Recommender System Architecture Research Articles

Hybrid Recommender System for Tourism Based on Big Data and AI

With the advancement of the Internet, technology, and communication channels, the generation of tourist data has significantly increased across various sectors such as hotels, restaurants, transportation, heritage sites, tourist events, and activities. This surge is particularly notable with the rise of Online Travel Agencies (OTAs). However, the sheer volume of options provided to tourists by web search engines or specialized tourism websites often overwhelms them, burying relevant results in a sea of information "noise." This inundation hinders or slows down the decision-making process. To alleviate this issue and aid tourists in trip planning by facilitating the discovery of relevant information, numerous recommender systems have emerged. This article offers an overview of the diverse recommendation approaches employed in the tourism domain. Through this investigation, we propose an architecture and conceptual framework for a tourism recommender system founded on a hybrid recommendation approach. This system surpasses merely suggesting a list of tourist attractions tailored to individual preferences. Instead, it functions as a comprehensive trip planner, crafting detailed itineraries comprising a variety of tourism resources, customized for specific visit durations. KeyWords: Internet Development, Tourist Data Production, Trip Planning, Conceptual Framework.

An Online Course Recommender System in e-Learning Using Learners’ Profile and Learning Behavior-Based Mechanism

The advent of online learning platforms, such as Coursera and Udemy, has facilitated global access to diverse educational opportunities. However, integrating recommender algorithms into these online learning systems presents a notable challenge. To address this challenge, this research paper proposes an architecture for online course recommender systems that leverages learners’ profiles and learning behavior. The proposed architecture comprises six core components: 1) User Management Engine, 2) Online Course Content Management Engine, 3) Learning Behavior Observer and Recorder, 4) Profile and Learning Behavior Builder Engine, 5) Course Recommendation and Filtering Engine, and 6) Feedback Engine. These components collectively form the foundation of the online course recommender system. In order to facilitate practical implementation, a prototype of the online course recommender system has been developed. To evaluate the system’s performance, a comparative analysis was conducted, to compare the personalized recommended list with the non-personalized recommended list. The analysis revealed an average precision of 77.60% for the personalized recommended list, while the non-personalized recommended list achieved an average precision of 65.60%. These findings highlight the superiority of the personalized approach in generating more accurate and relevant online course recommendations.

A generic architecture of an affective recommender system for e-learning environments

Personalization of suggestions of contents plays a key role in adaptive virtual learning environments. Good recommendations can raise the interest of students in the learning process, while, on the other hand, bad recommendations can have catastrophic results for the learning process. The affective state of the student is a very influential factor in the learning process. In this work, a generic architecture of an affective recommender system for e-learning environments is developed, to serve as a guide for future implementations of this kind of recommender system. Here, the affective characteristics of students are represented by their personalities, learning styles, emotional states, and expertise levels. Thus, the main contribution is the proposition of a generic architecture of an affective recommendation system for the educational field. The architecture is completely modular, which gives it great flexibility because the emotion engine is separated from the personal characteristics engine, and is not based on specific models of emotions. This work finishes with examples of use cases of the architecture. According to the results in these examples, our architecture is capable of analyzing the polarity of academic documents based on their content, determining the personal characteristics of students (including their emotions), and from there, recommending learning resources to students considering emotions as the main element of the process.

E-Commerce Product Image-Based Recommendation System Kalcare.Com Using Deep Learning

Recommendation systems have now become an important part of a digital service, one example is e-commerce. The facts show that the COVID-19 pandemic has had a significant impact on customers by making them spend more time surfing online to get daily necessities products by shopping on e-commerce sites. With the rapid development of deep learning technology today, of course, it can be used to help in terms of the process of producing a product image-based recommendation system that has a fairly high level of similarity. This study will discuss how to produce an image-based product recommendation system architecture by comparing the results of the application of algorithms 8 pre-trained models that are available and have also been widely used in various studies and the information technology industry. The dataset used is product images sourced from the kalcare.com website. After testing the pre-trained model, then an application prototype was made to be tested then in the final stage of this research a poll was conducted to determine the response and opinion of users to the protopine recommendation system made for e-commerce kalcare.com using deep learning.

Context-aware recommendations for extended electric vehicle battery lifetime

Electric vehicles are a means of reducing CO2 emissions in transportation. However, the sustainability of electric vehicle batteries is affected by battery health degradation, which decreases their overall lifetime. This results in a substantial amount of depleted batteries due to replacements. Users have a major impact on battery health degradation through their actions while handling electric vehicles, such as the use of fast charging. To mitigate this problem, this article presents a methodology to generate user guidance for battery-friendly actions in the upcoming use. Therefore, we first identify general recommendations from related work and combine them with the vehicle context in order to define context-aware recommendations in the form of if–then rules. These context-aware recommendations are then used to generate user advice. Second, the article covers how to predict the vehicle context in order to determine necessary recommendations. Third, a prescriptive recommendation system architecture is proposed, which takes vehicle context information, and produces user guidance. Finally, we test the proposed architecture using fuzzy logic as decision system. Overall, the architecture provides satisfactory user advice.

Healthcare Recommender System Based on Medical Specialties, Patient Profiles, and Geospatial Information

The global outburst of COVID-19 introduced severe issues concerning the capacity and adoption of healthcare systems and how vulnerable citizen classes might be affected. The pandemic generated the most remarkable transformation of health services, appropriating the increase in new information and communication technologies to bring sustainability to health services. This paper proposes a novel, methodological, and collaborative approach based on patient-centered technology, which consists of a recommender system architecture to assist the health service level according to medical specialties. The system provides recommendations according to the user profile of the citizens and a ranked list of medical facilities. Thus, we propose a health attention factor to semantically compute the similarity between medical specialties and offer medical centers with response capacity, health service type, and close user geographic location. Thus, considering the challenges described in the state-of-the-art, this approach tackles issues related to recommenders in mobile devices and the diversity of items in the healthcare domain, incorporating semantic and geospatial processing. The recommender system was tested in diverse districts of Mexico City, and the spatial visualization of the medical facilities filtering by the recommendations is displayed in a Web-GIS application.

Serverless E-commerce Recommendation System

Over the last decade, recommender systems have been widely applied by major e-commerce websites for personalized user experience. However, few efforts have been focused so far on recommender systems architecture. In addition, Big Data technologies present opportunities to create unprecedented business advantage and better service delivery at low cost. The recommender system architecture may vary according to the context in which e-commerce is inserted and with the adopted business settings. Consequently, from smaller to bigger companies, each recommendation system has his individual architecture with distinct implementations, but sharing similar issues. With the rapid development of e-commerce, its information structure is becoming more and more complex, and the amount of information is becoming larger and larger. Users are often lost in massive commodity information, and merchants cannot establish effective customer relationships in massive user information. In order to improve the service level and market competitiveness of Internet commerce, many e-commerce websites begin to introduce cloud computing technology. According to users' purchase records and historical browsing records, they can find the goods they like and recommend them to users. In order to manage massive commodity information and user information more efficiently, this paper proposes a solution to build e-commerce recommendation system on the cloud computing platform to improve the ability of massive data mining and business intelligence analysis, and realise high-performance computing at a lower cost.

3T-IEC*: a context-aware recommender system architecture for smart social networks (EBSN and SBSN)

3T-IEC*: a context-aware recommender system architecture for smart social networks (EBSN and SBSN)

Machine Learning-Driven Enterprise Human Resource Management Optimization and Its Application.

With the advent of the Internet era, the frequency and proportion of candidates obtaining recruitment information through the Internet is getting higher and higher, and the amount of human resource information, such as talent information and job information, has also increased unprecedentedly, which makes human resource services face information overload. Especially with the gradual increase of the amount of information, this method is not enough for the acquisition and classification of massive data. After that, experts developed search engines to deal with the retrieval problem, and the first ones were Google and Baidu. As long as the search engine is clear about the direction of the search, it is indeed very convenient for the retrieval of massive data. However, in many cases, most users cannot clearly recognize the content they need or how to accurately express their needs. Faced with this problem, people propose recommender systems to solve the problem of obtaining preference information, which can better increase the user's experience and meet their own needs more easily. Based on the main workflow of the recommender system, this paper designs the overall architecture of the human resources recommendation system and implements a human resources recommendation prototype system based on deep learning. The system can better overcome the cold start problem and provide real-time recommendation results, improving the quality of HR personalized recommendation results.

E-Commerce Personalized Recommendation Based on Machine Learning Technology

As e-commerce offers more and more choices for users, its structure becomes more and more complicated. Inevitably, it brings about the problem of information overload. The solution to this problem is an e-commerce personalized recommendation system using machine learning technology. People often seem confused when facing extensive information and cannot grasp the key points. This paper studies the personalized recommendation technology of e-commerce: deeply analyzes the related technologies and algorithms of the e-commerce recommendation system and proposes the latest architecture of the e-commerce recommendation system according to the current development status of the e-commerce recommendation system. The system recommends accuracy and real-time requirements and divides the system into two parts: offline mining and online recommendation and analyzes and implements the functions and technologies of each part. User-based recommender systems, collaborative filtering recommender systems, and content-based recommender systems are analyzed, respectively. The personalized recommendation cannot only quickly help customers find the required commodity information in a wide range of complex information but also can compare more commodity information to help customers to judge. However, the existing recommendation system has some problems such as the lack of recommendation personality, the reduced relevance of recommendation, and the poor timeliness of recommendation. Finally, a recommendation system that combines three recommendation algorithms is designed, and experiments are carried out. The newly designed recommendation system is compared with three different recommendation systems, and a summary and outlook are made. Based on the introduction of the relevant theories, characteristics, and mainstream technologies of personalized recommendation based on machine learning, this document presents a constructive example of a model based on the factors that influence personalized e-commerce information recommendations in the retail sector. Through questionnaire surveys, we analyze and design the influencing factors for consumers to purchase personalized products after the survey and build a project using state-of-the-art field learning techniques. Through the model to test the eight hypotheses proposed in this paper, the results show that customer income level, customer online shopping experience, commodity prices, product quality, recommendation relevance, credit evaluation, and service quality will have a significant positive impact on shopping willingness and ultimately affect the customer’s shopping behavior. e-commerce platform can use this influencing factor to establish personalized information recommendation service mode.

A survey on blockchain-based Recommender Systems: Integration architecture and taxonomy

A Recommender System (RS) is an integral part of present-day leading web services, such as YouTube, Amazon, Netflix, and many others. Modern RSs are challenged to go beyond their traditional role of predicting user preferences to efficiently provide reliable, carefully personalized, and highly accurate recommendations. This paper thoroughly explores and analyzes state-of-the-art literature surveys on RS to extract important challenges and open issues. Our goal in this paper is to survey the literature to extract essential features of RSs and Blockchain (BC), focusing on their integration. Because of the lack of an existing foundation of BC-based RSs, the intrinsic BC aspects in RSs are identified and described. Integrating BC technology within RSs can achieve many benefits such as transparency, decentralization, and security. To that end, a thorough study of the papers on current BC-based RSs is presented along with a synthesized comprehensive taxonomy. Furthermore, a modular RS architecture, encompassing on-chain and off-chain storage and computation processes, is designed. This paper also includes a thorough discussion on the validity of the proposed architecture, BC limitations concerning RSs, and the derivation of a rich set of pointers to future research directions.

Choosing the optimal approach to building a recommender system based on movie data

Recommender systems have become increasingly popular recently because they can address the problem of information overload by suggesting items of interest to the users. The volume of information with each year increases and more opportunities for Internet business, so it becomes easier to get anything through the internet, such as goods, books, movies, news. Work in the field of creating recommendation systems can be both commercial and research in nature, and need to address a number of issues. The paper studies the problem of choosing the optimal approach to building a recommendation system by repulsive from the available data. It is not possible to create a recommendation system without data. The data is usually available in an explicit or implicit way. Data that is called explicit can be collected by finding reviews and sharing of user views about different products. However, the implicit data is related to the search magazine and the data history available on the system. Choosing an optimal approach to building a recommendation system will allow the machine learning algorithm to be used. Simulated recommendation system architecture that helps to understand how to build a user-defined recommendation process. Recommender systems are classified in the manner of sampling the necessary material for the user system. Two basic approaches are primarily applied: collaborative filtering and content-oriented filtering. There is also a hybrid filtering that combines both collaborative and content-oriented filtering. The work carried out analysis and comparison of recommendation systems by types. Comparing types of recommendation system based on basic characteristics: implementation complexity, implementation accuracy, job speed, dependency on system users. Analysis of the core content-oriented filtering machine learning algorithms have been conducted.

Research on the Design of Mass Recommendation System Based on Lambda Architecture

In the era of big data, in order to increasing the information data for conforms to the personalized needs of content, research scholars put forward based on the Lambda mass recommendation system architecture design, it can not only to the recessive and dominant behavior of users of the system data collection storage and research analysis, can also be based on the analysis of cascading hybrid algorithm to explore how to carry out real-time recommendation. Therefore, on the basis of understanding the research and development achievements of recommender systems at home and abroad in recent years, and based on the understanding and analysis of Lambda architecture and cascading hybrid algorithm, this paper aims at how to design a massive recommender system in line with users’ behavior, and makes clear the recommendation effect by combining with system testing.

The architecture design of information intelligent recommendation system for mobile communication terminal

In order to effectively generate product recommendation information for specific users and push it to all kinds of mobile terminal systems, combined with the application environment of recommendation system in e-commerce, the overall architecture of information recommendation system for mobile terminal is given, and the recommendation engine structure with multi engine strategy is designed and analyzed, Processing flow and two kinds of information push schemes for mobile terminals: user-defined recommendation technology and third-party recommendation platform. The proposed recommender system architecture has clear division, strong scalability and easy expansion, and has achieved good results in practical application.

Improving Recommendations for Online Retail Markets Based on Ontology Evolution

The semantic web is considered to be an extension of the present web. In the semantic web, information is given with well-defined meanings, and thus helps people worldwide to cooperate together and exchange knowledge. The semantic web plays a significant role in describing the contents and services in a machine-readable form. It has been developed based on ontologies, which are deemed the backbone of the semantic web. Ontologies are a key technique with which semantics are annotated, and they provide common comprehensible foundation for resources on the semantic web. The use of semantics and artificial intelligence leads to what is known to be “Smarter Web”, where it will be easy to retrieve what customers want to see on e-commerce platforms, and thus will help users save time and enhance their search for the products they need. The semantic web is used as well as webs 3.0, which helps enhancing systems performance. Previous personalized recommendation methods based on ontologies identify users’ preferences by means of static snapshots of purchase data. However, as the user preferences evolve with time, the one-shot ontology construction is too constrained for capturing individual diverse opinions and users’ preferences evolution over time. This paper will present a novel recommendation system architecture based on ontology evolution, the proposed subsystem architecture for ontology evolution. Furthermore, the paper proposes an ontology building methodology based on a semi-automatic technique as well as development of online retail ontology. Additionally, a recommendation method based on the ontology reasoning is proposed. Based on the proposed method, e-retailers can develop a more convenient product recommendation system to support consumers’ purchase decisions.

SafeDrive: Hybrid Recommendation System Architecture for Early Safety Predication Using Internet of Vehicles.

The Internet of vehicles (IoV) is a rapidly emerging technological evolution of Intelligent Transportation System (ITS). This paper proposes SafeDrive, a dynamic driver profile (DDP) using a hybrid recommendation system. DDP is a set of functional modules, to analyses individual driver’s behaviors, using prior violation and accident records, to identify driving risk patterns. In this paper, we have considered three synthetic data-sets for 1500 drivers based on their profile information, risk parameters information, and risk likelihood. In addition, we have also considered the driver’s historical violation/accident data-set records based on four risk-score levels such as high-risk, medium-risk, low-risk, and no-risk to predict current and future driver risk scores. Several error calculation methods have been applied in this study to analyze our proposed hybrid recommendation systems’ performance to classify the driver’s data with higher accuracy based on various criteria. The evaluated results help to improve the driving behavior and broadcast early warning alarm to the other vehicles in IoV environment for the overall road safety. Moreover, the propoed model helps to provide a safe and predicted environment for vehicles, pedestrians, and road objects, with the help of regular monitoring of vehicle motion, driver behavior, and road conditions. It also enables accurate prediction of accidents beforehand, and also minimizes the complexity of on-road vehicles and latency due to fog/cloud computing servers.

Enabling recommendation system architecture in virtualized environment for e-learning

E-learning sites are useful for improving the skills and awareness of the academic backbone, such as instructors, students, administrative staff, and those who are searching for current information about various educational institutes. Despite all the benefits of an online learning platform, users face some challenges and complexities, such as selecting appropriate learning material and courses based on their needs and preferences. Hence, the provision of quality resources during the training phases is their central responsibility, the lack of online assistance offered by service providers is known to be the key cause of many difficulties. There is a need to create a system that can intelligently propose courses while considering a variety of viewpoints to enhance the learners' skills and knowledge. This research proposes an architecture that builds semantic recommendations with the aid of virtual agents based on user requirements and preferences, assisting academia in seeking appropriate courses in a real-world setting. The experimental and statistical results show that, when compared with existing techniques, the virtualized agent-based recommendation system not only improved user learning skills but also made course selection easier, depending on users’ interests and preferences.

An overview and evaluation of citation recommendation models

Recommendation systems assist web users with personalized suggestions based on past preferences for products, or items including documents, books, movies, and research papers. The plethora and variety of research papers on the Web and digital libraries make it challenging for researchers to find relevant publications to their scholarly interests. To cope with this inevitable challenge, various models and algorithms have been proposed to assist researchers with personalized citation recommendations. Nevertheless, so far, no research study has exploited the validity and suitability of evaluations conducted for these models to find the most promising among them. This study investigates and examines the existing citation recommendation algorithms based on the following criteria: evaluation methods adopted, comparative baselines employed, the complexity of the proposed algorithm, reproducibility of the experimental results, and consistency and universality of the evaluation methods. Besides this, our study presents a generic architecture and process of a typical citation recommendation system and provides a brief overview of information filtering methods used in the existing models. The findings of the study have implications for researchers and practitioners working on research paper recommendation and related areas.

Course Recommender System Architecture with Sentiment Score

Recommender systems are widely used these days for recommendation of courses. The recommender systems have gained acceptance among users because of their better results. This paper proposes a novel model on optimizing recommender system using sentiment score as a booster method. TOPSYS method is best suited for multi-criteria decision making (MCDM) problems. Sentiment Analysis has been added to existing recommender systems in the form of sentiment score for improving the recommendations. The sentiment analysis will add novelty and will eventually improve recommender system.

Content-based recommender system architecture for similar e-commerce products

Recommendation systems are quite famous and are increasingly being used on e-commerce platforms for a variety of purposes. The recommendation system technique used also varies greatly depending on the scope and Item of recommendation. Content-based filtering, for example, is used to recommend related product items based on user preferences. However, how the recommendation system architecture should be built starts by creating a data model for bringing up related product items. This paper offers a system architecture by considering the initial problem usually faced by recommendation systems, namely the cold start problem. The problem of lack of user preferences data is trying to be overcome by utilizing product item documents. Product item documents are processed using the TF-IDF algorithm and Vector Space Model to generate a data model. Then a query can be applied to find similarities to items that the user has seen. In the end, the recommendation system architecture that was built produced excellent Precision using Recall and Precision testing. Tests are carried out for data using the weighting of product names and product labels. The result obtained 0.84 for the average value of Recall and 0.78 for the average value of Precision.