News selection and recommendation algorithm based on machine learning and image matching algorithms

Recommendation systems are used when searching online databases. As such they are very important tools because they provide users with predictions of the outcomes of different potential choices and help users to avoid information overload. They can be used on e-commerce websites and have attracted considerable attention in the scientific community. To date, many personalized recommendation algorithms have aimed to improve recommendation accuracy from the perspective of vertex similarities, such as collaborative filtering and mass diffusion. However, diversity is also an important evaluation index in the recommendation algorithm. In order to study both the accuracy and diversity of a recommendation algorithm at the same time, this study introduced a “third dimension” to the commonly used user/product two-dimensional recommendation, and a recommendation algorithm is proposed that is based on a triangular area (TR algorithm). The proposed algorithm combines the Markov chain and collaborative filtering method to make recommendations for users by building a triangle model, making use of the triangulated area. Additionally, recommendation algorithms based on a triangulated area are parameter-free and are more suitable for applications in real environments. Furthermore, the experimental results showed that the TR algorithm had better performance on diversity and novelty for real datasets of MovieLens-100K and MovieLens-1M than did the other benchmark methods.

Big data technology has not only become an important driving force of industry digitalization and intelligence, but also a fundamental source of innovation and development. Through the study of pricing model, the application of big data technology in service trading can be more standardized and orderly. As a special data form in the recommendation field, traditional recommendation methods are often difficult to model session sequence data. Although some scholars have improved the traditional recommendation method to solve the problem of session sequence modeling, the effectiveness of the recommendation system is limited by many problems in the model structure. In recent years, with deep learning in image recognition, natural language processing, speech recognition and other breakthroughs in the field of artificial intelligence, will study deep into the recommendation algorithm, can help to effectively solve the traditional recommendation algorithm of multi-source heterogeneous information, data sparse, cold start, data characteristics of the design of artificial rely on such issues, in this case, The results of traditional recommendation methods are often inaccurate, and the results are often lagging and repeatable.

A novel context-aware recommendation algorithm with two-level SVD in social networks

At present, the Internet products for the study of user interests and hobbies data mining and then recommended to increase user retention and user stickiness behaviour has been a common practice, music hobbies as an important point of interest for thousands of users, and its recommendation has become an important content of social life and has been the subject of more and more attention by the researchers, based on the collaborative filtering algorithm of the music recommendation is the use of the particularly wide range of recommendation algorithms. Music recommendation based on collaborative filtering algorithm is one of the most widely used recommendation algorithms, which mainly analyses the similarity between users or items, and completes the recommendation process based on this, but it has the problems of poor recommendation diversity and low coverage. Therefore, this paper designs comparative experiments to compare the recommendation algorithms based on FP-growth and collaborative filtering, relying on Last.fm's real user data warehouse, and demonstrates through experimental comparative research that compared with the collaborative filtering recommendation algorithm, the results of the recommendation content based on the FP-growth algorithm have a certain degree of enhancement in terms of diversity and coverage, and can produce a certain degree of accuracy in terms of the recommendation of music. accuracy, it can produce certain reference value.

Chinese traditional culture contains the wisdom and experience of the concept of governing the country, and contains the moral principles taught by the ancients. Carrying forward and developing the excellent traditional Chinese culture is of great significance for enhancing cultural self-confidence. The purpose of this paper is to study digital cultural communication based on recommendation algorithms. Combined with the particularity of the existence of digital culture, the recommendation technology is studied, aiming at the problems existing in collaborative filtering recommendation technology, an improvement method is proposed, and a recommendation algorithm for digital cultural resources is proposed. Combined with the digital cultural resource recommendation algorithm, a digital cultural resource recommendation engine is designed and implemented. Through the realization of the algorithm, using the data in the “Digital Cultural Resource Database” of the scientific research achievement of the Key Laboratory of Cultural Informatization, the digital cultural resource recommendation algorithm is compared with the traditional collaborative filtering recommendation algorithm, and the MAE mean absolute deviation is used as the main According to the experimental data, the MAE values of the improved collaborative filtering digital culture recommendation algorithm are all lower than 0.15 points, and the conclusion is drawn that the improved recommendation algorithm has higher recommendation quality.

With the arrival of the era of big data, recommendation algorithm has become a hot research field in internet life and social business. However, the traditional recommendation algorithms have problems of cold start, scoring matrix data sparse, affecting the accuracy of recommendation. This paper proposes a hybrid recommendation algorithm based on the SVD (singular value decomposition) and Slope One algorithm, experiments results show that the algorithm proposed by this paper has superior performance compared with the traditional collaborative filtering recommendation algorithm. Keywords-SVD; Slope-One; Recommendation Algorithm; Collaborative Filtering

As an important branch of machine learning, recommendation algorithms have attracted the attention of many experts and scholars. The current recommendation algorithms all more or less have problems such as cold start and single recommended items. In order to overcome these problems and improve the accuracy of personalized recommendation algorithms, this paper proposes a recommendation for multi-task learning based on directed graph convolutional network (referred to as MTL-DGCNR) and applies it to recommended areas for e-commerce. First, the user’s micro-behavior is constructed and converted into directed graph structure data for model embedding. It can fully consider the embedding of first-order proximity nodes and second-order proximity nodes, which can effectively enhance the transformation ability of features. Secondly, this model adopts the multi-task learning method, and uses knowledge graph embedding to effectively deal with the one-to-many or many-to-many relationship between users and commodities. Finally, it is verified by experiments that MTL-DGCNR has a higher interpretability and accuracy in the field of e-commerce recommendation than other recommendation models. The ranking evaluation experiments, various training methods comparison experiments, and controlling parameter experiments are designed from multiple perspectives to verify the rationality of MTL-DGCNR.

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

The purpose is to provide researchers with reliable Scientific Research Data (SRD) from the massive amounts of research data to establish a sustainable Scientific Research (SR) environment. Specifically, the present work proposes establishing an Intelligent Recommendation System (IRS) based on Machine Learning (ML) algorithm and SRD. Firstly, the IRS is established over ML technology. Then, based on user Psychology and Collaborative Filtering (CF) recommendation algorithm, a hybrid algorithm [namely, Content-Based Recommendation-Collaborative Filtering (CBR-CF)] is established to improve the utilization efficiency of SRD and Sustainable Development (SD) of SR. Consequently, the present work designs literature and SRD-targeted IRS using the hybrid recommendation under the background of SD. The proposed system’s feasibility is analyzed through experiments. Additionally, the system performance is analyzed and verified from accuracy, diversity, coverage, novelty, and recommendation efficiency. The results show that the hybrid algorithm can make up for the shortcomings of a single algorithm and improve the recommendation efficiency. Experiments show that the accuracy of the proposed CBR-CF algorithm is the highest. In particular, the recommendation accuracy for the single-user system can reach 82–93%, and the recall of all recommended algorithms falls between 60 and 91%. The recall of the hybrid algorithm is higher than that of a single algorithm, and the highest recall is 91%. Meanwhile, the hybrid algorithm has comprehensive coverage, good applicability, and diversity. Therefore, SD-oriented SRD-targeted IRS is of great significance to improve the SRD utilization and the accuracy of IRS, and expand the achievement value of SR. The research content provides a reference for establishing a sustainable SR environment and improving SR efficiency.

Recommender systems help users to find products or services they may like when lacking personal experience or facing an overwhelming set of choices. Since unstable recommendations can lead to distrust, loss of profits, and a poor user experience, it is important to test recommender system stability. In this work, we present an approach based on inferred models of influence that underlie recommender systems to guide the generation of dataset modifications to assess a recommender’s stability. We implement our approach and evaluate it on several recommender algorithms using the MovieLens dataset. We find that influence-guided fuzzing can effectively find small sets of modifications that cause significantly more instability than random approaches.

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%.

Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners' enthusiasm and give full play to different learners' learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low-dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.

Recommendation algorithms trained on a training set containing sub-optimal decisions may increase the likelihood of making more bad decisions in the future. We call this harmful effect self-induced bias, to emphasize that the bias is driven directly by the user’s past choices. In order to better understand the nature of self-induced bias of recommendation algorithms that are used by older adults with cognitive limitations, we have used agent-based simulation. Based on state-of-the-art results in psychology of aging and cognitive science, as well as our own empirical results, we have developed a cognitive model of an e-commerce client that incorporates cognitive decision-making abilities. We have evaluated the magnitude of self-induced bias by comparing results achieved by simulated agents with and without cognitive limitations due to age. We have also proposed new recommendation algorithms designed to counteract self-induced bias. The algorithms take into account user preferences and cognitive abilities relevant to decision making. To evaluate the algorithms, we have introduced 3 benchmarks: a simple product filtering method and two types of widely used recommendation algorithms: Content-Based and Collaborative filtering. Results indicate that the new algorithms outperform benchmarks both in terms of increasing the utility of simulated agents (both old and young), and in reducing self-induced bias.

At present, research on users’ preferences is becoming increasingly widespread. Traditional research used a simple cosine similarity algorithm to calculate the similarity between users and just focused on one factor. So, a more complete method is described for researching preferences by considering more factors of the network to analyse data. Two personalisation recommendation algorithms are given based on users’ preferences. These two algorithms mainly work on two aspects: one is the content-based recommendation algorithm. It uses a cosine similarity algorithm to calculate the similarity between users, uses LDA to obtain improvement and uses screens according to a user impact factor. The other is a relation-based recommendation algorithm. It uses the PersonalRank algorithm to calculate the similarity, and uses the weight of users’ communication behaviours to improve the algorithm. Finally, micro-blog users’ data are analysed in detail, and the consequences of the two recommendation methods are fused based on the content of the results and recommendations. As a result, we obtained more accurate recommendation results and verified the feasibility of the algorithm by experimental analysis.