommender strategies. We learn a context-aware delegation strategy, allowing us to select the best recommender algorithm for each request. The evaluation shows that the implemented framework outperforms traditional recommender approaches and allows us to adapt to the specific properties of the considered news portals and recommendation requests.

In recent years, many traditional news websites developed corresponding recommendation systems to cater to readers’ interests and news recommendation systems are widely applied in traditional PCs and mobile devices. News recommendation system has become a critical research hotspot in the field of recommendation system. As News contains more text information, it is more helpful to improve the recommendation effect to obtain the content related to news features (location, time, events) from the news. This survey summarizes news features-based recommendation methods including location-based news recommendation methods, time-based news recommendation methods, events-based news recommendation methods. It helps researchers to know the application of news features in news recommendation methods. Also, this suvery summarizes the challenges faced by the news recommendation system and the future research direction.

Publishing news represents a vital function for societal health. News recommender systems, which support readers finding relevant content, face challenges beyond those encountered by other types of recommender systems. They have to deal with a dynamic flow of unstructured, fragmentary, and potentially unreliable news stories. The International Workshop on News Recommendation and Analytics (INRA) focuses on the challenges of news recommender systems and aims to connect researchers, practitioners and journalists. The seventh edition of INRA takes place as a half-day workshop in conjunction with thirteenth ACM Conference on Recommender Systems (RecSys '19) on September 16--20, 2019 in Copenhagen, Denmark. INRA 2019 focuses on the news recommender systems under three main categories: News recommendation, news analytics, and ethical aspects of news recommendation.

Deep learning in news recommender systems: A comprehensive survey, challenges and future trends

Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-of-the-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions.

Online news contents is increasing exponentially and news are collected from various sources. Personalized news recommendation system has been developed for supporting individual users and this approach will increase the user engagement and satisfaction. Traditional news recommendation system suffers with volatility of user preferences and feedback comment given news feeds. This paper proposes an adaptive reinforcement learning framework by designing with improved artificial bee colony optimization technique. This recommendation framework will enhance personalized news recommendations. The proposed news recommendation system uses reinforcement learning technique for creating an interactive user mode and adaptive recommendations based on continuous learning model. The traditional news recommendation system rely on static user item relationships, ARL optimization to participate in a long time through exploration and exploitation strategies. The efficiency and accuracy in a learning environment has been improved by applying IABC technique. The improved artificial bee colony optimization technique enhance learning rate, guided searching strategies, and efficient exploration. These improvements will enhance the results through fast convergence speed and solution quality. The news recommendations are performed concerning the personalized wish list and most common attributes in the research of news content. The Agent node will create a suggestion list with news feeds based on personally collected information from individuals. Based on the user news reading strategy, the environment will perform the rewarding mechanism. The proposed Agent node is designed using the IABCO algorithm, which makes a suggestion list with enriched news content by using adaptive threshold value selection-based probability of success. The performance evaluation has been conducted with following parameters, like precision, recall, F1 Score, Click Through Rate (CTR), Average Click Position, Diversity, and Coverage. A comparative analysis is carried out with existing news recommendation systems and this result shows that the proposed news recommendation system achieves 93.6 % precision, 92. % recall, and 92.9% F1 score values. This result shows that the superiority of proposed news recommendation approach and this would be able to provide highest accuracy value, which is nearly 97.5%.

In order to solve the problems of poor performance of the recommendation system caused by not considering the needs of users in the process of news recommendation, a news recommendation system based on deep network and personalized needs is proposed. Firstly, it analyzes the news needs of users, which is the basis of designing the system. The functions of the system module mainly include the network function module, database module, user management module, and news recommendation module. Among them, the user management module uses the deep network to set the user news interest model, inputs the news data into the model, completes the personalized needs of the news, and realizes the design of the news recommendation system. The experimental results show that the proposed system has good effect and certain advantages.

News article recommendation differs in several ways from other well-known types of recommender systems such as for music and movies. First, freshness represents an important aspect. Sometimes, freshness is deemed more important than relevancy. Second, similarity between news articles does not necessarily reflect their relatedness. For instance, two news articles might share a majority of words. Still, their actual topic might differ. Third, news are typically published in a rather unstructured format. In contrast, structured data such as social graphs facilitate pre-processing steps. Fourth, news readers might have special preferences on some particular events which recommender systems can barely predict. Fifth, serendipities (i. e., variety in recommended news articles) represent a crucial property of a news recommender system. Contrarily to music recommendations, users avoid to re-consume an item. Thus, news recommender systems are required to provide diverse sets of items in order to assure not to recommend monotonously. Sixth, breaking or trendy news might have a high relevance even though the appear completely unrelated to the individual user profile. Seventh, the has not yet established consensus on how to evaluate news recommender systems. We typically observe implicit preferences as users interact with news portals. Those preferences do not exhibit a graded relevance. Thus, well-established evaluation criteria based on ratings (e.g., root mean squared error) cannot be applied. Another set of challenges arises from the context of news recommendation. Recommender systems are known to struggle with so-called "cold-start users" (i. e., users form whom no preferences are available yet). News portals typically refrain to require users to login prior to read news articles. Hence, there is a large fraction of users who appear to be "cold-start users". The lack of sufficiently many interaction to established trustworthy user profiles entails further challenges. Inferring interest signals suffers from incomplete profiles. Additionally, items' relevance is time dependent and diminishes with time progressing. Another challenge related to the context arises from the increasingly frequent use of mobile devices to read news articles such as tablets and smart phones. Those have a limited space available to display news and related recommendations. News recommender systems have to deal with this issue thus applying suited layout mechanisms to fit the content to the available screen. Finally, news recommender systems face numerous technical challenges. Those challenges include minimizing response time improve user experience, scaling to the large amount of requests to avoid time outs, providing flexibility to incorporate new recommendation methods or adjust parameter settings for existing implementations, and guarantee a reliable service whom user can access at any time.

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

News consumption has shifted over time from traditional media to online platforms, which use recommendation algorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their preferences and reading behavior. In comparison to domains such as movies or e-commerce, where recommender systems have proved highly successful, the characteristics of the news domain (e.g., high frequency of articles appearing and becoming outdated, greater dynamics of user interest, less explicit relations between articles, and lack of explicit user feedback) pose additional challenges for the recommendation models. While some of these can be overcome by conventional recommendation techniques, injecting external knowledge into news recommender systems has been proposed in order to enhance recommendations by capturing information and patterns not contained in the text and metadata of articles, and hence, tackle shortcomings of traditional models. This survey provides a comprehensive review of knowledge-aware news recommender systems. We propose a taxonomy that divides the models into three categories: neural methods, non-neural entity-centric methods, and non-neural path-based methods. Moreover, the underlying recommendation algorithms, as well as their evaluations are analyzed. Lastly, open issues in the domain of knowledge-aware news recommendations are identified and potential research directions are proposed.

News recommendation systems are widely used to address the information overloading problem. Many Web-based news reading services, like Google News and Yahoo! News, have become increasingly prevalent as they help users find interesting articles from news providers that match the users' preference. However, few research efforts have been reported on campus news recommendation. Different from news articles, news from vertical systems is often short with limited topic scope, targeting at specific audience. To address the aforementioned characteristics, in the paper, we develop a hybrid recommendation system for campus news by integrating different recommendation algorithms using linear combination. Offline and online experiments are conducted to evaluate the system effectiveness.

In our fast changing world, data streams move into the focus. In this paper, we study recommender systems for news portals. Compared with traditional recommender scenarios based on static data sets, the short life cycle of news items and the dynamics in users' preferences are major challenges when developing news recommender systems. This motivates us to research methods facilitating the inclusion of context and trends into news recommender systems. We explain specific requirements for news recommender system and discuss approaches incorporating trends and temporal user habits in order to improve news recommender system. A detailed data analysis motivates our approach. In addition, we discuss experiences of applying news recommendation algorithms online. The evaluation shows that approaches come with specific strengths and weaknesses. Consequently, publishers should select the recommendation strategy with the specific requirements in mind.

News recommendation systems? purpose is to tackle the immense amount of news and offer personalized recommendations to users. A major issue in news recommendation is to capture the precise news representations for the efficacy of recommended items. Commonly, news contents are filled with well-known entities of different types. However, existing recommendation systems overlook exploiting external knowledge about entities and topical relatedness among the news. To cope with the above problem, in this paper, we propose Topic-Enriched Knowledge Graph Recommendation System(TEKGR). Three encoders in TEKGR handle news titles in two perspectives to obtain news representation embedding: (1) to extract meaning of news words without considering latent knowledge features in the news and (2) to extract semantic knowledge of news through topic information and contextual information from a knowledge graph. After obtaining news representation vectors, an attention network compares clicked news to the candidate news in order to get the user's final embedding. Our TEKGR model is superior to existing news recommendation methods by manipulating topical relations among entities and contextual features of entities. Experimental results on two public datasets show that our approach outperforms state-of-the-art deep recommendation approaches.