Traditional Recommendation Algorithm Research Articles

RWESA-GNNR: Fusing Random Walk Embedding and Sentiment Analysis for Graph Neural Network Recommendation

A graph neural network-based recommendation system treats the relationship between user items as a graph, and achieves deep feature mining by modelling the graph nodes. However, the complexity of the features of graph neural network-based recommendation systems brings poor interpretability and suffers from data sparsity problems. To address the above problems, a graph convolutional neural network recommendation model (RWESA-GNNR) based on random walk embedding combined with sentiment analysis is proposed. Firstly, a random walk-based matrix factorization is designed as the initial embedding. Secondly, the user and item nodes are modelled using a convolutional neural network with an injected attention mechanism. Then, sentiment analysis is performed on the review text, and attention mechanism is introduced to fuse text sentiment features and semantic features. Finally, node features and text features are aggregated to generate recommendation results. The experimental results show that our proposed algorithm outperforms traditional recommendation algorithms and other graph neural network-based recommendation algorithms in terms of recommendation results, with an improvement of about 2.43%-5.75%.

A collaborative filtering method by fusion of facial information features

Personalized recommendation systems fundamentally assess user preferences as a reflection of their emotional responses to items. Traditional recommendation algorithms, focusing primarily on numerical processing, often overlook emotional factors, leading to reduced accuracy and limited application scenarios. This paper introduces a collaborative filtering recommendation method that integrates features of facial information, derived from emotions extracted from such data. Upon user authorization for camera usage, the system captures facial information features. Owing to the diversity in facial information, deep learning methods classify these features, employing the classification results as emotional labels. This approach calculates the similarity between emotional and item labels, reducing the ambiguity inherent in facial information features. The fusion process of facial information takes into account the user’s emotional state prior to item interaction, which might influence the emotions generated during the interaction. Variance is utilized to measure emotional fluctuations, thereby circumventing misjudgments caused by sustained non-interactive emotions. In selecting the nearest neighboring users, the method considers not only the similarity in user ratings but also in their emotional responses. Tests conducted using the Movielens dataset reveal that the proposed method, modeling facial features, more effectively aligns recommendations with user preferences and significantly enhances the algorithm’s performance.

Recommendation Approach Based on TwinBERT Model

In order to provide more personalized recommendation services for users as well as improve the platform's traffic and user satisfaction, a recommendation method based on the TwinBERT model has been proposed. As a matter of fact, this method combines not only the basic information of users and items but also user comments or item text information to predict users' behaviors and make more accurate recommendations. The text information in the dataset is encoded in sentences using the BERT model, and the encoded text features are fused with other features to obtain a representation of user and item embedding vectors. These vectors are then used to predict the user's rating of the item and recommend relevant items for the user. The proposed method was tested on the Amazon review dataset and the MovieLens dataset, and the experimental results showed that it improved over traditional recommendation algorithms in terms of accuracy and efficiency.

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.

An Empirical Study on Personalized Product Recommendation Based on Cross-Border E-Commerce Customer Data Analysis

Thanks to the rapid growth of cross-border e-commerce platforms, numerous cross-border items are now available to customers. Several serious issues with cross-border e-commerce platforms related to item promotion and consumer product screening have arisen. Particular importance should be placed on studying and implementing personalized recommendation systems based on international e-commerce. In light of the quick expansion of commodities, when making individualized suggestions, traditional recommendation algorithms have had to deal with issues such as scant data, a chilly start to the market, and trouble identifying user preferences. To automatically mine the implicit and latent relationships between users and objects in recommendation systems, this study employs deep learning with nonlinear learning capabilities, which resolves the challenges of user interest mining. The weaknesses of the existing global recommendation research are emphasized, the study of conventional recommendation algorithms mixed with deep learning technology is deep factorization machine (DeepFM) and neural matrix factorization (NeuMF) models. Both models excel in recommending implicit feedback data. The DeepFM model yields the lowest loss function values, while the NeuMF model outperforms the competing models in terms of HR@20 (a commonly used indicator to measure the recall rate) and loss functions. In summary, this research addresses critical issues in cross-border e-commerce by developing personalized recommendation systems and integrating deep learning with traditional recommendation algorithms to enhance global recommendations.

Investigating the Use of Deep Learning and Implicit Feedback in K12 Educational Recommender Systems

Recommender systems are increasingly being used in university or online education. However, recommender systems still have not found major usage in K12 education. This may be because of unique challenges that recommender systems face when used by a young and diverse population. Regardless, recommender systems for K12 education could provide many benefits for students and teachers, such as the simplification of personalized learning. Some of the issues with K12 educational recommender systems may be solved by the use of deep learning and implicit feedback. As such, we investigated the use of deep recommendation algorithms and implicit feedback for K12 educational recommender systems. To do this, we compared metrics for highly cited traditional and deep recommendation algorithms trained on explicit and implicit data. We found that recommendation algorithms using deep learning as a group do not differ in performance compared to traditional recommendation algorithms. We also found that the use of implicit feedback led to higher performance than using explicit feedback. The best performing algorithm used both deep learning and implicit feedback. We conclude that the deep learning can be a benefit for K12 recommender systems, particularly when the ordered sequence of items is carefully accounted for. We also conclude that researchers and developers must carefully consider which feedback contributes the most information for learning.

Research on Intelligent Recommendation of Computer Course Items Combining Augmented Learning Algorithms and Data Mining Techniques

Abstract This paper combines augmented learning algorithms and data mining techniques for an intelligent recommendation system for computer course projects to optimize the allocation of learning resources and improve learning efficiency and effectiveness. The research methodology includes an improved association rule mining algorithm and an augmented learning model applying deep Q-network and adaptive reward function. The research results show that the system can effectively mine knowledge point associations and achieve personalized recommendation. In particular, the accuracy of knowledge point associations and the customized level of recommendation are improved by multi-support strategy improvement and knowledge point association correction. Experiments show that on the edX dataset, the recommendation recall reaches 0.347 at k=10 and 0.433 at k=20, significantly better than the traditional recommendation algorithm. This intelligent recommendation system can effectively improve the learning effect of computer course projects, and it has a positive impact on both learning attitude and independent learning ability, especially helping the backward students significantly and reducing the learning differences between classes.

An E-Commerce Personalized Recommendation Algorithm Based on Multiple Social Relationships

Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.

Efficient Course Recommendation using Deep Transformer based Ensembled Attention Model

The exponential development of online learning resources has led to an information overload problem. Therefore, recommender systems play a crucial role in E-learning to provide learners with personalised course recommendations by automatically identifying their preferences. In addition, e-Learning platforms such as MOOCs and LMS have been criticised for their low course completion rates, and one of the primary reasons is that they do not provide personalised course recommendations for users with varying interests. Rapidly locating the courses that users are interested in on enormous e-Learning platforms can have a significant impact on the quality of learning and the dissemination of knowledge to the learner. This paper examines the most prevalent recommendation techniques utilised in E-learning. We examined how to apply Deep Transformer based Ensembled Attention Model (DTEAM) on e-Learning system in order to achieve personalized course recommendations. The proposed recommendation model uses BERT as its foundation integrated MLM and Transformers. Predicted course recommendations are more aligned with the interests of users. Our experimental results proved that traditional recommendation algorithms, such as collaborative filtering and item-based filtering are incapable of producing superior results. The consequence of the research can assist students in selecting courses according to their preferences and improve their learning caliber

The Application of Digital Multimedia Technology in the Innovative Mode of English Teaching in institutions of higher education

Abstract Innovative English teaching resources will inevitably result from the use of media technology in the classroom. This article will examine the new approach to teaching English in college from the standpoint of teaching resources. In order to establish a new mode of English teaching in higher education institutions based on personalized recommendation of teaching resources, this paper proposes two recommendation algorithms: one is content-based, and the other is collaborative filtering. The algorithm is enhanced by combining it with the design of multimedia English teaching resources. The influence of hit rate and MAE values prior to and following the cooperative filtering recommendation method is examined. The distinction between the pre-and post-test results of the experimental and control courses is analyzed and compared to conclude the examination of the impact of the novel model of teaching college English. The analysis indicates that the improved recommendation algorithm has a 4.85% higher hit rate than the traditional collaborative filtering algorithm. The improved recommendation algorithm is more accurate than the traditional recommendation algorithm, as suggested by this.

Optimization of intelligent recommendation of innovation and entrepreneurship projects based on collaborative filtering algorithm

The advent of the information age has made accurate search for information a challenge. In this paper, we analyze intelligent recommendations for innovative entrepreneurial projects based on collaborative filtering algorithms. Collaborative filtering is one of the most widely used and successful techniques in recommendation systems. In this paper, an interest migration function plus time is introduced to address the shortcomings of traditional collaborative filtering recommendation algorithms. Meanwhile, this paper builds an intelligent recommendation engine system for innovative entrepreneurial projects based on the Hadoop open-source distributed computing framework, sustainable PSCM, and Mahout collaborative filtering recommendation engine technology. This paper uses experiments to test and evaluate the overall performance of the distributed recommendation platform and the improved collaborative filtering recommendation algorithm. It is found that the algorithm outperforms similar algorithms in terms of data volume and coverage of recommended innovation and entrepreneurship projects. This is sufficient to show that the collaborative filtering algorithm and sustainable PSCM are useful for the intelligent recommendation analysis of innovative entrepreneurial projects.

Convolutional neural network and recommendation algorithm for the new model of college music education

With the rapid development of the field of artificial intelligence, we expect that AI+ music can produce a new music education model to help efficient college students improve their personal music level. In this paper, we design a convolutional neural network music recommendation system, including a user modeling module, audio feature extraction module recommendation algorithm module, etc., which can model students' music preferences to generate Top recommendations for target users. It is helpful for teachers to timely and dynamically grasp the types of music that students like. Experiments show that the proposed method has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, we can make full use of the powerful advantages of deep neural network automatic feature extraction and integrate the historical behavior information of users' interaction with music.

Analysis of the fusion development of traditional culture concept and interior art design based on 3D scene analysis

Abstract In this paper, by collecting a large number of 3D scenes with traditional cultural styles, after pre-processing, the style analysis task of 3D scenes is realized through two stages of model style element discovery and scene style pattern mining. In the style pattern mining stage, the maximum frequent pattern mining algorithm is used to get the style pattern of the final scene, and the part is visualized. To reduce the user’s selection time, a 3D interior layout recommendation system based on style rule mining is designed using collaborative filtering. Based on the previous work on scene style pattern mining, the style association rules are further mined, and the user is provided with candidate layouts and furniture dynamically and in real-time by introducing user interaction. Comparing the text-improved recommendation algorithm with the traditional recommendation algorithm, the accuracy rate of the traditional algorithm starts to saturate to 65% after TopN>20, while the algorithm in this paper is higher than 80%. In the analysis of traditional cultural contexts of interior art, in terms of the aesthetic acquisition, the highest proportion of aesthetic association is 63.7%. In terms of emotional expression, the proportion of symbolization of emotion is 73.1%. In terms of object comparison, the highest proportion of plurality and flexibility of combinations was 57.5%. Therefore, this paper provides good research ideas for how to better integrate traditional culture into interior design.

GNN-based deep reinforcement learning for MBD product model recommendation

ABSTRACT Digital twin is more and more widely used, and the delivery demand of digital twin is more and more prominent at the same time of product physical delivery. Research on the digital twin product model recommendation method is of great significance for the rapid construction and reuse of digital twins. The methods currently in use, however, principally concentrate on geometric reuse and pay little attention to functional or knowledge reuse. In this paper, a graph neural network (GNN)-based deep reinforcement learning (DRL) for product model recommendation is presented. First, an MBD (model-based definition)-based semantic feature attribute adjacency graph (MSFAAG) is introduced to structured MBD model as the carrier of the digital twin product model. The MSFAAG is then embedded into continuous vector spaces using a GNN to obtain the categorization of these MBD models. Finally, DRL is used to adaptively identify more important semantic features, including manufacturing semantics and functional semantics, to obtain more detailed model classification results. The experiment effectively improves the reuse efficiency of the non-geometric aspects of the digital twin product and MBD model. Compared with other traditional recommendation algorithms, the algorithm proposed in this paper has higher accuracy and can well meet the design requirements of users.

The application of friend recommendation algorithm in the design of traditional handicraft art communication platform

Abstract Cultural awareness is a context in which the integration and development of traditional arts and crafts can be promoted by establishing an exchange platform. In this paper, the exchange platform is designed through the exploration of traditional handicraft art resources and the study of different classifications. The main points of the platform design and key operational processes are explored based on the analysis of the users’ needs for the traditional handicraft art platform. In response to the lack of accuracy of the traditional buddy recommendation algorithm, a buddy recommendation algorithm incorporating user interests was proposed based on big data to carve out different user preferences for traditional arts and crafts. The user interaction response rate of the platform was good, with TPS of 98.7, 137.4 and 75.6 on the sending side, service side and receiving side, respectively, in stable operation. The friend recommendation algorithm incorporating user interests can effectively dig out user groups with similar interests, which is optimized by 11.83% compared to the traditional friend recommendation algorithm. The exchange platform supported by big data, based on friend recommendations to strengthen user interaction, can effectively promote the exchange and integration of traditional handicraft arts.

A heterogeneous graph neural recommendation model with hierarchical social trust

In traditional social recommendation algorithms, methods based on Graph Neural Networks (GNNs) representation of social networks are widely used, but the increasing sparsity of data and the tendency of users’ social influence to be complex make it difficult for the algorithms to improve the recommendation quality efficiently. To address the issues, a heterogeneous graph neural recommendation model with hierarchical social trust (GraphNON) is proposed. The algorithm is improved by fully exploiting user-friend relationships and modeling different propagation channels of social influence in multiple ways respectively. Firstly, different types of potential friends are mined based on user-user social graph and user-item rating graph respectively. Different dynamic influences of direct friends and indirectly friends among users are modeled through hierarchical influence propagation. Secondly, associations between items are mined from user-item rating graph, and influence transmission among items is modeled through hierarchical influence propagation. Finally, updated user characteristics and item characteristics are used to predict whether a user is interested in an item or not. It is shown on both datasets that the model has significant improvement in both hit rate (HR) and normalized discounted cumulative gain (NDCG) metrics compared to the optimal benchmark model. For a potential feature dimension of 64, the HR of the Yelp dataset improved by 18.3% and the NDCG by 29.3%, while the HR of the Flickr dataset improved by 31.4% and the NDCG by 37.1%.

Research on Personalized Recommendation Algorithm of Internet Platform Goods Based on Knowledge Graph

Personalized recommendation method is an effective means to filter out the information users need from a large amount of information, which is rich in practical value. Personalized recommendation methods are maturing, and many e-commerce platforms have been using different forms of recommendation methods with great success. In the recommendation systems of large-scale e-commerce platforms, traditional recommendation algorithms represented by collaborative filtering are modeled only based on users' rating data, and sparse user-project interaction data and cold start are two inevitable problems. The introduction of knowledge graphs in recommendation systems can effectively solve these problems because of their rich knowledge content and powerful relationship processing capability. In this paper, we study the personalized recommendation algorithm based on knowledge graph as auxiliary information, and use the temporal information of user-item interaction in the graph to model users' interests over time at a finer granularity, taking into account the problem of high training time cost of the model due to frequent updates of the knowledge graph when recommending to users dynamically. The article proposes the Interactive Knowledge-Aware Attention Network Algorithmic Model for Recommendations (IKANAM) and conducts comparison experiments on public datasets. The results show that the IKANAM recommendation algorithm can effectively improve the effectiveness of personalized recommendation of products on Internet platforms.

Individualized Movie Recommendation Algorithm Based on Machine Learning

With the continuous acceleration of economic growth and the remarkable improvement of people's living standards, people's individualized needs have increased. Moreover, due to the growth of computer technology and network technology, many forms of entertainment and services have been integrated into the network. The recommendation engine actively pushes the information that meets the user's taste and preference to the user by analyzing the user's behavior data on the website when the user's needs are not clear. This article will study how to apply the deep learning algorithm to the field of movie recommendation system to solve the problems existing in the traditional recommendation algorithm based on machine learning (ML), and combine the existing mainstream big data (BD) to improve the execution efficiency of the model. Individualized movie recommendation system saves users the time of searching, viewing and screening, and discovers users' potential movie preferences at the same time. Most users think that the algorithm can help them find the type they want from a large quantity of film and television resources, so as to meet the needs of users and tap their potential interests.

Personalized movie recommendations based on deep representation learning.

Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and the user-item rating matrix is maximized. Then softmax regression is used to learn multiple categories in the feature space to predict the probability of interaction between users and items. Finally, the method is applied to the area of movie recommendation. The key to this method is the negative sampling mechanism, which greatly improves the effectiveness of the recommendations, as a result, creates an accurate list of recommendations. This method was verified and evaluated on Douban and several movielens datasets of different sizes. The experimental results demonstrate that the recommended performance of this model, which has high accuracy and generalization ability, is much better than typical baseline models such as singular value decomposition (SVD), and the mean absolute error (MAE) value is 98%, which is lower than the best baseline model.

De-Selection Bias Recommendation Algorithm Based on Propensity Score Estimation

There are various biases present in recommendation systems, and recommendation results that do not consider these biases are unfair to users, items, and platforms. To address the problem of selection bias in recommendation systems, in this study, the propensity score was utilized to mitigate this bias. A selection bias propensity score estimation method (SPE) was developed, which takes into account both user and item information. This method accurately estimates the user’s choice tendency by calculating the degree of difference between the user’s selection rate and the selected preference of the item. Subsequently, the SPE method was combined with the traditional matrix decomposition-based recommendation algorithms, such as the latent semantic model (LFM) and the bias singular value model (BiasSVD). The propensity score was then inversely weighted into the loss function, creating a recommendation model that effectively eliminated selection bias. The experiments were carried out on the public dataset MovieLens, and root mean square error (RMSE) and mean absolute error (MAE) were selected as evaluation indicators and compared with two baseline models and three models with other propensity score estimation methods. Overall, the experimental results demonstrate that the model combined with SPE achieves a minimum increase of 2.00% in RMSE and 2.97% in MAE compared to its baseline model. Moreover, in comparison to other propensity score estimation methods, the SPE method effectively eliminates selection bias in the scoring data, thereby enhancing the performance of the recommendation model.