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.

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.

Ontology-based framework for personalized recommendation in digital libraries

To promote information service ability of digital libraries, a browsing and searching personalized recommendation framework based on the use of ontology is described, where the advantages of ontology are exploited in different parts of the retrieval cycle including query-based relevance measures, semantic user preference representation and automatic update, and personalized result ranking. Both the usage and information resources can be exploited to extract useful knowledge from the way users interact with a digital library. Through combination and mapping between the extracted knowledge and domain ontology, semantic content retrieval between queries and documents can be utilized. Furthermore, ontology-based conceptual vector of user preference can be applied in personalized recommendation feedback.

Progresses on Personalized Nutritional Evaluation and Recommendation

Health is maintained by a state of dynamic homeostasis in which nutrient intake and ex- penditure are of good balance. Therefore, it is important to know exactly the nutritional value of food sources, as well as the nutritional requirements of individuals, in order to achieve optimal nutrition. Considering the interaction between diet and individual back- ground, nutritional evaluation and recommendation has become a complicate issue needing further investigations. While traditional nutrition research has made significant progress in population nutrition, modern nutrition research is now becoming possible to focus on personalized nutrition in health promotion, disease prevention, performance improvement, and risk assessment of individual with the development of emerging omics technologies. This review tried to summarize the methods used in nutritional evaluation and recom- mendation as well as their applications. Though personal nutrition evaluation and recommendation are still not well-established, utilization of these advanced technologies may expand our knowledge in bioavailability and bioefficacy of diet ingredients, pathophysiological changes in response to dietary intervention, as well as nutrition-associated disease biomarkers discovery, and thus contributing to personalized nutrition.

FedNRM:A Federal Personalized News Recommendation Model Achieving User Privacy Protection

In recent years,the type and quantity of news are growing rapidly,and it is not easy for users to find the news they are interested in the massive amount of news.A news recommendation system can score and predict the candidate news,and finally recommend the news with high scores to users.However,existing user models usually only consider users’long-term interests and ignore users’recent interests,which affects users’usage experience.Therefore,this paper introduces gated recurrent unit(GRU)sequence network to capture users’short-term interests and combines users’short-term interests and long-terminterests to characterize users.While existing models often only use the user’s browsing history and ignore the variability of different users’interest in the same news,we introduce additional user’s ID information and apply the personalized attention mechanism for user representation.Thus,we achieve a more accurate user representation.We also consider the risk of compromising user privacy if the user model training is placed on the server side.To solve this problem,we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side.We further employ secure multiparty computation to request news representations from the server side,which protects privacy to some extent.Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics.Compared with the current state-of-the-art federated news recommendation models,our model has increased by 0.54%in AUC,1.97%in MRR,2.59%in nDCG@5%,and 1.89%in nDCG@10.At the same time,because we use a federated learning framework,compared with other centralized news recommendation methods,we achieve privacy protection for users.

Combining User-Driven Social Marketing with System-Driven Personalized Recommendation for Student Finding

Student selection is of crucial importance for supervisors who are choosing students for postgraduate studies or research projects.Due to the challenge of asymmetric information,it is difficult for them to find suitable candidates.The existing methods do not work so well in the web 2.0 context which is inundated with vast online information.In order to overcome the deficiency,a research social network enhanced approach is proposed to provide decision support.It appeals to supervisors to adopt the proposed user-driven social marketing strategy.Meanwhile,this study mainly presents a system-driven personalized recommendation approach to support supervisors'decisions of student selection.The proposed method distinguishes supervisors based on their co-author networks to extract their potential preferences of collaboration styles.Subsequently,corresponding recommendation strategies are employed to provide personalized student recommendation services for targeted supervisors.A prototype is implemented on ScholarMate which provides online communication channels for researchers.A user study is conducted to verify the effectiveness of the proposed approach.The results enlighten designers to consider the differences among different users when designing recommendation strategies.

A Personalized Cloud Services Recommendation Based on Cooperative Relationship between Services

A personalized recommendation for cloud services, which is based on usage history and the cooperative relationship of cloud services, is presented. According to service groups, a service group could be defined as several services that were used together by one user at a time, and cooperative relationship between each two services can be calculated. In the process of recommendation, the services which are highly related to the service that the user has selected would be obtained firstly, the result should then take the QoS (Quality of Service) similarity between service’s QoS and user’s preference into account, so the final result combining the cooperative relationship and similarity will meet the functional needs of users and also meet the user’s personalized non-functional requirements. The simulation proves that the algorithm works effectively.

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.

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.

Research on the Personalized Recommendation of Clothing Based on Rough Set Theory

With time going on, the fact that pace of life becomes faster make more and more customers pay more attention to of clothing. In order to survive and develop better and to attract more customers, enterprisesmust have the ability to provide the personalized recommendations and the implementation of differentiated business strategy. This text aims to make enterprises understand the customers＇ personalized requirement by using the data processed though questionnaire and rough set theory. And enterprises can provide production and marketing auxiliary decision-making effectively. The feasibility and practicality of rough set theory is verified through the personalized recommendationseases.

Research and implementation of a personalized book recommendation algorithm --Taking the library of Jinan University as an example

Abstract： Taking the basic data and the log data of the various businesses of the automation integrated management system of the library in Jinan University as the research object this paper analyzes the internal relationship between books and between the books and the readers, and designs a personalized book recommendation algorithm, the BookSimValue, on the basis of the user collaborative filteringtechnology. The experimental results show that the recommended book information produced by this algorithm can effectively help the readers to solve the problem of the book information overload, which can bring great convenience to the readers and effectively save the time of the readers＇ selection of the books, thus effectively improving the utilization of the library resources and the service levels.

The Mobile Personalized Recommendation Model Containing Implicit Intention

Because mobile e-commerce is limited by the mobile terminal,network environment and other factors,accurate personalized recommendations become more and more important.We establish a large data intelligence platform,aiming at the characteristics of mobile e-commerce;we put forward a personalized recommendation model with implicit intention further.Firstly,create an intelligence unit with the virtual individual association set,virtual demand association set and virtual behavior associated set;Secondly,calculate the complex buying behavior prediction engine;Finally,give the predictive value of complex buying behavior.This method takes full account of factors such as hidden wishes perturbations that affect the predict of complex buying behavior,which to some extent solve a long-span composite purchasing behavior prediction.It shows that this method improves the purchasing behavior prediction accuracy effectively through experiments.

Gamified Learning Systems’Personalized Feedback Report Dashboards via Custom Machine Learning Algorithms and Recommendation Systems

Gamification in education enables for the holistic optimization of the learning process,empowering learners to ameliorate their digital,cognitive,emotional and social skills,via their active experimentation with game design elements,accompanying pertinent pedagogical objectives of interest.This paper focuses on a cross-platform,innovative,gamified,educational learning system product,funded by the Hellenic Republic Ministry of Development and Investments:howlearn.By applying gamification techniques,in 3D virtual environments,within which,learners fulfil STEAM(Science,Technology,Engineering,Arts and Mathematics)-related Experiments(Simulations,Virtual Labs,Interactive Storytelling Scenarios,Decision Making Case Studies),howlearn covers learners’subject material,while,simultaneously,functioning,as an Authoring Gamification Tool and as a Game Metrics Repository;users’metrics are being,dynamically,analyzed,through Machine Learning Algorithms.Consequently,the System learns from the data and learners receive Personalized Feedback Report Dashboards of their overall performance,weaknesses,interests and general class competency.A Custom Recommendation System(Collaborative Filtering,Content-Based Filtering)then supplies suggestions,representing the best matches between Experiments and learners,while also focusing on the reinforcement of the learning weaknesses of the latter.Ultimately,by optimizing the Accuracy,Performance and Predictive capability of the Personalized Feedback Report,we provide learners with scientifically valid performance assessments and educational recommendations,thence intensifying sustainable,learner-centered education.

Personalized Recommendation System on Hadoop and HBase

In view of the existing recommendation system in the Big Data have two insufficiencies:poor scalability of the data storage and poor expansibility of the recommendation algorithm,research and analysis the IBCF algorithm and the working principle of Hadoop and HBase platform,a scheme for optimizing the design of personalized recommendation system based on Hadoop and HBase platform is proposed.The experimental results show that,using the HBase database can effectively solve the problem of mass data storage,using the MapReduce programming model of Hadoop platform parallel processing recommendation problem,can significantly improve the efficiency of the algorithm,so as to further improve the performance of personalized recommendation system.

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.

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.

Improving Personal Product Recommendation via Friendships’ Expansion

The trust as a social relationship captures similarity of tastes or interests in perspective. However, the existent trust information is usually very sparse, which may suppress the accuracy of our personal product recommendation algorithm via a listening and trust preference network. Based on this thinking, we experiment the typical trust inference methods to find out the most excellent friend-recommending index which is used to expand the current trust network. Experimental results demonstrate the expanded friendships via superposed random walk can indeed improve the accuracy of our personal product recommendation.

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.

Design of a Student Recommendation Platform Based on Learning Behavior and Habit Training

This study innovatively built an intelligent analysis platform for learning behavior,which deeply integrated the cutting-edge technology of big data and Artificial Intelligence(AI),\mined and analyzed students’learning data,and realized the personalized customization of learning resources and the accurate matching of intelligent learning partners.With the help of advanced algorithms and multi-dimensional data fusion strategies,the platform not only promotes positive interaction and collaboration in the learning environment but also provides teachers with comprehensive and in-depth students’learning portraits,which provides solid support for the implementation of precision education and the personalized adjustment of teaching strategies.In this study,a recommender system based on user similarity evaluation and a collaborative filtering mechanism is carefully designed,and its technical architecture and implementation process are described in detail.

Integrating Research Analytic Framework and Personality Matching for Supervisor Recommendation

Supervisor selection is important for research students in their future studies and careers.Currently,students rely on information search or friends recommendation to find potential research supervisors.However,due to the challenges of incomplete and asymmetric information,students can hardly find suitable supervisors that match their research interests as well as personalities.Existing methods mainly consider topic-relevance and candidate-quality,and overlook the significance of connectivity consideration and two-sided matching degree of individuals personality styles.It proposes a novel supervisor recommendation approach that integrates relevance,connectivity,quality and personality-matching dimensions.The results of user-based evaluations demonstrate that the proposed approach generates more satisfactory recommendations as compared to that of all baseline methods.The present solution has been implemented as a social network recommendation service on ScholarMate.