Personalized Recommendation Algorithms Research Articles

Digital media entertainment technology based on artificial intelligence robot in art teaching simulation

The combination of digital media entertainment technology and artificial intelligence robots provides new possibilities for art teaching, providing students with a richer and more personalized learning experience. The aim of this study is to explore the application of artificial intelligence based digital media entertainment technology in art teaching simulation. By designing an intelligent digital media system, the interactivity, personalization, and effectiveness of art teaching can be improved. Research the use of digital media interactive technology to create an immersive art learning environment. Applying artificial intelligence recommendation algorithms to recommend personalized art teaching resources to students based on their learning history, interests, and abilities, in order to improve learning efficiency and outcomes. Combining artificial intelligence recommendation algorithms, intelligently recommend suitable art teaching resources for different students’ needs and levels. Through teaching interaction simulation testing, evaluate the interaction effect and user experience of the system in simulated art teaching scenarios, identify and fix potential problems. By utilizing digital media interaction technology and personalized recommendation algorithms, designing an intelligent digital media system can effectively enhance the interactivity, personalization, and effectiveness of art teaching, providing students with a better learning experience.

Content-based fabric recommendation system integrating image and text information

The increasing popularity of e-commerce and online fashion platforms has led to a growing demand for effective fabric recommendation systems. Choosing the suitable fabric is crucial to achieving desired aesthetic outcomes, ensuring product quality, and enhancing user satisfaction in the fashion design and textile manufacturing industries. Although our research group has made significant progress in fabric retrieval, we have encountered several challenges in practical collaborations with industry partners. These challenges include users’ difficulty expressing their preferences and the lack of personalization in fabric retrieval. We proposed a content-based fabric recommendation system that combines image and text information to address these challenges. Our approach employed various methods to extract hand-crafted features, such as image color and texture, to characterize the visual communication of fabrics. We combined this with text information to provide a more comprehensive fabric representation. By mining user preferences and employing personalized recommendation algorithms, our system delivered personalized fabric recommendations to cater to individual needs. Experimentation with comparison texts, colors, and textures separately or together. According to the assessment results, the highest F1 value of 83% can be achieved when the color, texture, and text combine with the color: texture: text = 1:1:1 and the filter value = 2.2. Precision and recall rates are also 83% at this point. Our proposed fabric recommendation system is useful and efficient.

From Loose Groups to Rigorous Organizations: The Transitional Logic of Chinese Fan Communities from the Perspective of Media Development

Currently, Chinese fan organizations demonstrate strong internal cohesion and efficient executive force, far exceeding previous fan groups. This article explores the development of Chinese fan communities and finds that the Internet, a new kind of media that breaks through time and space limitations, has caused this change. Firstly, the development of the Internet has made timely interaction between fans possible, strengthening the internal cohesion of fan groups. Besides, the information cocoon brought by personalized recommendation algorithms further makes the fan communities change into homogeneous groups with similar value orientations and high group identification regarding their respective idols.Meanwhile, the flow economy based on the Internet has prompted fan groups to seek ways to achieve efficient data labor, leading to the division of labor within fan groups. As a result, these previously loose fan groups evolve into rigorous organizations with a hierarchical structure, functional division of labor, and institutional norms. The common emotional foundation—is the recognition of idols; the common goal—is creating internet flow for idols; the field of collective action is the internet and social media platforms; and the mechanism of unified action—the hierarchical and functional system makes the current Chinese fan organizations unprecedentedly strong.

Research on the Optimization of English Teaching Content in Colleges and Universities by Combining Decision Tree and Big Data Technology

Abstract English teaching in colleges and universities is facing the growth of personalized teaching demand, and the traditional education model is challenging to meet the diverse learning needs of students. Big data and decision tree technology provide a new way for teaching content optimization, which can achieve a high degree of matching between educational resources and students’ needs, and improve teaching effectiveness and learning efficiency. This study explores the application of decision trees and big data technology in optimizing English teaching content in colleges and universities. By constructing an ontology tree model of learning resources and adopting similarity calculation and personalized recommendation algorithms, the study achieves personalized learning resource recommendations for students. The study utilizes comparative experimental methods to analyze achievement changes between the experimental and control classes after customized learning. The personalized teaching program’s effectiveness can be confirmed by the fact that the average final grade of students in the experimental class is 1.5 points higher than that of the control class. The Analysis of CET-4 scores found that personalized teaching improved students’ English listening, reading, and writing skills. This paper combines decision trees and big data technology to optimize teaching content, effectively enhancing students’ English learning outcomes and providing new teaching strategies for English education in colleges and universities.

Research on Personalized Recommendation Algorithms Based on User Profile

Research on Personalized Recommendation Algorithms Based on User Profile

Job-Recommendation Website

Abstract: In today's competitive work world, it can be challenging to find the right employment opportunities that match an individual's talents and goals. To address this, we propose building a job recommendation website with HTML, CSS, JavaScript, Bootstrap, PHP, and SQL, with the purpose of linking job seekers with suitable employment openings. Personalized recommendation algorithms match users with job adverts based on their profiles, talents, and preferences. By delivering an intuitive user interface, effective job search tools, and data-driven insights, the website improves the whole job search experience, fostering meaningful interactions between job seekers and organizations.

DOR: a novel dual-observation-based approach for recommendation systems

As online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, often overlooking the intricate connection between the reading content and the user’s prior knowledge and interest. This disconnect can consequently lead to a paucity of diverse and personalized recommendations. In this paper, we propose a novel approach to tackle the multifaceted issue of recommendation. We introduce the Dual-Observation-based approach for the Recommendation (DOR) system, a novel model leveraging dual observation mechanisms integrated into a deep neural network. Our approach is designed to identify both the core theme of an article and the user’s unique engagement with the article, considering the user’s belief network, i.e., a reflection of their personal interests and biases. Extensive experiments have been conducted using real-world datasets, in which the DOR model was compared against a number of state-of-the-art baselines. The experimental results explicitly demonstrate the reliability and effectiveness of the DOR model, highlighting its superior performance in news recommendation tasks.

Research on a Hotel Collaborative Filtering Recommendation Algorithm Based on the Probabilistic Language Term Set

In the face of problems such as information overload and the information cocoon resulting from big data, it is a key point of current research to solve the problem of semantic fuzziness of online reviews and improve the accuracy of personalized recommendation algorithms by using online reviews. Based on the advantage of the probabilistic language term set to deal with fuzzy information and the historical data of online hotel reviews, this paper proposes a collaborative filtering recommendation algorithm for hotels. Firstly, the text data of hotel online reviews are crawled by a crawler and processed by jieba and TF-IDF tools. Secondly, the hotel evaluation attribute set is constructed, and the sentiment analysis of the review statements is carried out with the help of the HowNet sentiment dictionary and manual annotation method. The probabilistic language term set is used to classify the data and derive statistics, and the maximum deviation method is used to determine the weight of each attribute. Then, the cosine similarity formula is fused with the modified cosine similarity formula to calculate the similarity and construct the decision matrix. Finally, combined with the historical data of the user’s hotel selection, the hotel recommendation results are generated. This paper collected review data from 10 hotels in Macau from the official “Ctrip” website. The proposed recommendation algorithm model was then applied to process and analyze the data, resulting in the generation of a ranked list of hotel recommendations. To validate the accuracy and effectiveness of this research, the recommendation results were compared with those produced by other algorithms.

Personalized Hybrid Recommendation Algorithm for MOOCs Based on Learners’ Dynamic Preferences and Multidimensional Capabilities

In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current recommendation algorithm focuses mainly on the learners’ course ratings. However, the choice of courses is not only based on the learners’ interests and preferences. It is also affected by learners’ knowledge domains and learning capabilities, all of which change dynamically over time. Therefore, this study proposes a personalized hybrid recommendation algorithm combining clustering with collaborative filtering. First, data on learners’ course rating preferences, course attribute preferences, and multidimensional capabilities that match course traits are used based on multidimensional item response theory. Second, considering that learners’ preferences and multidimensional capabilities change dynamically over time, the Ebbinghaus forgetting curve is introduced by integrating memory weights to improve the accuracy and interpretation of the proposed recommendation algorithm for MOOCs. Finally, the performance of the proposed recommendation algorithm is investigated using data from Coursera, an internationally renowned MOOCs platform. The experimental results show that the proposed recommendation algorithm is superior to the baseline algorithms. Accordingly, relevant suggestions are proposed for the development of MOOCs.

Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks

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.

E-Commerce Online Shopping Platform Recommendation Model Based on Integrated Personalized Recommendation

With the continuous innovation of Internet technology and the substantial improvement of network basic conditions, e-commerce has developed rapidly. Online shopping has become the mainstream mode of e-commerce. In order to solve the problem of information overload and information loss in the selection of e-commerce online shopping platform, a personalized recommendation system using information filtering technology has come into being. An e-commerce online shopping platform recommendation model is proposed based on integrated multiple personalized recommendation algorithms: random forest, gradient boosting decision tree, and eXtreme gradient boosting. The proposed model is tested on the public data set. The experimental results of the separate model and mixed model are compared and analyzed. The results show that the proposed model reduces the recommendation sparsity and improves the recommendation accuracy.

Filter bubble effect in the multistate voter model.

Social media influence online activity by recommending to users content strongly correlated with what they have preferred in the past. In this way, they constrain users within filter bubbles strongly limiting their exposure to new or alternative content. We investigate this type of dynamics by considering a multistate voter model where, with a given probability λ, a user interacts with "personalized information," suggesting the opinion most frequently held in the past. By means of theoretical arguments and numerical simulations, we show the existence of a nontrivial transition between a region (for small λ) where a consensus is reached and a region (above a threshold λc) where the system gets polarized and clusters of users with different opinions persist indefinitely. The threshold always vanishes for large system size N, showing that a consensus becomes impossible for a large number of users. This finding opens new questions about the side effects of the widespread use of personalized recommendation algorithms.

Teaching and confronting digital extremism: contexts, challenges and opportunities

PurposeThis paper aims to offer practical guidance on teaching about digital extremism – defined here as the intersection of digital disinformation campaigns with political extremism – by highlighting four pedagogical challenges: the danger of unintentionally “redpilling” students; the slippery slope to false equivalency and “bothsidesism” in turbulent partisan waters; the difficulty of separating empirical analyses from prescriptive debates circulating in popular media; and the trouble of getting students to understand digital extremism as a sociotechnical problem rather than as a social-or-technical problem. The conclusion proposes opportunities for educators to integrate practical approaches to confronting digital extremism with digital civics curricula.Design/methodology/approachThis paper reviews pedagogical challenges and outlines a curricular program for teaching about digital extremism drawn from the author’s experience designing undergraduate courses and open teaching modules between 2016 and 2021.FindingsEducators should shift focus from the substance of digital extremism to its tools – social media platforms’ surveillance and data-gathering methods, advertising technologies and monetized user-generated content, personalized recommendation algorithms and media manipulation strategies that amplify some narratives while suppressing others – and the media and political institutions that benefit most from it. Proposed lessons include: how digital extremists manipulate social media metadata; engagement with data creation and targeting practices; and analysis of information production, circulation and consumption exploring media manipulation tools and their effects.Originality/valueThis paper’s added values are the insights and practical recommendations for undergraduate educators teaching on a topic of urgent contemporary concern: digital extremism.

A study on the impact of personalized recommendation algorithms in webcasting on the development of rural e-commerce entrepreneurship

A study on the impact of personalized recommendation algorithms in webcasting on the development of rural e-commerce entrepreneurship

Micromedia News Dissemination Impact Assessment Integrated with Personalized Recommendation Algorithm

The development of the Internet has completely changed the way of recommending and disseminating news content. Traditional media forms of news dissemination effectiveness evaluation methods are no longer fully suitable for the evolving needs of new media news dissemination. Therefore, it is necessary to innovate methods for evaluating the effects of new media news dissemination. This article mainly combines personalized recommendation algorithms to evaluate the effectiveness of news dissemination. Currently, popular personalized recommendation algorithms include content-based recommendation algorithms, collaborative filtering recommendation algorithms, knowledge-based recommendation algorithms, and associated recommendation algorithms. These recommendation algorithms are effective. This promotes the dissemination of news, which also recommends news content that is more relevant to user preferences for most users. In addition, the quality of news content is further evaluated through the news rating system, thereby effectively improving the quality of news content.

Design of Personalized Recommendation Algorithms based on Big Data Framework

The issue of information overload has become increasingly prominent since there are various kinds of data generated daily. A good recommendation systems can better deal with such problems. However, traditional recommendation systems for a single machine are suffering from the computing bottleneck in the environment of massive data. An individual recommendation algorithm is unable to gratify desiring users. To tackle this problem, we designed and implemented three kinds of recommendation algorithms based on big data framework in this paper. Besides, we improved the traditional recommendation algorithms leveraging the prevailing big data processing technologies. Finally, we evaluated the efficiency of the algorithm through recall rate, precision rate and coverage. Experiments show that the hybrid model-based recommendation algorithms which can be applied to the bulk data environment are better than the single recommendation algorithms.

Application of Collaborative Filtering Personalized Recommendation Algorithms to Website Navigation

This article builds a personalized management and push design for the functional and performance requirements of website personalized recommendations. The system uses PHPstorm as the development platform and Hadoop distributed file system combined with DBMS design algorithm to achieve data level research, and uses a combination of customer-based collaborative filtering and project-based collaborative filtering to categorize clients and web pages in personalized recommendation, which improves the calculation and personalization of website navigation to a certain extent.

Embodying algorithms, enactive artificial intelligence and the extended cognition: You can see as much as you know about algorithm

The recent proliferation of artificial intelligence (AI) gives rise to questions on how users interact with AI services and how algorithms embody the values of users. Despite the surging popularity of AI, how users evaluate algorithms, how people perceive algorithmic decisions, and how they relate to algorithmic functions remain largely unexplored. Invoking the idea of embodied cognition, we characterize core constructs of algorithms that drive the value of embodiment and conceptualizes these factors in reference to trust by examining how they influence the user experience of personalized recommendation algorithms. The findings elucidate the embodied cognitive processes involved in reasoning algorithmic characteristics – fairness, accountability, transparency, and explainability – with regard to their fundamental linkages with trust and ensuing behaviors. Users use a dual-process model, whereby a sense of trust built on a combination of normative values and performance-related qualities of algorithms. Embodied algorithmic characteristics are significantly linked to trust and performance expectancy. Heuristic and systematic processes through embodied cognition provide a concise guide to its conceptualization of AI experiences and interaction. The identified user cognitive processes provide information on a user’s cognitive functioning and patterns of behavior as well as a basis for subsequent metacognitive processes.

Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm

With the unprecedented development in the internet technology, the information overload issues have become more and more complex, resulting in users being unable to obtain the target information accurately and effectively in selecting the required information from a large pool of surfed data. In view of this a recommendation system can be used to predict the user's selection probability for different potential objects as an important tool, which can help to solve the information overload issues. So far, many personalized recommendation algorithms based on bipartite graphS have been proposed, most of which are based on the similarity degree among users or items, such as collaborative filtering (CF), mass diffusion (MD) and heat conduction (HC). Among many recommendation algorithms, the performances of algorithms are varied. MD algorithm has high recommendation accuracy but poor diversity, while HC algorithm has good diversity but low accuracy. In order to solve the dilemma in accuracy and diversity, some hybrid recommendation algorithm have been proposed. This paper has mainly focused on the hybrid recommendation algorithm HHM, and pointed out its shortcomings. Based on the reconsideration of the effect of item popularity in the recommendation process, an improved hybrid recommendation algorithm using dual parameter called IHM was proposed. The particle swarm optimization (PSO) algorithm was applied to the parameter optimal of the hybrid recommendation algorithm to obtain the parameters of the algorithm. Experiments on 3 real datasets indicated that the IHM algorithm is better than HHM algorithms in terms of the recommendation accuracy, diversity and novelty. Meanwhile, the IHM algorithm can also improve the recommendation for items with lower popularity and solve the cold start problem.

Improved Personalized Recommendation Algorithm Based on Context-Aware in Mobile Computing Environment

With the development and popularization of e-commerce and Internet, more and more attention has been paid to personalized recommendation for users. The traditional user interest model only considers the user’s behavior on the project, ignoring the user’s context at that time. Pointing to the shortage that context-related factors are not considered in previous works, combining the characteristics of a mobile computing environment, this paper studies the algorithm and model of mobile service recommendation. A recommendation algorithm based on specified context filtering in mobile computing environment is proposed. The context of the classification is aggregated, by grouping the scenarios of the same category together. Through experiments, we found that the improved personalized recommendation algorithms are superior to the common collaborative filtering algorithm.