Research on the Trust Model Based on the Groups’ Internal Recommendation in E-Commerce Environment

The trust plays an extremely important role in online shopping. In order to make online shopping trusty, this paper puts foreword a new trust model in e-commerce environment GIR-TM (Groups’ Internal Recommendation Trust Model). First, it regarded the network as a combination of groups, and then did the internal recommendation based on these groups. The GIR-TM, in the process of recommendation, distinguished clearly between the trust degrees of recommen-dation node and the trust degrees of recommended node, and then calculated the integrated credibility value of the recommended node according to the weight of recommendation node in the group, the partial trust degree and the de-gree of recommendation when the recommendation node recommends the recommended node, and the overall credibil-ity value of recommended node as well. Lastly through listing the experimental data and comparing with the HHRB-TM (History and Honest Recommendation Based Trust Model) on the same condition, it is verified that GIR-TM is feasible and effective.

The Design and Realization of Personalized E-commerce Recommendation System

According to demand and function of the e-commerce recommendation system demand, this paper analyze and design e-commerce and personalized recommendation, design and complete different system functions in different system level; then design in detail system process from the front and back office systems, and in detail descript the key data in the database and several tables. Finally, the paper respectively tests several main modules of onstage system and the backstage system. The paper designed electronic commerce recommendation based on personalized recommendation system, it can complete the basic function of the electronic commerce system, also can be personalized commodity recommendation for different users, the user data information and the user＇ s shopping records.

Research and Modelling on the E-commerce Consumer Behavior based on Intelligent Recommendation System and Machine Learning

In this paper, we conduct research on the E-commerce consumer behavior based on the intelligent recommendation system andmachine learning. Closely associated with consumer network information search of a problem is that the consumer’s information demand ascan be thought of consumer’s information demand is leading to trigger the power of consumer network information search behavior, whenconsumer is willing to buy goods, in a certain task under the infl uence of factors, environmental factors, individual factors, consumers and thetask object interaction to form the demand of consumer cognition. Under this basis, this paper proposes the new idea on the related issues thatwill solve the related challenges.

An E-Commerce Recommender System Based on Click and Purchase Data to Items and Considered of Interest Shifting of Customers

A well-performed recommender system for an e-commerce web site can help customers easily find favorite items and then increase the turnover of merchants, hence it is important for both customers and merchants. In most of the existing recommender systems, only the purchase information is utilized data and the navigational and behavioral data are seldom concerned. In this paper, we design a novel recommender system for comprehensive online shopping sites. In the proposed recommender system, the navigational and behavioral data, such as access, click, read, and purchase information of a customer, are utilized to calculate the preference degree to each item; then items with larger preference degrees are recommended to the customer. The proposed method has several innovations and two of them are more remarkable: one is that nonexpendable items are distinguished from expendable ones and handled by a different way; another is that the interest shifting of customers are considered. Lastly, we structure an example to show the operation procedure and the performance of the proposed recommender system. The results show that the proposed recommender method with considering interest shifting is superior to Kim et al(2011) method and the method without considering interest shifting.

An E-Commerce Recommender System Based on Content-Based Filtering

Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.

A multilayer network diffusion-based model for reviewer recommendation

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

An Adaptive Program Recommendation System for Multi-User Sharing Environment

More and more accounts or devices are shared by multiple users in video applications,which makes it difficult to provide recommendation service.Existing recommendation schemes overlook multiuser sharing scenarios,and they cannot make effective use of the mixed information generated by multi-user when exploring users’potential interests.To solve these problems,this paper proposes an adaptive program recommendation system for multi-user sharing environment.Specifically,we first design an offline periodic identification module by building multi-user features and periodically predicting target user in future sessions,which can separate the profile of target user from mixed log records.Subsequently,an online recommendation module with adaptive timevarying exploration strategy is constructed by jointly using personal information and multi-user social information provided by identification module.On one hand,to learn the dynamic changes in user-interest,a time-varying linear upper confidence bound(LinUCB)based on personal information is designed.On the other hand,to reduce the risk of exploration,a timeinvariant LinUCB based on separated multi-user social information from one account/device is proposed to compute the quality scores of programs for each user,which is integrated into the time-varying LinUCB by cross-weighting strategy.Finally,experimental results validate the efficiency of the proposed scheme.

Improving Diversity with Multi-Loss Adversarial Training in Personalized News Recommendation

Users’interests are often diverse and multi-grained,with their underlying intents even more so.Effectively captur-ing users’interests and uncovering the relationships between diverse interests are key to news recommendation.Meanwhile,diversity is an important metric for evaluating news recommendation algorithms,as users tend to reject excessive homogeneous information in their recommendation lists.However,recommendation models themselves lack diversity awareness,making it challenging to achieve a good balance between the accuracy and diversity of news recommendations.In this paper,we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity.Unlike most existing works that solely optimize accuracy or employ more features to meet diversity,the proposed algorithm leverages the diversity-aware capability of the model.First,we introduce an augmented user model to fully capture user intent and the behavioral guidance they might undergo as a result.Specifically,we focus on the relationship between the original clicked news and the augmented clicked news.Moreover,we propose an effective adversarial training method for diversity(AT4D),which is a pluggable component that can enhance both the accuracy and diversity of news recommendation results.Extensive experiments on real-world datasets confirm the efficacy of the proposed algorithm in improving both the accuracy and diversity of news recommendations.

Urban Traffic Control Meets Decision Recommendation System:A Survey and Perspective

Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.

Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

Impacts of agri-food e-commerce on traditional wholesale industry:Evidence from China

Rapidly expanding studies investigate the effects of e-commerce on company operations in the retail market.However,the interaction between agri-food e-commerce(AEC)and the traditional agri-food wholesale industry(AWI)has not received enough attention in the existing literature.Based on the provincial panel data from 2013 to 2020 in China,this paper examines the effect of AEC on AWI,comprising three dimensions:digitalization(DIGITAL),agrifood e-commerce infrastructure and supporting services(AECI),and agri-food e-commerce economy(AECE).First,AWI and AEC are measured using an entropy-based combination of indicators.The results indicate that for China as a whole,AWI has remained practically unchanged,whereas AEC exhibits a significant rising trend.Second,the findings of the fixed-effect regression reveal that DIGITAL and AECE tend to raise AWI,whereas AECI negatively affects AWI.Third,threshold regression results indicate that AECI tends to diminish AWI with three-stage inhibitory intensity,which manifests as a first increase and then a drop in the inhibition degree.These results suggest that with the introduction of e-commerce for agricultural product circulation,digital development will have catfish effects that tend to stimulate the vitality of the conventional wholesale industry and promote technical progress.Furthermore,the traditional wholesale industry benefits financially from e-commerce even while it diverts part of the traditional wholesale circulation for agricultural products.

The use of oral human immunodeficiency virus pre-exposure prophylaxis in pregnant and lactating women in sub-Saharan Africa:considerations,barriers,and recommendations

In sub-Saharan Africa(SSA),63%of new human immunodeficiency virus(HIV)infections in 2021 were among women,particularly adolescent girls,and young women.There is a high incidence of HIV among pregnant and lactating women(PLW)in SSA.It is estimated that the risk of HIV-acquisition during pregnancy and the postpartum period more than doubles.In this article,we discuss the safety and effectiveness of drugs used for oral HIV pre-exposure prophylaxis(PrEP),considerations for initiating PrEP in PLW,the barriers to initiating and adhering to PrEP among them and suggest recommendations to address these barriers.Tenofovir/emtricitabine,the most widely used combination in SSA,is safe,clinically effective,and cost-effective among PLW.Any PLW who requests PrEP and has no medical contraindications should receive it.PrEP users who are pregnant or lactating may experience barriers to starting and adhering for a variety of reasons,including personal,pill-related,and healthcare facility-related issues.To address the barriers,we recommend an increased provision of information on PrEP to the women and the communities,increasing and/or facilitating access to PrEP among the PLW,and developing strategies to increase adherence.

Recommendation System Based on Perceptron and Graph Convolution Network

The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution network.The current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction data.This paper presents a new approach to address such issues,utilizing the graph convolution network to extract association relations.The proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction layer.The embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation layer.The forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph convolution.Furthermore,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction layer.The score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,respectively.Finally,the prediction score of users to items is obtained.The recall rate and normalized discounted cumulative gain were used as evaluation indexes.The proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms.

Diversified and compatible web APIs recommendation based on game theory in IoT

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible remotely.In this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile applications.However,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs selected.Considering this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this paper.First of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation subgraphs.Afterwards,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search problem.At last,a set of experiments are designed and implemented on a real dataset crawled from www.programmableweb.com.Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners:A Recommendation System

Recognizing human activity(HAR)from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases.Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not.Typically,smartphones and their associated sensing devices operate in distributed and unstable environments.Therefore,collecting their data and extracting useful information is a significant challenge.In this context,the aimof this paper is twofold:The first is to analyze human behavior based on the recognition of physical activities.Using the results of physical activity detection and classification,the second part aims to develop a health recommendation system to notify smartphone users about their healthy physical behavior related to their physical activities.This system is based on the calculation of calories burned by each user during physical activities.In this way,conclusions can be drawn about a person’s physical behavior by estimating the number of calories burned after evaluating data collected daily or even weekly following a series of physical workouts.To identify and classify human behavior our methodology is based on artificial intelligence models specifically deep learning techniques like Long Short-Term Memory(LSTM),stacked LSTM,and bidirectional LSTM.Since human activity data contains both spatial and temporal information,we proposed,in this paper,to use of an architecture allowing the extraction of the two types of information simultaneously.While Convolutional Neural Networks(CNN)has an architecture designed for spatial information,our idea is to combine CNN with LSTM to increase classification accuracy by taking into consideration the extraction of both spatial and temporal data.The results obtained achieved an accuracy of 96%.On the other side,the data learned by these algorithms is prone to error and uncertainty.To overcome this constraint and improve performance(96%),we proposed to use the fusion mechanisms.The last combines deep learning classifiers tomodel non-accurate and ambiguous data to obtain synthetic information to aid in decision-making.The Voting and Dempster-Shafer(DS)approaches are employed.The results showed that fused classifiers based on DS theory outperformed individual classifiers(96%)with the highest accuracy level of 98%.Also,the findings disclosed that participants engaging in physical activities are healthy,showcasing a disparity in the distribution of physical activities between men and women.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

Levels of evidence and grades of recommendation supporting European society for medical oncology clinical practice guidelines

Background:The European Society for Medical Oncology(ESMO)guidelines are among the most comprehensive and widely used clinical practice guidelines(CPGs)globally.However,the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated.This study assessed ESMO CPG levels of evidence(LOE)and grades of recommendations(GOR),as well as their trends over time across various cancer settings.Methods:We manually extracted every recommendation with the Infectious Diseases Society of America(IDSA)classification from each CPG.We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across different topics and cancer types.Results:Among the 1,823 recommendations in the current CPG,30%were classified as LOEⅠ,and 43%were classified as GOR A.Overall,there was a slight decrease in LOEⅠ(−2%)and an increase in the proportion of GOR A(+1%)in the current CPG compared to previous versions.The proportion of GOR A recommendations based on higher levels of evidence such as randomized trials(LOEⅠ–Ⅱ)shows a decrease(71%vs.63%,p=0.009)while recommendations based on lower levels of evidence(LOEⅢ–Ⅴ)show an increase(29%vs.37%,p=0.01)between previous and current version.In the current versions,the highest proportion of LOEⅠ(42%)was found in recommendations related to pharmacotherapy,while the highest proportion of GOR A recommendations was found in the areas of pathology(50%)and diagnostic(50%)recommendations.Significant variability in LOEⅠand GOR A recommendations and their changes over time was observed across different cancer types.Conclusion:One-third of the current ESMO CPG recommendations are supported by the highest level of evidence.More well-designed randomized clinical trials are needed to increase the proportion of LOEⅠand GOR A recommendations,ultimately leading to improved outcomes for cancer patients.

Impact of BRICS Trade Facilitation on China's Exports Using Cross-Border E-Commerce

With the rapid growth of the global digital economy, cross-border e-commerce, as an emerging form of trade, has gradually become a powerful engine to promote the development of global trade. BRICS is an important force in the global economy, and the progress of the BRICS countries' trade facilitation level has an important impact on the global trade environment. This paper conducts an in-depth study of the dynamic changes in BRICS trade facilitation from 2013 to 2022, and uses an extended gravity model to analyze the specific impact of this change on China's exports using cross-border e-commerce. The results show that although the BRICS countries have made some progress in trade facilitation, the overall level still needs to be improved, and there are obvious differences among member countries. However, the improvement of trade facilitation among BRICS countries has undoubtedly brought significant positive effects to China's exports using cross-border e-commerce.

Enhancing personalized exercise recommendation with student and exercise portraits

The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity.

Enhancing Multicriteria-Based Recommendations by Alleviating Scalability and Sparsity Issues Using Collaborative Denoising Autoencoder

A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.