The Knowledge Grid Based Intelligent Electronic Commerce Recommender Systems

Among thousands of alternatives, most of the time online customers cannot easily decide on which product to purchase or service to utilize. In order to assist online customers in their decision-making process, business owners have started to make their online platforms more intelligent by enhancing their platforms with intelligent recommender systems. Recommender systems, also known as recommender agents or intelligent agents, are intelligent software that provide easily accessible, personalized, highly relevant, and high-quality recommendations to customers in various online platforms. This chapter discusses different types of recommender systems and provides use case examples of recommender systems in various e-commerce platforms. This chapter shows how recommender systems make life easier for online customers in the constantly developing and growing internet environment. This chapter also discusses the challenges posed by recommender systems to online customers.

A recommender system uses specific algorithms and techniques in order to suggest specific services, goods or other type of recommendations that users could be interested in. User’s preferences or ratings are used as inputs and top-N recommendations are produced by the system. The evaluation of the recommendations is usually based on accuracy metrics such as the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE), while on the other hand Precision and Recall is used to measure the quality of the top-N recommendations. Recommender systems development has been mainly focused in the development of new recommendation algorithms. However, one of the major problems in modern offline recommendation system is the sparsity of the datasets and the selection of the suitable users Y that could produce the best recommendations for users X. In this paper, we propose an algorithm that uses Fuzzy sets and Fuzzy norms in order to evaluate the correlation between users in the data set so the system can select and use only the most relevant users. At the same time, we are extending our previous work about Reproduction of experiments in recommender systems by developing new explanations and variables for the proposed new algorithm. Our proposed approach has been experimentally evaluated using a real dataset and the results show that it is really efficient and it can increase both accuracy and quality of recommendations.

In big data environments, knowledge fusion is the necessary prerequisite and effective approach to implement knowledge service. This paper firstly analyses the requirements of big data knowledge service and the contents of knowledge fusion, constructs a multi-level architecture of knowledge service based on knowledge fusion. Then, this paper presents a design of a knowledge fusion process model and analyses its implementation patterns. Finally, a system framework of big data knowledge service is proposed based on knowledge fusion processes, in which processes of both knowledge fusion and knowledge service are organically combined together to provide an effective solution to achieve personalized, multi-level and innovative knowledge service.

We propose a trust model for recommender systems in mobile virtual communities that improves the quality of recommendations. High-quality recommendations come from trustworthy recommenders and are supported by convincing (from a user's viewpoint) arguments. Such recommendations match better a user's profile and his/her way of reasoning. We also propose a mobile open multi-agent framework for our model of trust. In this framework each member is represented by an embedded agent, which resides in the user's mobile, and by a delegate agent, which runs in a meeting infrastructure (called Virtual Agora) where it interacts with other delegates. Each delegate builds and maintains two weighted and subjective networks of trust: a register of recommenders of proven (un)trustworthiness and a register of rated items. In our setting, delegates generally prefer opinions that come from their network of recommenders; moreover delegates are able to play argumentation games. The implemented system is also privacy preserving. Agents receive different ratings depending on their trustworthy recommenders and these ratings are not necessarily public. Since we address mobile communities of users, the Virtual Agora is designed to reduce communication traffic between agents. Finally, we illustrate these concepts in a recommender system for restaurants.

Knowledge discovery is a compute and data intensive process that allows for finding patterns, trends, and models in large datasets. The Grid can be effectively exploited for deploying knowledge discovery applications because of the high-performance it can offer and its distributed infrastructure. For effective use of Grids in knowledge discovery, the development of middleware is critical to support data management, data transfer, data mining and knowledge representation. To such purpose, we designed the Knowledge Grid, a high-level environment providing for Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications, composed as workflows that integrate data sources and data mining tools provided as distributed Grid services. This chapter describes the Knowledge Grid architecture and describes how its components can be used to design and implement distributed knowledge discovery applications. Then, the chapter describes how the Knowledge Grid services can be made accessible using the Open Grid Services Architecture (OGSA) model.

Identifying Healthcare Professionals With Lower Human Papillomavirus (HPV) Vaccine Recommendation Quality: A Systematic Review

The rapid growth of big data has created new opportunities for developing intelligent systems capable of supporting effective decision-making processes. In this context, recommendation systems play a crucial role by providing personalized and data-driven suggestions to users. However, traditional recommendation approaches often face limitations in scalability, accuracy, and the ability to process large-scale heterogeneous data. This paper focuses on the design of an intelligent recommendation system based on big data analytics. The study analyzes modern big data technologies and machine learning methods used for collecting, processing, and analyzing large volumes of structured and unstructured data. A system architecture is proposed that integrates data preprocessing techniques, big data platforms, and intelligent algorithms to improve recommendation quality. Experimental results demonstrate that the proposed approach enhances recommendation accuracy and system performance compared to conventional methods. The findings highlight the effectiveness of big data–driven analytics in developing scalable and intelligent recommendation systems. The results of this study can be applied in various domains such as e-commerce, education, and decision support systems, and provide a foundation for future research in intelligent data-driven system design.

In existing network teaching systems, deficiencies of the resource sharing and management are analyzed. An intelligent network teaching system based on knowledge grid is constructed to provide a uniform platform of intelligent information for teaching. The global sharing of knowledge resource will be realized by this platform. A course domain knowledge representation method based on the topic map in grid environment was put forward. Meanwhile, the domain knowledge model was built. Aiming at the problem about the knowledge resource deficiency in the present answering systems, an intelligent answering subsystem based on knowledge grid was studied and designed. By expanding the answering resource base to the Internet, this subsystem will fully mine the Internet knowledge resources by use of knowledge mining technology to complete the answering task. Details are provided about the architecture's design and main techniques of the system in this paper, including the constructing of domain knowledge structure model, intelligent answering, personalized knowledge service and so on.

Cross-domain recommender systems and transfer learning approaches are useful to help integrate knowledge from different places, so that we alleviate some existing problems (such as the cold-start problem), or improve the quality of recommender systems. With the advantages of these techniques, we host the first international workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) to provide such a forum for academia researchers and application developers from around the world to present their work and discuss exciting research ideas or outcomes. The workshop is held in conjunction with the ACM Conference on Recommender Systems 2017 on August 27th at Como, Italy.

Recommender systems are used widely in each and every web services to recommend and suggest user interested information or products. The advancement of digital information era and pain in traversing the vast amount of information made users to realise the importance of intelligent recommender systems. Intelligent recommender system suffers over many issues some among them are scrutability, scalability and in-memory purchase problem. In-memory purchase problem is one of the currently persisting recommender system problems. In this paper the deep learning models based recommendation scheme are framed to address the scrutability and in-memory purchase issue of the recommender system. Initially the working of deep learning models like feed forward and recurrent neural network are discussed. Then the recommendation schemes based on the deep learning models in addressing the issues of the recommender system are detailed.

Provider communication and HPV vaccination: The impact of recommendation quality

Different web recommendation systems have been proposed to address the problem of information overload on the Internet. They attempt to guide users toward interesting and useful items in a large information space. They anticipate the information needs of on-line users and provide them with recommendations to facilitate and personalize their navigation. There are many approaches to building such systems, but most of these systems are static which analyze information resources, discover patterns from this data and make recommendations based on the extracted knowledge. So they need to periodically update extracted pattern and rules in order to make sure they still reflect the trends of users or the changes of the site structure or content. In this paper we propose a dynamic web page recommender system based on asynchronous cellular learning automata (ACLA) which continuously interacts with the users and learns from his behavior. Furthermore, we try to use all factors which have influence on the quality of recommendation and might help system to be able to make more successful recommendations. The proposed system use web usage data and structure of the web site to learn user navigation patterns and predicting user's future requests. We evaluate our method under different settings. Our experiments on real data set show that our proposed system performs better than the other algorithm we compared to and show how this method can improve the overall quality of web recommendations.

Currently the use of web recommendation techniques is growing worldwide with aim of providing the custom-made required data to end users. Most of the web users complain about finding useful information on web sites. Web recommender systems predict the information needs of users and provide them with recommendations to facilitate their navigation. Recommender systems have been extensively explored in web mining. The web recommendation systems are split into two main categories such as collaborative recommendation system and content based recommendation system. However, the quality of recommendations and the user satisfaction with such systems are still not optimal. In adaptive association recommender systems based on web mining techniques have strengths and weaknesses. One of these weaknesses is lack of consideration of an appropriate measure to calculate the user’s interest degree of pages. Applying Fuzzy Association Rule Mining (FARM) algorithm to a recommender system leads to calculate user’s interest of pages more accurately. Consequently generated recommendations will be more desirable when compared to other association rule mining techniques.

Los sistemas de recomendación han tenido un rápido crecimiento tanto en investigación académica como en aplicaciones industriales en los últimos años. Las recomendaciones se nos proporcionan cuando elegimos elementos como música, películas, restaurantes, noticias, etc. Como parte de las técnicas de recomendación más prometedoras, los sistemas basados en recomendación de filtros colaborativos desempeñan un papel importante en las aplicaciones actuales. La aparición de nuevas fuentes de información requiere la combinación de sistemas de recomendación con técnicas avanzadas de otros campos, como el análisis de redes sociales, el procesamiento del lenguaje natural, el aprendizaje en profundidad, etc. Esta combinación mejora la calidad de las recomendaciones. Los sistemas de recomendación anteriores se centran principalmente en las mejoras de la precisión de la recomendación. Recientemente, surge una tendencia para la evaluación del rendimiento de los sistemas de recomendación que toma muchas propiedades nuevas en consideración. La fiabilidad es uno de los aspectos más notables y será profundamente estudiado en esta tesis. Se han propuesto algunas medidas para medir la fiabilidad de los sistemas de recomendación de filtrado colaborativo basados en la memoria. Sin embargo, observamos un área sin cubrir en el diseño de las medidas de fiabilidad adecuadas para los sistemas de recomendación de filtros colaborativos basados en modelos, especialmente aquellos basados en la factorización matricial. También encontramos que hay una falta de medidas de calidad de fiabilidad adecuadas. Esta tesis se concentra en proporcionar soluciones a ambas ausencias. Después de un estudio exhaustivo de trabajos de investigación del estado del arte, proponemos una nueva medida de fiabilidad que supera los desafíos de generalización y abstracción causados por los factores latentes de los métodos de factorización matricial. La medida propuesta se compara con con métodos y algoritmos representativos, usando bases de datos publicas, tanto académicas como comerciales. Los resultados experimentales muestran que nuestra nueva medida logra mejoras en la calidad de la recomendación. Además, la medida de fiabilidad propuesta también tiene un impacto muy positivo contra los "Shilling Attacks". Para obtener una mejora respecto a las medidas existentes de calidad de la fiabilidad, también proponemos, hasta donde sabemos, la primera medida de calidad de fiabilidad universal que es capaz de proporcionar valores numéricos para cualquier tipo de sistema de recomendación. Esta medida también se ha probado usando conjuntos de datos públicos reales y ha sido comparada con los métodos de calidad de la fiabilidad existentes. Basándonos en los resultados de los experimentos, concluimos que nuestra medida supera los últimos trabajos de investigación, proporcionando valores de calidad de fiabilidad más precisos e informativos. ----------ABSTRACT---------- Recommender systems have experienced a rapid growth in both academical research and industrial applications in latest years. Recommendations are provided to us when we choose items such as music, movies, restaurants, news, etc. As the most promising recommendation techniques, collaborative filtering recommender systems play an important role in current applications. The emergence of novel information sources requires to combine recommender systems with several advanced techniques coming from other fields like social network analysis, natural language processing, deep learning etc. This combination improves the quality of recommendations. Previous recommender systems research mainly focuses on the improvements of recommendation accuracy. Recently a new trend arises for the performance evaluation of recommender systems that takes many novel properties into consideration. Reliability is one of the most remarkable aspects, which will be deeply studied in this thesis. Some measures have been proposed for measuring reliability of memory based collaborative filtering recommender systems. However, we observe a gap in the design of proper reliability measures for model based collaborative filtering recommender systems, especially those based on matrix factorization. We also found that there is a lack in proper reliability quality measures. This thesis concentrates on providing solutions to both lacks. After a comprehensive study of state-of-the-art research works, we propose a novel reliability measure that overcomes the challenges of generality and abstraction caused by latent factors of matrix factorization methods. The proposed measure is tested against current baselines on public academic and commercial datasets; experiment results show that our novel measure achieves better improvements on recommendation quality. What’s more, the proposed reliability measure also has a very positive impact against shilling attacks. In order to fill in the blank of reliability quality measures, we also propose, as far as we know, the first universal reliability quality measure that is able to provide numerical values for any type of recommender systems. This measure has also been tested on public academic and commercial datasets using current methods. Based on the experiment results we conclude that our quality measure outperforms state-of-the-art research works by providing more accurate and informative reliability quality values.

Recently, online shopping has become very prominent. Furthermore, most e-commerce platforms use recommender systems to suggest products to the user for potential purchase to help enhance the user experience. Existing recommender models determine the correlation between user profiles and product information to make product recommendations. Although current recommender systems can accurately suggest products, there is a lack of transparency in terms of how the recommendations are made. As a result, users may not trust the suggestions made by what could be perceived as a black box. To address this problem, we have designed a deep recommender system that discovers user preferences and provides the reasons behind the recommendations it makes by examining clues such as user reviews and item descriptions. We adopt the attention mechanism to enhance text analysis capability, which not only enables accurate recommendations but also shows the relationship between products and user profiles. Furthermore, we conduct a case study in which we utilize feature weights to present the reasons to the user by observing attention maps. The results of our experiment show that our model can consistently provide accurate recommendations and justifications to the user. Moreover, our model can handle new items and users, that is, data that is not part of the initial training dataset. Our recommender system is a novel solution that leverages the advantages of content-based and collaborative filtering.