Collaborative Recommendation Algorithm Research Articles

Weight prediction and recognition of latent subject terms based on the fusion of explicit & implicit information about keyword

Weight prediction and recognition of subject term in documents is widely used in application fields such as literature recommendation and keyword retrieval. Unlike traditional methods which only focus on searching for subject terms from existing keywords in documents, this paper proposes a recognition method of latent subject term outside documents based on a one-class collaborative filtering algorithm that fuses explicit and implicit information. A matrix factorization model based on the analysis of document activity and subject term popularity is constructed to measure the correlation probability between documents and subject terms that do not appear in the current document. After these subject terms being divided into Latent Subject Terms (LST) and Irrelevant Subject Terms (IST), two methods for predicting the weight of these types of subject terms is introduced, which include Hybrid Filling with Preference Coefficients (HFPC), and Zero Filling. In order to verify the effectiveness of subject term recognition, many collaborative filtering recommendation algorithms are conducted with the filled document keyword matrix. On the basis of not changing these algorithms, MAE and FCP can be improved by 28.01% and 22.79% at most, while P@N and NDCG@N can be improved by 22.37% and 27.06%, respectively.

Research on the innovation of ideological and political education for college students based on the Civic and Political Science Cloud Education Platform

Abstract Innovative ideological and political education for college students is to help better students improve their ideological quality and political literacy. In this paper, under the background of big data, the Civic and Political Cloud Education Platform is constructed using big data technology, which is mainly composed of four main parts: model layer, view layer, control layer, and basic services. Student behavior data is collected and analyzed through a collaborative filtering recommendation algorithm under big data collection and analysis technology, and performance simulation analysis is conducted for the Civic and Political Science Cloud Education Platform. From the performance analysis, when the number of concurrent users of the Civic Education Cloud Platform is 80-120, the average transaction processing per second is stable at 10.5 s. From the practicality, the Civic Education Cloud Platform built by big data can effectively explore the value of Civic Education teaching resources. This shows that the Civic and Political Science cloud education platform is an effective way to innovate and develop the ideological and political education of college students, and through this platform, the ideological quality and political literacy of college students can be effectively improved.

A New Collaborative Filtering Recommendation Algorithm for Course Resources Based on Language-Scoring Data

As digital tools and online resources are widely used in education these days, providing students with personalized learning resource recommendations is now an important task for educators- however, the currently available recommendation algorithms created based on language-scoring data are deficient in aspects of sentence similarity calculation and students’ acceptance of new knowledge, so this study proposed an automatic language-scoring method based on the improved sentence similarity calculation; then, combining with the language-scoring data of students, a new collaborative filtering recommendation algorithm of course resources was proposed for changes in the effect of students’ acceptance of new knowledge. The proposed new algorithm can more accurately evaluate students’ language ability, with students’ interests and capacity taken into consideration, and it can give more suitable and personalized recommendations of learning resources for students. In terms of algorithm design, both efficiency and accuracy have been taken into account and balanced, so the algorithm is applicable to large-scale data processing and real-time recommendation. This study not only helps in improving students’ learning effect, but also provides useful references for educational institutions to optimize course design and teaching methods. Research findings of this study can be used in other disciplines and fields as well.

The impact of modern Internet technology on the teaching of American voice in colleges and universities

Abstract In this paper, firstly, the average prediction rating of interest points is performed by a recommendation model incorporating multiple factors through probabilistic matrix decomposition to improve the accuracy of the beauty teaching features obtained by matrix decomposition. Then, we combine the collaborative filtering recommendation algorithm and propose a recommendation model called TGSS-MF, and optimize the TGSS-MF recommendation model through the model of neural network for the sparse data problem faced by the interest point recommendation and the hidden feature vector representation problem of users and interest points, and finally use the TGSS-MF recommendation model to analyze the user needs of teachers, students and system administrators who are involved in teaching and learning. Finally, the TGSS-MF recommendation model is used to analyze the needs of users such as teachers, students, and system administrators involved in teaching and learning. A mobile teaching platform is designed to meet the characteristics of American voice teaching in colleges and universities. The performance analysis of the TGSS-MF recommendation model shows that when k=10, the accuracy and recall of the TGSS-MF model in the two data sets are 0.095 and 0.113, respectively, which are better than the other three algorithms in both accuracy and recall. This study can present more rich resources to students through modern Internet technology, which can help students learn effectiveness.

Retracted: Intelligent Sports Auxiliary Training Method Based on Collaborative Filtering Recommendation Algorithm

Retracted: Intelligent Sports Auxiliary Training Method Based on Collaborative Filtering Recommendation Algorithm

Analysis of media content recommendation in the new media era considering scene clustering algorithm

Abstract With the advent of the new media era, we face the problem of information overload every day, based on which this paper proposes a scenario-conscious clustering algorithm for recommending media content. The improved K-means algorithm is used to cluster the media content, an initial cluster center is randomly selected, and the remaining initial cluster centers are obtained by executing the improved algorithm to reduce the number of iterations and avoid neighboring situations. The clustering algorithm is then compared with content-based recommendation techniques, KNN recommendation algorithm, and collaborative filtering recommendation algorithm in terms of accuracy, recall, MAE value, and execution time. The clustering algorithm is significantly better than the other algorithms regarding accuracy and recall. The accuracy of the clustering algorithm is 0.4 when the recommendation sequence is 30, which is 0.4 higher than the collaborative filtering technique, 0.1 higher than the KNN, and 0.12 higher than the content recommendation method. In terms of MAE value, the clustering algorithm outperforms the other algorithms when the number of nearest neighbors is selected to be above 20. In terms of execution time, the longer the amount of data, the more obvious the advantage of the clustering algorithm. Therefore, the applicability and reliability of the model proposed in this paper for media content recommendation are verified in terms of accuracy, recall and execution time, which meet the design requirements.

A movie recommendation method based on knowledge graph and time series

Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model.

Rural tourism service design based on collaborative filtering algorithm after epidemic normalization

Abstract In the epidemic, normalized tertiary and rural tourism service industries are in the economic depression stage. Under the epidemic, combined with big data technology to improve the economic income and development scale of rural tourism services become the current development trend of the tourism industry. This paper first proposes a collaborative filtering recommendation algorithm based on big data technology to study the design of rural tourism services under the new epidemic normalization. Then the basic principle of the content-based recommendation algorithm is to obtain the interests of tourists based on their historical behaviors and recommend rural tourism similar to their interest preferences, and choosing the appropriate similarity function can improve the accuracy of the neighborhood-based CF method. Finally, to meet the tourists’ demand for a full range of tourism experience services and build a rural tourism service system, the psychological demand of rural tourism tourists’ consumption is analyzed based on a collaborative recommendation algorithm. The results show that among the main factors attracting tourists, 75.54% are natural scenery, 54.68% are folk culture, 51.08% are unique flavors and food, 43.17 are experiencing rural life, and 41.73% are promoting relationships with friends. This study plays an important role in accelerating rural revitalization by attracting urban tourists back to the countryside and driving the transfer of consumer groups and the rural economy to increase income; thus, rural tourism plays an important role in accelerating rural revitalization.

Research on personalized recommendation method of preschool e-learning resources

Abstract E-learning is a very popular learning method at this stage, and Internet learners need to invest much time to retrieve the required e-learning network resources. In building the existing e-learning resources, the cognitive level, thinking ability, learning style, and other factors of learners are not considered. For this challenge, the paper gives the recommendation methods of the collaborative filtering algorithm model and learner model, compares the advantages and disadvantages of these models and traditional recommendation methods using the data of mean square error, response time, and accuracy, and examines the students’ suggestions for this recommendation method in the field. The collaborative filtering recommendation technique had the highest completion rate, above 0.7 for 5-20 recommendations and above 0.75 and 0.8 for 20-35. The rate of the learner model is stable between 0.7 and 0.8. In contrast, the detection rate of traditional methods is between 0.6 and 0.7. The collaborative filtering recommendation method outperforms the other two methods regarding completeness, response time, accuracy, and F1 value. In terms of satisfaction, the collaborative filtering recommendation algorithm had the highest satisfaction rate, followed by the learner model; the collaborative filtering method had the best results in improving learning performance. Therefore, personalized recommendation methods can improve learning efficiency and reduce retrieval time, and the collaborative filtering recommendation method is the most effective among the two methods proposed in this paper.

Research on User Profile Combined with Collaborative Filtering Recommendation Algorithm for Intelligent Tourism

In recent years, the online travel sector in the tourism industry has experienced significant growth and popularity due to the development and widespread adoption of internet technology and smart devices. However, despite these advancements, scenic spots have struggled to provide precise services to tourists, as the online marketplace is flooded with numerous and disorganized commodity resources, lacking standardized construction and systematic management. As a result, travelers find it challenging to access specialty goods that cater to their personalized needs. To address this issue, this paper proposes the use of user profiling and collaborative filtering recommendation algorithms to achieve personalized recommendations for specialty products in scenic spots. The general process of constructing user profiles for scenic spots and combining them with collaborative filtering algorithms to create an intelligent tourism recommendation system is outlined. The paper also highlights the current challenges faced by this system in practical applications and provides future research prospects to promote accurate services in tourist attractions.

Harnessing Item Features to Enhance Recommendation Quality of Collaborative Filtering

Recommendation systems provide ways of directing users to items that may be relevant to them by guiding them to relevant items that will be suitable to the users according to their profiles. Collaborative filtering is one of the most successful and mature techniques of recommender system because of its domain independent ability. Bayesian Personalized Ranking Smart Linear Model (BPRSLIM) is model-based collaborative filtering (CF) recommendation algorithm that usually reconstructs a scanty user-item matrix directly; also, using only user-rating matrix usually prevents the algorithm from accessing relevant information that could enhance its recommendation accuracy. Therefore, this work reconstructs BPRSLIM user-item rating matrix via item feature information in order to improve its performance accuracy. Comprehensive experiments were carried out on a real-world dataset using different evaluation metrics. The performance of the model showed significant improvement in recommendation accuracy when compared with other top-N collaborative filtering-based recommendation algorithms, especially in precision and nDCG with 30.6% and 22.1% respectively.

Dual-Channel Feature Enhanced Collaborative Filtering Recommendation Algorithm

The dual-channel graph collaborative filtering recommendation algorithm (DCCF) suppresses the over-smoothing problem and overcomes the problem of expansion in local structures only in graph collaborative filtering. However, DCCF has the following problems: the fixed threshold of transfer probability leads to a decrease in filtering effect of neighborhood information; the K-means clustering algorithm is prone to trapping clustering results into local optima, resulting in incomplete global interaction graphs; and the impact of time factors on the predicted results was not considered. To solve these problems, a dual-channel feature enhanced collaborative filtering recommendation algorithm (DCFECF) is proposed. Firstly, the self-attention mechanism and weighted average method are used to calculate the threshold of neighborhood transition probability for each order in local convolutional channels; secondly, the K-means++ clustering algorithm is used to determine the clustering center in the global convolutional channel, and the fuzzy C-means clustering algorithm is used for clustering to solve the local optimal problem; then, time factor is introduced to further improve predicted results, making them more accurate. Comparative experiments using normalized discounted cumulative gain (NDCG) and recall as evaluation metrics on three publicly available datasets showed that DCFECF improved by up to 2.3% and 4.1% on two metrics compared to DCCF.

Trust Enhanced Collaborative Filtering Recommendation Algorithm

Recommender systems have been designed to suggest to users the suitable items based on the user profiles and therefore reduce the danger of information overload, however, the recommender systems are still prone to profile injection attacks which then exposes a user to a potential fraud, which leads to a sense of untrustworthiness and reduced accuracy due to malicious manipulations. In this research, we developed a model which should be embedded into the recommendation pipeline in order to improve trustworthiness of recommender system output. We extended the classical collaborative recommendation algorithm by adding a new trust parameter and then compare the prediction accuracy of the trust enhanced collaborative filtering algorithm against that of the classical collaborative filtering algorithm using Mean Absolute Error and Root Mean Square Error and then test the hypothesis using t-test. We found that the new trust parameter improves the accuracy of collaborating filtering algorithm significantly.

Visualization of data analysis platform Taking QQ music recommendation system as an example

With the rapid development of big data technology, people's demand for personalized music recommendation systems is growing more and more urgent. However, the current music recommendation system still has some problems, such as inaccurate recommendations and too slow recommendation speeds, as well as cold starts and data sparsity caused by massive data. In order to design and implement a music recommendation system for the recommendation system storage caused by the continuous increase of data, insufficient storage, and computing power, this paper improved the QQ music recommendation system based on the collaborative filtering recommendation algorithm of the offline data warehouse technology project. After testing, the music recommendation system designed in this paper has good stability, scalability, and efficiency.

A Study on the Recommendation Method of Intelligent Media Learning Resources in the Foreign Communication and Teaching of International Communication of Chinese Central Plains Culture

Today, with the development of intelligent media, the foreign communication and teaching activities of the Chinese central plains culture should actively seek experiences that can be learned from, establish a multi-channel foreign communication mode, and then promote the Chinese central plains culture to go out of the country and into the world better. The study improves the collaborative filtering recommendation algorithm and the joint matrix decomposition algorithm based on the theory of migration learning, aiming to improve the learning to optimize the resource recommendation system by calculating the user similarity and establishing the user preference-resource feature matrix. The experimental results show that the average absolute error and root mean square error of the improved algorithms are lower than those of other algorithms, proving that the optimized algorithms improve the accuracy and efficiency of resource recommendation in the foreign communication and teaching activities of the Chinese central plains culture while operating stably and with wide applicability on the recommendation system.

Research on personalized recommendation algorithm for micromechanical sensors based on cloud model

Abstract In order to improve the ability of micromechanical sensors to update data-matching nodes in real-time and speed up the establishment of a neighbor relationship between nodes, this paper proposes a personalized recommendation algorithm for micromechanical sensors based on the cloud model to improve the stability and real-time performance of micromechanical sensors. The algorithm uses the service attribute values of the cloud computing model and cloud clustering method to set feature term labels and establish a cloud service similarity matrix so as to meet the user-matched manufacturing service requirements. The intra-class clustering technique is applied to measure the clustering effect, and the evaluation function of each clustering number is calculated on the basis of considering the time sequence to determine the best clustering result to complete the personalized recommendation path for micromechanical sensors. To verify the application effect of the personalized recommendation algorithm for micromechanical sensors based on the cloud model, experiments are conducted. The results show that the recommendation algorithm in this paper can always control the node energy consumption below 2.5×103, and the average discovery delay is stable between 41-43 seconds. And the sensor response time is 12.1 seconds, and the average absolute deviation value is 0.23, which is nearly 1.3 times smaller than 0.53 and 0.52 of the collaborative recommendation algorithm and hybrid recommendation algorithm. It can be seen that the recommendation algorithm in this paper solves the problem of excessive neighbor discovery delay in the communication process of micromechanical sensors and effectively improves the personalized recommendation performance of micromechanical sensors.

Intelligent recommendation model of tourist places based on collaborative filtering and user preferences

ABSTRACT Providing personalized recommendation service for users and improving the accuracy of recommendation and user satisfaction are the main research tasks of current travel recommendation systems. The intelligent recommendation model of tourist places requires the algorithm to be able to accurately recommend tourist attractions according to the user’s interests. Big data and artificial intelligence technologies have driven the development of intelligent recommendation systems. However, realistic data are often sparse, and the lack of common rating items among users makes some traditional similarity measures impossible to calculate. In addition, traditional collaborative filtering algorithms ignore the issue of user preferences, which can cause a decrease in recommendation accuracy. To address these issues, this paper analyzes the factors affecting users’ interest preferences in terms of their global and local rating information. The interest preferences of users are calculated by computing the probability distribution of user rating information globally and using Hamming approach degree. A similarity algorithm about user preferences is derived using Jeffries-Matusita distance. The similarity algorithm is effectively combined with the traditional similarity algorithm to propose a model of collaborative filtering recommendation algorithm for tourist attractions based on user preferences under sparse data. The paper aims to improve the accuracy of tourist attraction recommendation systems by incorporating user preferences and addressing the issue of sparse data and the lack of common rating items among users that traditional similarity measures cannot calculate. The experimental results show that the improved algorithm model outperforms the traditional collaborative filtering algorithm and other algorithms. And it also has high accuracy rate on more sparse tourism data set.

A Collaborative Filtering Recommendation Algorithm Based on Community Detection and Graph Neural Network

A Collaborative Filtering Recommendation Algorithm Based on Community Detection and Graph Neural Network

Film and television art innovation in network environment by using collaborative filtering recommendation algorithm

Film and television art innovation in network environment by using collaborative filtering recommendation algorithm

An intelligent recommendation method based on multi-interest network and adversarial deep learning

Recommender systems have shown to popular in many Internet communities, as they could help users discover interesting items based on their history behaviors. However, with the explosive growth of data-intensive tasks and online information, cybersecurity risks become larger, conventional collaborative recommendation algorithms may not meet users’ security requirements. Besides, the sparsity issue and the cold-start issue also hinder the performance of conventional recommendation methods. Recently, deep learning has shown to outperform traditional modeling techniques, which can be employed in Recommender systems (RSs) to improve user behavior prediction. In light of these challenges and observations, an intelligent recommendation method based on multi-interest network and adversarial deep learning is proposed, where multi-source behavior information is applied for multi-view embedding extraction for better prediction performance. Specifically, multi-view preference embeddings, including self-embedding, interaction-aware embedding, and neighbor-based embedding, are combined to model users’ interests at a finer granularity. Besides, in neighbor-based embedding learning, an adversarial search scheme is adopted for fast similarity searching and privacy preservation. Finally, a DNN-based prediction mechanism is adopted for embedding aggregation and final prediction. Extensive experiments on real-world datasets show that our proposal achieves decent prediction performance with security concerns compared with state-of the-art baselines.