Personalized Recommendation System Research Articles

Personalized Recommendation System Based on Meta-Learning for Human-Centric Consumer Services in Gig Economy

Personalized Recommendation System Based on Meta-Learning for Human-Centric Consumer Services in Gig Economy

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.

The Impact of E-commerce and Social Media Personalized Recommendations on Consumer Behavior in the Digital Era from the Perspective of Behavioral Economics

With the digitization of consumption methods, consumers' consumption psychology will also be affected, thereby affecting their final consumption behavior. E-commerce and personalized recommendations on social media in the digital era are two important influencing factors. Therefore, studying how these two factors affect consumers' consumption concepts and behaviors through behavioral economics has theoretical and practical significance. Existing research mostly analyzes consumers' choices when making online purchases from the perspective of behavioral economics, or how a single social media platform affects consumer behavior. This article aims to combine the two and analyze through behavioral economics how shopping platforms and social media platforms affect consumer psychology and thus consumer behavior in the information age. Summarize existing research results to determine how e-commerce shopping platforms and social media affect consumption outcomes, and explain the reasons from a behavioral economics perspective. Qualitative research is conducted through questionnaire data analysis and literature review. From the perspective of behavioral economics, it is found that most people hold a positive attitude towards personalized recommendation systems, but consumers are more interested in the gap between the recommended products and their psychological expectations and the effects it makes to improve shopping efficiency and protect one's privacy. Therefore, these factors have a significant impact on consumer psychology. In order for e-commerce platforms to increase their revenue, they need to improve in these aspects and establish trust with consumers.

Personalized Sports Health Recommendation System Assisted by Q-Learning Algorithm

In response to the current problem of single sports plan and lack of long-term motivation in recommendation systems, a more intelligent personalized sports health recommendation system was designed by introducing Q-Learning (Quality Learning) algorithm. Firstly, user sports health data was collected, and the user model was constructed to track user sport preferences and historical behavior. Secondly, the sports environment was defined, including different types of sports activities, venues, and weather. Then, the reward function was formulated to reward and punish users based on their sports activities and goals, in order to maximize long-term health benefits. Finally, the Q-Learning algorithm was implemented to continuously iteratively learn and optimize user recommendation models to provide the best personalized sports recommendations. For personalized accuracy, indicators such as precision, recall, F1 value, MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) were used to evaluate, while the system’s participation in sports, user satisfaction, long-term incentive effects, and overall health improvement were collected. The results showed that the average precision of the recommendation system on 10 different datasets was 88%, and the average AUC (Area Under Curve) was 96%, which was 6.7% higher than the SVD (Singular Value Decomposition) algorithm. The user’s sports persistence rate was improved by 25%, and the health score was improved by about 13.3%. These data not only reflect the superior performance of the recommendation system but also highlight its positive impact on long-term user motivation and overall health levels. The results indicate that the proposed personalized exercise health recommendation system, assisted by the Q-Learning algorithm, has significantly improved accuracy. Moreover, it offers users more intelligent and personalized exercise suggestions, effectively increasing long-term participation in physical activities and overall health levels.

An Accuracy Study of Personalized Recommendation System for E-commerce Based on Big Data Analysis

Abstract E-commerce, as an emerging value chain model for the global economy, has greatly promoted development, while the impact of digitalization on traditional publishing enterprises is increasingly evident. In this paper, we propose a TextRank keyword extraction algorithm based on comprehensive weights, which extracts and assigns keywords that identify user information, behavior, and product characteristics. We then output a keyword weight table for user information, user behavior, and product keywords. Finally, utilizing an optimized collaborative filtering recommendation algorithm, we establish a recommendation model between the user-commodity matrix to build an e-commerce personalized recommendation system that provides users with more accurate customized recommendations. The practical application of the designed personalized recommendation system is examined to evaluate its accuracy. The MAE of this algorithm is smaller than that of user-based (0.8915, 0.9470) or item-based (0.8873, 0.9327) collaborative filtering algorithms, indicating that the improved collaborative filtering algorithm effectively enhances system recommendation accuracy. The direct effect value of recommendation strength is 0.344, with an indirect effect value of 0.018, leading to the highest overall effect value. This study provides users with convenient and attentive services, significantly enhances user experience quality, and generates substantial profits for the e-commerce platform.

Application of AI technology in personalized recommendation system for financial services

Abstract How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that financial institutions must solve. In this study, an interpretable EPRSA model for personalized financial service recommendations based on self-attention mechanisms is proposed by combining an LSTM model and an LDA topic model with AI technology support. A customized recommendation system for financial services is constructed by introducing the Nginx server into the Flask framework, and the design of the database and personalized recommendation module is interpreted. For the financial service personalized recommendation system proposed in this paper, its recommendation performance and system performance are tested, and the stock financial products are selected as the recommendation objects to explore its recommendation effect. It is found that the DNCG index of personalized recommendation of financial products of the EPRSA model is improved by 40.18%, the average response time of the system when the number of concurrent users is 1,000 is 1.96 s. The average quality of the personalized recommendation of the collection of stocks reaches 0.153. The customized recommendation of financial services using AI technology can select financial products based on the investor’s preference, help investors better understand the product returns, and improve the service quality of the financial industry.

Research on personalized recommendation system for social media marketing content in big data environment

Abstract A personalized recommendation system is an effective and consistent marketing method that caters to consumer demand for goods. It solves the problem of consumer shopping choices on e-commerce websites and is currently a hot spot in the field of network information services. In this paper, from the perspective of the user’s shopping process, we summarize the four factors that can maximally reflect the consumer’s interest and preference, i.e., product browsing, collection, adding to cart, and purchasing behavior. To achieve static user interest weights, we quantify each index factor and set the corresponding rules. Considering the change in user interest, the decaying interest value over time is designed to compensate for the deficiencies of static system recommendations. We use implicit user feedback data to identify content that truly interests users and then construct a personalized recommendation algorithm for social media marketing content using multi-source data. The test results show that UIPR has an MRR value of more than 0.8 and an NDCG value of more than 0.55 in both the MovieLens and Taobao datasets, compared to the baseline model’s best value. This proves that UIPR consistently performs better and makes better recommendations.

Personalized Recommendation System for English Teaching Resources in Colleges and Universities Based on Collaborative Recommendation

Abstract In recent years, with the rapid development of the Internet and education informatization, online teaching has become a popular education mode in the information age, providing learners with very rich teaching resources. In this paper, we construct a personalized system and co-recommendation technology for English teaching resources, and we improve the traditional co-recommendation algorithm and propose hybrid recommendations. The performance of the system is evaluated experimentally to compare the effectiveness of the performance of the four systems. The improved recommendation algorithm is superior to the other three recommendation algorithms in each dataset in the system. The average grade mean of the experimental class assisted by the hybrid recommendation system in teaching English in colleges and universities in the latter two experiments is 27.54, which is higher than that of the comparison class of 25.33, and the T-value is 1.81>1.645. The improved personalized recommender system has good validity and stability in both performance and practical application.

Design of Personalized Recommendation System for Teaching Resources Based on Cloud Edge Computing

With the continuous development of cloud computing and edge computing technologies, the education sector is gradually applying these technologies to enhance the management and utilization efficiency of teaching resources. Therefore, to address the issue of information overload mentioned above, it is necessary to establish a personalized recommendation system based on user needs, preferences, and other information, recommending products, information, and resources that may be of interest to users. This can not only save users search time, but also alleviate the problem of information overload to some extent. On this basis, this article discussed a new content oriented recommendation method: constructing a user interest feature vector resource association matching model, and analyzing it to achieve recommendation of similar resources. The experimental results showed that the MAE (Mean Absolute Error) value of personalized recommendation based on CF (Collaborative Filtering) algorithm was below 0.8, which was smaller than other algorithms, indicating high accuracy of recommendation based on CF algorithm.

A Study of Innovative Teaching Methods in Higher Education for Digital Localization

Abstract With the rapid development of the Internet and information technology, the data generated by online learning resources has shown explosive growth. In this paper, a personalized recommendation model is used to transform and innovate digital localized education. Firstly, the model is constructed by combining online learning characteristics to generate personalized learning paths. Then, personalized recommendation algorithms are proposed on this basis, and traditional recommendation algorithms are found to have limitations. Therefore, this paper chooses a personalized recommendation algorithm based on a knowledge graph. After the questionnaire survey of the learners, it was concluded that there were significant differences in the three dimensions of learning motivation, cognitive load, and learning self-control through the comparison of the differences between the pre-and post-tests in the five dimensions, and the self-control of the students was improved. Then, the satisfaction analysis of the recommender system was conducted, and most of the learners were satisfied and very satisfied with the system, and “dissatisfied” and “very dissatisfied” were 0. The personalized recommender system provides effective targeted learning methods and improves learning efficiency in the innovation of digital localization education. The personalized recommendation system offers effective targeted learning methods that enhance learning efficiency in the innovation of digital localized education.

Personalized Recommendation Mechanism of College English Writing Teaching Content Based on OBE Concept in Big Data Environment

Abstract With the rapid development of education information technology today, more and more educators are beginning to consider how to implement more personalized education and better align teaching to students’ abilities. This paper explores personalized recommendations for college English writing instruction, utilizing the concept of Personalized Behavior Education (OBE). This paper first analyzed English-related data from 200 college students, including vocabulary, grammar, reading, and speaking abilities. Cluster analysis is used to classify the abilities of different groups in English writing. Collaborative filtering recommendation modeling is used to recommend writing methods for college students that are appropriate for them individually. This paper revealed that the first group of students utilizes 14% and 26% more quotations and advanced vocabulary, respectively, compared to the fifth group. Simulation experiments reveal that setting parameter 1 to 0.6 and parameter 2 to 0.2 results in a lower and more stable MAE value, thereby lessening its influence on the recommendation algorithm. Furthermore, the performance test results demonstrate that the precision value of this paper’s scheme surpasses that of the traditional CF algorithm by a range of 0.088 to 0.14, and the recall value of this paper’s algorithm surpasses that of the traditional CF method, with a maximum difference of 0.11. In addition, the actual accuracy of this paper’s personalized recommender system is 6.2% higher than the expected one, and it takes only 1s to provide the user with a personalized recommender’s response time. In summary, the experiments above reflect the high efficiency of this paper’s recommender system and the high acceptance of students.

Q-ADER: An Effective Q-Learning for Recommendation With Diminishing Action Space.

Deep reinforcement learning (RL) has been widely applied to personalized recommender systems (PRSs) as they can capture user preferences progressively. Among RL-based techniques, deep Q-network (DQN) stands out as the most popular choice due to its simple update strategy and superior performance. Typically, many recommendation scenarios are accompanied by the diminishing action space setting, where the available action space will gradually decrease to avoid recommending duplicate items. However, existing DQN-based recommender systems inherently grapple with a discrepancy between the fixed full action space inherent in the Q-network and the diminishing available action space during recommendation. This article elucidates how this discrepancy induces an issue termed action diminishing error in the vanilla temporal difference (TD) operator. Due to this discrepancy, standard DQN methods prove impractical for learning accurate value estimates, rendering them ineffective in the context of diminishing action space. To mitigate this issue, we propose the Q-learning-based action diminishing error reduction (Q-ADER) algorithm to modify the value estimate error at each step. In practice, Q-ADER augments the standard TD learning with an error reduction term which is straightforward to implement on top of the existing DQN algorithms. Experiments are conducted on four real-world datasets to verify the effectiveness of our proposed algorithm.

RETRACTION: Personalized Recommendation System Design for Library Resources through Deep Belief Networks

RETRACTION: Personalized Recommendation System Design for Library Resources through Deep Belief Networks

FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems

FPGA-Accelerated Data Preprocessing for Personalized Recommendation Systems

Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm

Abstract This study proposes a teaching resource recommendation method (TRRDLMF) based on deep learning and probabilistic matrix decomposition, aiming to improve teaching resource recommendation’s accuracy and personalization level. The study combines hybrid neural network feature extraction of teaching resources and user feature extraction based on extended noise-reducing self-encoder and extended probabilistic matrix decomposition-based teaching resource recommendation technique. The study validates the model’s effectiveness by conducting empirical analyses on four online learning platform datasets of different sizes. The recommendation system can effectively track learners’ knowledge mastery status and learning preferences, and provide corresponding teaching resources. Test question similarity and difficulty analysis results show that the model can accurately capture the correlation between test questions and provide complementary data in education. The personalized recommendation analysis reveals the learners’ knowledge preference states that change with learning, demonstrating the model’s ability to give explanations at the level of learners’ knowledge preferences. The analysis of the impact of data size on recommendation results shows that the recommender system can achieve up to 73.12% accuracy and 97.89% recall for different TOP-K recommendation lists, with the best F-value at TOP-6. The personalized recommendation system for teaching resources based on the joint probability matrix decomposition model and CNN improvement algorithm proposed by the research demonstrates significant effects in improving the accuracy and personalization level of teaching resources recommendation, which helps learners to acquire suitable learning resources more efficiently, and has high practical value and promotion potential.

News Recommendation System Based on User Interest and Deep Network

In order to provide the personalized news recommendation for users more efficiently, the personalized recommendation system combined with deep network was investigated. Based on the deep network, a news recommendation system for users was designed. By means of neural network and aggregating users’ interest characteristics, users’ personalized needs for news recommendation was met. In order to solve the problem of inaccurate construction of users’ interest preference characteristics, a personalized news method based on users’ search records and interest preference was proposed. By constructing exogenous user interest preferences from user search records, the final recommendation list was generated by using the fusion method of the two preferences. Because the traditional recommendation algorithm ignored time series of the users’ browsing behaviors, an improved circulation model of neural network algorithm was proposed. The parallel convolution neural network based on attention was used to aggregate characteristics of users’ interest and the recursive neural network based on attention mechanism was used to explore hidden time series characteristics. At the same time, it was tested on real news data sets and the results showed that this method had a good recommendation effect.

Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model

Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model

Personalized learning paths recommendation system with collaborative filtering and content-based approaches

Recommender systems have undergone a transformative evolution, reshaping user interactions across diverse domains. Notably, the emphasis on personalized learning paths has grown significantly in education. This research paper delves into the performance evaluation of User-based Collaborative Filtering and Content-based recommendation techniques to develop innovative recommender systems explicitly tailored to Information Systems students. By integrating the primary dataset collection rooted within the Knowledge - Skill - Attitude framework for students in the Faculty of Information Systems at the University of Economics and Law, this study assesses how effectively these two separate models develop personalized recommendation systems. Furthermore, the empirical evaluation of two distinct models, the Collaborative Filtering and Content-Based approach, across key metrics such as Precision, Recall, and F1-Score, provides a comprehensive view of their effectiveness in generating a Recommendation System for the University of Economics and Law. Findings reveal that the Collaborative Filtering approach excels in Precision, achieving a perfect score. At the same time, the Content-based technique demonstrates superior recall capabilities, suggesting its potential to cater to diverse educational needs. This paper also highlights the transformative role of recommendation systems in higher education, particularly in enhancing student engagement through personalized learning experiences and aligning curricula with industry requirements. Recognizing the limitations inherent in deploying either model independently, future research should propose a hybrid approach that combines the strengths of both Collaborative Filtering and Content-based methods, aiming to mitigate the existing drawbacks of the distinct model. The findings provide actionable insights for students, universities, and businesses to enhance educational content and career development tools and pave the way for future research on hybrid recommendation methodologies, which promise a more tailored and efficient learning experience for learners.

A Study on Personalized Digital Marketing Content Creation Based on Consumer Psychoanalysis

Abstract At present, personalized recommendation technology is widely used in digital marketing. In this paper, on the basis of the existing personalized recommendation algorithm based on commodity characteristics, from the perspective of consumer psychology, we propose a multiple attitude recommendation algorithm under the apparent awareness of the customer. In this recommendation algorithm, the user’s recent and historical interest weights are added, and personalized digital marketing content recommendations are made based on consumer psychology. The MT algorithm designed in this paper has a higher recommendation accuracy when compared to other recommendation algorithms. A questionnaire survey is conducted to examine the influence of marketing content on consumers’ purchase intentions on shopping websites using the personalized recommendation system designed in this paper. The correlation analysis results indicate that the variables and the willingness to buy have a positive correlation at a significance level of 0.01. The final regression equation: willingness to buy = 0.065+0.126*information orchestration+0.113*pop-up ads+0.109*social channel recommendation+0.158*web system recommendation+0.152*user trust, which indicates that the variable of web system recommendation has the greatest effect on willingness to buy.

Personalized Recommendations of Online English Teaching Resources for Higher Vocational Education

Abstract Facing the intricate challenges of English instruction in higher vocational colleges, this research innovatively employs online resources to elevate educational quality and learner efficiency. It critiques the ineffectiveness of “one-size-fits-all” educational models and proposes a cutting-edge personalized recommendation system. This system adeptly curates learning materials to align with individual students’ learning patterns and competencies. By pioneering the TDINA and CUPMF models, and synergizing them with convolutional neural network (CNN) technology, our study dives deep into the educational content, ensuring precise resource recommendations. The system’s prowess was demonstrated through the successful recommendation of 815 resources, identifying strengths in electronic wall charts and courseware, alongside areas for enhancement in microclass scripts and recordings. A specialized experiment in personalized exercise recommendations further underscored the system’s efficacy, accurately tailoring the exercise level and difficulty to students’ learning capacities. Significantly, the CUPMF model emerged as a robust tool in refining the accuracy of these personalized recommendations, heralding a new era of bespoke educational experiences for students.