Recommendation technique-based government-to-business personalized e-services

One of the new directions in current e-government development is to provide personalized online services to citizens and businesses. Recommendation techniques can bring a possible solution for this issue. This study proposes a hybrid recommendation approach to provide personalized government to business (G2B) e-services. The approach integrates fuzzy sets-based semantic similarity and traditional item-based collaborative filtering methods to improve recommendation accuracy. A recommender system named Intelligent Business Partner Locator (IBPL) is designed to apply the proposed recommendation approach for supporting government agencies to recommend business partners. ©2009 IEEE

A Fuzzy Tree Matching-Based Personalized E-Learning Recommender System

© 1993-2012 IEEE. The rapid development of e-learning systems provides learners with great opportunities to access learning activities online, and this greatly supports and enhances the learning practices. However, an issue reduces the success of application of e-learning systems; too many learning activities (such as various leaning materials, subjects, and learning resources) are emerging in an e-learning system, making it difficult for individual learners to select proper activities for their particular situations/requirements because there is no personalized service function. Recommender systems, which aim to provide personalized recommendations for products or services, can be used to solve this issue. However, e-learning systems need to be able to handle certain special requirements: 1) leaning activities and learners' profiles often present tree structures; 2) learning activities contain vague and uncertain data, such as the uncertain categories that the learning activities belong to; 3) there are pedagogical issues, such as the precedence relations between learning activities. To deal with the three requirements, this study first proposes a fuzzy tree-structured learning activity model, and a learner profile model to comprehensively describe the complex learning activities and learner profiles. In the two models, fuzzy category trees and related similarity measures are presented to infer the semantic relations between learning activities or learner requirements. Since it is impossible to have two completely same trees, in practice, a fuzzy tree matching method is carefully discussed. A fuzzy tree matching-based hybrid learning activity recommendation approach is then developed. This approach takes advantage of both the knowledge-based and collaborative filtering-based recommendation approaches, and considers both the semantic and collaborative filtering similarities between learners. Finally, an e-learning recommender system prototype is well designed and developed based on the proposed models and recommendation approach. Experiments are done to evaluate the proposed recommendation approach, and the experimental results demonstrate the good accuracy performance of the proposed approach. A comprehensive case study about learning activity recommendation further demonstrates the effectiveness of the fuzzy tree matching-based personalized e-learning recommender system in practice

A fuzzy content matching-based e-Commerce recommendation approach

© 2015 IEEE. E-Commerce products often come with rich and tree-structured content information describing the attributes. To well utilize the content information, this study proposed a fuzzy content matching-based recommendation approach to assist e-Commerce customers to choose their truly interested items. In this paper, users' ratings and preferences are represented using fuzzy numbers to remain uncertainties. Tree-structured content information is transformed to a set of descriptors, and users' preferences on these descriptors are derived from fuzzy ratings by using fuzzy number operations. A kind of preference dependence relations is established between descriptors to explore the relations of different content features, and as a base to sketch the complete profile of users. While the extended preference profile of a user is established, given a new item, the fuzzy match degree of the user preference and the item content information is carried out, and then a fuzzy Topsis ranking method is proposed to able to rank all candidate items according to the fuzzy match degrees, and the highest ranked items are recommended to the target user. We conduct empirical experiments on Yelp and MovieLens datasets. The results indicate that the proposed approach improve recommendation performance in terms of both coverage and accuracy

A fuzzy tree matching-based personalised e-learning recommender system

© 2014 IEEE. The rapid development of e-learning systems provides learners great opportunities to access the learning activities online, which greatly supports and enhances learning practices. However, too many learning activities are emerging in the e-learning system, which makes it difficult for learners to select proper ones for their particular situations since there is no personalised service function. Recommender systems, which aim to provide personalised recommendations, can be used to solve this issue. However, e-learning systems have two features to handle: (1) data of learners and leaning activities often present tree structures; (2) data are often vague and uncertain in practice. In this study, a fuzzy tree-structured data model is proposed to comprehensively describe the complex learning activities and learner profiles. A tree matching method is then developed to match the similar learning activities or learners. To deal with the uncertain category issues, a fuzzy category tree and relevant similarity measure are developed. A hybrid recommendation approach, which considers precedence relations between learning activities and combines the semantic and collaborative filtering similarities between learners, is developed. The proposed approach can handle the special requirements in e-learning environment and make proper recommendations in e-learning systems

Mixed Similarity Diffusion for Recommendation on Bipartite Networks

© 2013 IEEE. In recommender systems, collaborative filtering technology is an important method to evaluate user preference through exploiting user feedback data, and has been widely used in industrial areas. Diffusion-based recommendation algorithms inspired by diffusion phenomenon in physical dynamics are a crucial branch of collaborative filtering technology, which use a bipartite network to represent collection behaviors between users and items. However, diffusion-based recommendation algorithms calculate the similarity between users and make recommendations by only considering implicit feedback but neglecting the benefits from explicit feedback data, which would be a significant feature in recommender systems. This paper proposes a mixed similarity diffusion model to integrate both explicit feedback and implicit feedback. First, cosine similarity between users is calculated by explicit feedback, and we integrate it with resource-allocation index calculated by implicit feedback. We further improve the performance of the mixed similarity diffusion model by considering the degrees of users and items at the same time in diffusion processes. Some sophisticated experiments are designed to evaluate our proposed method on three real-world data sets. Experimental results indicate that recommendations given by the mixed similarity diffusion perform better on both the accuracy and the diversity than that of most state-of-the-art algorithms

Personalized government online services with recommendation techniques

University of Technology, Sydney. Faculty of Information Technology.With the integration of information from different government agencies, a vast resource of information and services may be gathered in one portal. Many businesses have difficulty locating the required information and services. In such a situation of vast information overload, one of the difficulties facing governments is how to provide businesses with information specific to their needs, rather than an undifferentiated mass of information. One way to do this is through the development of personalized government online services. Indeed, the recent Accenture e-government study indicates that personalization techniques in e-government are beginning to emerge. However, existing personalization with recommendation techniques focuses on text document retrieval and e-commerce product recommendation domain. Personalization and recommendation applications in e-government have paid relatively little research attention. Many mechanisms have been developed to deliver only relevant information to web users and prevent information overload. The most popular recent developments in the e- commerce domain are the user-preference based personalization and recommendation techniques. The existing techniques have a major drawback: they are difficulty to generate recommendation on one-and-only items, because most of them do not understand the item’s semantic features and attributes. Therefore, this study aims to: (1) propose a novel approach, semantic product relevance model and its attendant personalized recommendation technique, to handle the one-and-only item recommendation problem; (2) develop a recommender system prototype, called Smart Trade Exhibition Finder, to tailor the relevant trade exhibition information to each particular business user, and to assist export business selecting the right trade exhibitions for market promotion. Smart Trade Exhibition Finder may reduce significantly the time, cost and risk faced by exporters in selecting, entering and developing international markets. In particular, the proposed approach can be used to overcome the drawback of existing recommendation techniques and enable recommender systems to work within a much wider range of problems which cannot currently be handled. The outcome of this study will solve the rating data lacking and new item problem, and significantly improve the performance compared to existing recommendation techniques

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information