Collaborative Filtering Algorithm Research Articles

A Personalized Collaborative Filtering Recommendation System Based on Bi-Graph Embedding and Causal Reasoning

The integration of graph embedding technology and collaborative filtering algorithms has shown promise in enhancing the performance of recommendation systems. However, existing integrated recommendation algorithms often suffer from feature bias and lack effectiveness in personalized user recommendation. For instance, users’ historical interactions with a certain class of items may inaccurately lead to recommendations of all items within that class, resulting in feature bias. Moreover, accommodating changes in user interests over time poses a significant challenge. This study introduces a novel recommendation model, RCKFM, which addresses these shortcomings by leveraging the CoFM model, TransR graph embedding model, backdoor tuning of causal inference, KL divergence, and the factorization machine model. RCKFM focuses on improving graph embedding technology, adjusting feature bias in embedding models, and achieving personalized recommendations. Specifically, it employs the TransR graph embedding model to handle various relationship types effectively, mitigates feature bias using causal inference techniques, and predicts changes in user interests through KL divergence, thereby enhancing the accuracy of personalized recommendations. Experimental evaluations conducted on publicly available datasets, including “MovieLens-1M” and “Douban dataset” from Kaggle, demonstrate the superior performance of the RCKFM model. The results indicate a significant improvement of between 3.17% and 6.81% in key indicators such as precision, recall, normalized discount cumulative gain, and hit rate in the top-10 recommendation tasks. These findings underscore the efficacy and potential impact of the proposed RCKFM model in advancing recommendation systems.

Research on the Recommendation System of Music e-learning Resources with Blockchain based on Hybrid Deep Learning Model

Learners are confronted with an ever-growing array of diverse and complex educational resources as music education increasingly moves to online platforms. Traditional resource curation methods, which rely heavily on educators, fall short of meeting the dynamic needs of modern students. To address this issue, we present a novel recommendation system for music e-learning resources that combines the power of blockchain technology with a hybrid deep learning model. Our model combines blockchain's robust security and transparency features with advanced deep learning algorithms, enhancing the personalization and efficiency of resource recommendations. A backpropagation neural network with K nearest neighbor classification, traditional collaborative filtering (CF), and an improved CF algorithm are used in the hybrid approach. For the back propagation neural network algorithm, K nearest neighbor classification algorithm, traditional collaborative filtering (CF) and improved CF algorithm, the accuracy rate of improved CF algorithm is higher, reaching 95%. Comparing the proposed model with the association rule-based recommendation model and the content-based recommendation model, the model constructed in this study received high evaluation from experts, with an average score of 98, and more than 97% of them gave a high score of 95 or more, and the evaluation of experts tended to be consistent. Overall, the model proposed in this study can make better recommendations for music education learning resources and bring users a good learning experience, so this study has some practical application value. This research demonstrates a highly effective, blockchain-enhanced recommendation system for music e-learning resources. Our model has significant practical value and potential for adoption in online music education platforms because it provides tailored educational content and an enhanced learning experience.

U-Net-based Recommender Systems for Political Election System using Collaborative Filtering Algorithms

U-Net-based Recommender Systems for Political Election System using Collaborative Filtering Algorithms

Online English writing teaching method that enhances teacher–student interaction

Abstract A significant component of the online learning platform is the online exercise assessment system, which has access to a wealth of past student exercise data that may be used for data mining research. However, the data from the present online exercise system is not efficiently used, making each exercise less relevant for students and decreasing their interest and interaction with the teacher as she explains the activities. In light of this, this research creates an exercise knowledge map based on the connections between workouts, knowledge points, and previous tournaments. The neural matrix was then improved using cross-feature sharing and feature augmentation units to deconstruct the workout recommendation model. The study also developed an interactive text sentiment analysis model based on the expansion of the self-associative word association network to assess how students interacted after the introduction of the personalized exercise advice teaching approach. The outcomes demonstrated that the suggested model’s mean diversity value at completion was 0.93, an increase of 0.14 and 0.23 over collaborative filtering algorithm and DeepFM (deep factor decompose modle), respectively, and that the proposed model’s final convergence value was 92.3%, an improvement of 2.3 and 4.1% over the latter two models. The extended model used in the study outperformed the support vector machine (SVM) and Random Forest models in terms of accuracy by 5.9 and 1.7%, respectively. In terms of F1 value indicator, the model proposed by the research has a value of 90.4%, which is 2.5 and 2.1% higher than the SVM model and Random Forest model; in terms of recall rate indicators, the model proposed by the research institute has a value of 94.3%, which is an increase of 6.2 and 9.8% compared to the latter two models. This suggests that the study’s methodology has some application potential and is advantageous in terms of customized recommendation and interactive sentiment recognition.

A Comprehensive Survey on Recommender Systems Techniques and Challenges in Big Data Analytics with IOT Applications

Purpose: Purpose of this research is to carry out survey on Recommendation systems techniques in Big Data Analytics. This article presents designing of recommender systems and evaluates it with help of various performance metrics in IoT applications.
 
 Theoretical framework: With fast development and applications of Internet of Things, large amount of user data is generated and accumulated every day. Growth of media consumption in online social networks is exponential which requires an efficient and effective recommendation system to enhance excellence in experience for users. Recommender systems help users to overcome Information Overload problem by providing them relevant contents. 
 
 Method/design/approach: The main aspect of recommender system is how to take complete advantage of this ubiquitous data. Recommender system is mainly used to guess or predict users’ interests and make relevant recommendations. Collaborative filtering is the technique that uses the relationships between users and between items in order to build a prediction. Collaborative filtering algorithms are mainly categorized as model-based methods and memory-based methods. In this article, various methods to build recommender system are described. Similarly, Collaborative filtering uses Pearson cosine, cosine vector, Jaccard similarity to identify same users or items. Recommender system has various applications in domain such as healthcare, transportation, agriculture, e-media etc. 
 
 Findings: Evaluation of recommender system with help of metrics such as Precision and Recall is presented. Comparison of experimental results is presented with help of MAE and RMSE. Recommendation system helps to discover relevant insights and can be one of the vital technologies in future IoT solutions.
 
 Research, Practical & social implications: The research makes significant contribution by providing survey of existing recommender systems along with challenges faced while designing effective and accurate recommender. Various similarity measures to find similar users or items are investigated with future pointer direction. Recommender system help in decision making process.
 
 Originality/value: The results and conclusion obtained in this research are helpful in development of novel Recommender systems which definitely assist users to overcome Information Overload issue. It helps user to save network load as well.

Website Design Using Laravel Framework for Tart Tuns Shop Sales Pontianak

Websites have become a means of getting information, promotion, sales that are widely used by Indonesians. Tart Tuns has difficulties in managing orders and products because currently sales in this shop are only through Instagram, in making transactions with buyers the admin communicates via Instagram direct messages and via WhatsApp messenger. This transaction process is less efficient because the Tart Tuns cake shop must always update information about product availability on insta story. This research aims to address the existing needs of the store in the form of a website that can be used as a sales medium to be more structured and expand product offerings not limited to the store's Instagram account by utilizing the MVC architecture of the Laravel Framework and collaborative filtering algorithms. System modeling uses UML (Unified Modeling Languange) and the research method used is Design Science Research (DSR). The results of this study are to produce a sales website software at Tart Tuns using the Laravel framework which functions to help shop owners in making it easier to manage orders, products, transactions, and product categories. Suggestions from this research are that other developers can add / develop features such as product reviews, chat on the website, and product liking features.

Matrix Factorization Recommendation Algorithm Based on Attention Interaction

Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item.

The Growth of Contemporary Music Subject and the Reform of Music Teaching in Universities

In order to promote the growth of contemporary music and the reform of music, this article designs an improved collaborative filtering (CF) algorithm to solve the problem of sparse matrix in traditional recommendation algorithms. The data matrix is dimensionally reduced to find the nearest neighbor, so as to realize personalized recommendation of music teaching resources in colleges and universities. The test results show that the accuracy of the proposed teaching resource recommendation algorithm is improved by 22.56% compared with the traditional CF algorithm. The improved CF algorithm can provide more accurate prediction, and the recommendation effect of the improved algorithm is better than the original algorithm, which can effectively avoid the sparse matrix problem faced by the CF algorithm, and provide technical support for the development of contemporary music discipline and the reform of music discipline.

Retracted: Analysis of Brand Visual Design Based on Collaborative Filtering Algorithm

Retracted: Analysis of Brand Visual Design Based on Collaborative Filtering Algorithm

Retracted: Construction and Application of Music Audio Database Based on Collaborative Filtering Algorithm

Retracted: Construction and Application of Music Audio Database Based on Collaborative Filtering Algorithm

Retracted: Research on Tourism Route Recommendation Strategy Based on Convolutional Neural Network and Collaborative Filtering Algorithm

Retracted: Research on Tourism Route Recommendation Strategy Based on Convolutional Neural Network and Collaborative Filtering Algorithm

Music Therapy Music Selection Based on Big Data Analysis

Abstract Music, as a carrier of emotional sustenance, can not only achieve spiritual resonance in the process of listening but also reflect the vitality of life in the rhythm of music. As an effective relief method, music intervention has far-reaching significance in the development of psychotherapy. In the practice of music therapy, music selection is particularly important, and the rationality and rigor of music selection directly affect the therapeutic effect. The process of music selection in music therapy is in common with Internet music recommendation, so it has theoretical and practical value to apply personalized music recommendation algorithms to music therapy. In this paper, driven by big data, a music recommendation model based on an improved collaborative filtering (CF) algorithm is proposed, which combines the psychological adjustment of users’ music preferences and different music rhythm features to select music for music therapy and provides theoretical support for music therapy selection. The results show that the construction of music resources for music therapy based on an improved CF algorithm can greatly improve the music selection process of music therapy.

Correlation Analysis and Teaching Optimization of University English Lecture Mode Preferences

Abstract The article first discusses the categorization and difficulty of learning resources and proposes a method to quantify the difficulty of teaching resources. Then, the article recommends appropriate learning resources through similarity matching and student knowledge level diagnosis. This includes analyzing students’ test answers and modeling students’ knowledge level based on this. Data analysis and model building were employed to propose four key indicators: knowledge point density, knowledge point depth, evaluation index and correctness rate. In addition, cosine distance was employed to measure the similarity between resources and Hidden Markov Models were used to predict students’ knowledge point mastery. Four key indicators were effectively applied to construct a vector of resource difficulty indicators in quantifying learning resource difficulty. A vector representation of students’ knowledge mastery was successfully built in diagnosing students’ knowledge level. In addition, the article discusses the construction of a personalized learner preference model and a personalized English teaching model based on collaborative filtering algorithms. The effect of college English teaching can be effectively improved through the detailed Analysis of learning resources and the application of personalized recommendation model.

Implementation and Application of Employment Recommendation System for College Students with Personalized Preferences

Abstract With the popularization of higher education, the competition in the employment market of college students is becoming increasingly intense. To enhance the employment efficiency and satisfaction of college students. The study first analyzes college students’ employment unit selection, attribute preference, and location preference through an employment recommendation algorithm. The collaborative filtering algorithm is utilized to complete personalized modeling and output the final recommendation results based on the acquired employment preferences and relevant data collection. Finally, the gradient descent method is used to evaluate the accuracy of college students’ employment recommendations. The results show that the overall educational requirements of enterprises for the three significant positions of short video production, account operation and anchor are not high, and the percentage of those with education of high school or below or master’s degree or above is meager, neither exceeding 3.5%. The personalized employment recommendation algorithm can provide scientific and reasonable guidance for graduates’ employment, with an accuracy rate of up to 50% and a recommendation list length of N=30. When α=0.75, the personalized employment recommendation algorithm can obtain better recommendation performance with smaller recommendation list length. This paper provides new solutions for college students’ employment and valuable references and lessons for research in related fields.

Hybrid Recommender Systems based on Content Feature Relationship

Recommendation systems get ever-increasing importance due to their applications in both academia and industry. The most popular type of these systems, known as collaborative filtering algorithms, employ user-item interactions to perform the recommendation tasks. With growth of additional information sources other than the rating (or purchase) history of users on items, such as item descriptions and social media information, further extensions of these systems have been proposed, known as hybrid recommendation algorithms. Hybrid recommenders use both user-item interaction data and their contextual information. In this work, we propose new hybrid recommender algorithms by considering the relationship between content features. This relationship is embedded into the hybrid recommenders to improve their accuracy. We first introduce a novel method to extract the content feature relationship matrix, and then the collaborative filtering recommender is modified such that this relationship matrix can be effectively integrated within the algorithm. The proposed algorithm can better deal with the cold-start problem than the state-of-art algorithms. We also propose a novel content-based hybrid recommender system. Our experiments on a benchmark movie dataset show that the proposed approach significantly improves the accuracy of the system, while resulting in satisfactory performance in terms of novelty and diversity of the recommendation lists.

An Innovative Demonstration of Functional Linguistic Ideas for Teaching English in Colleges and Universities Based on Multi-objective Optimization Models

Abstract Aiming at the current characteristics of English teaching in colleges and universities which are too theoretical, this paper combines the ideas of functional linguistics to innovate the teaching method, focusing on constructing a personalized English teaching resources recommendation system to assist English teaching. Among them, in the teaching resources recommendation module, the collaborative filtering algorithm is improved by calculating the learner trust degree and similarity degree, then describing the learner features and learning resources features respectively, constructing the multi-objective optimization function, and solving the optimal value by using the hybrid differential evolutionary algorithm. The constructed English teaching resources recommendation system is applied to actual teaching. For the answer situation of knowledge difficulty category 5, the highest level students can still maintain the average correct rate of 55.21%. In the comparison experiment of innovation effect demonstration, the experimental group and the control group reached statistical significance in fluency index (P=0.038<0.05) and logic index (P=0.001<0.05). This study can promote the practicability of English teaching and increase the effect of cultivating applied talents.

Development Path of Tea Culture and Aesthetic Education Integration in Higher Vocational Colleges and Universities Based on Collaborative Filtering Algorithm

Abstract Tea culture is an excellent traditional culture in China, and its integration into higher vocational aesthetic education can promote the overall development of students. This paper takes the higher vocational colleges and universities in H city as an example, constructs the correlation index of the integration and development of tea culture and aesthetic education, and explores the key factors of the integration and development of tea culture and aesthetic education by combining the gray correlation method. At the same time, the students’ portraits are constructed, the constructed user portraits are clustered using the K-mean algorithm, combined with the improved collaborative filtering algorithm, and the resources related to tea culture and aesthetic education in higher vocational colleges and universities are accurately recommended. The improved model is also applied to the city to analyze the resource recommendation effect of tea culture and aesthetic education and explore the development path of integration of tea culture and aesthetic education in higher vocational colleges. The results show that the gray correlation between the integration of tea culture and aesthetic education in higher vocational colleges and universities, and the educational foundation, educational level, student ability, and educational needs are within (0.642, 0.808). The similarity between the users recommending aesthetic education resources is above 0.7, the similarity between the users recommending tea culture and aesthetic education resources and the users recommending tea culture resources is above 0.8, and the content recommended by each user achieves the desired effect (100%). The effective promotion of tea culture and aesthetic education in higher vocational colleges and universities is achieved through the accurate recommendation of relevant resources for students’ needs.

Current Situation and Practical Path of Cultivating Students’ Core Literacy in the Context of the New Era

Abstract In this paper, Bayesian estimation and Coleman filtering are used to compose a multidimensional data fusion algorithm to study the cultivation of students’ personalized core literacy. A Trans E model is constructed based on the calculation of students’ specific course ratings using collaborative filtering algorithms. Combining the knowledge graph and collaborative filtering methods, the similarities between the course and user ratings are calculated respectively. The user’s rating situation can be predicted by combining similarity. Through the empirical analysis method, the overall and specific competency dimensions of students’ core literacy and the effect of core literacy cultivation, as well as students’ satisfaction, were analyzed, and the corresponding practice paths were proposed based on the analysis results. The mean value of students’ core literacy test is 46.25, and the number of students scoring between 50-60 points is the largest, but the number of excellent students is 0, which is poor in the cultivation of core literacy. The analysis of the effect of core literacy cultivation shows that in the correlation analysis of teaching effect and teaching support, the p-value is less than 0.01, but most of the correlation coefficients are less than 0.35, and there exists a weak relationship between the two, and the school authorities should strengthen the teaching support of core literacy.

Personalized route recommendation for passengers in urban rail transit based on collaborative filtering algorithm

AbstractThe rapid advancements in information technology and intelligent systems within urban rail transit (URT) systems have highlighted the need for more personalized route recommendations that consider passengers’ travel habits. This study aims to address this issue by investigating passenger travel routes alongside other passengers who share similar travel preferences, utilizing collaborative filtering (CF) techniques. The approach involves analyzing historical card data to assess passenger travel profiles, including actual travel time under crowded conditions. By considering both individual passenger preferences and the preferences of similar passengers, a CF algorithm is employed to offer personalized route recommendations. The Shenzhen metro is used as a case study to illustrate the proposed method. The results demonstrate that the proposed approach surpasses traditional route recommendation methods by providing tailored suggestions that align more closely with passengers’ travel preferences. These findings emphasize the value of incorporating passenger travel preferences into route recommendation models, thereby enhancing the accuracy and effectiveness of personalized route recommendations within URT systems.

Retracted: Design of Mobile English Teaching Platform Based on Collaborative Filtering Algorithm.

[This retracts the article DOI: 10.1155/2022/7566896.].