An effective recommender system by unifying user and item trust information for B2B applications

© 2015 Elsevier Inc. Although Collaborative Filtering (CF)-based recommender systems have received great success in a variety of applications, they still under-perform and are unable to provide accurate recommendations when users and items have few ratings, resulting in reduced coverage. To overcome these limitations, we propose an effective hybrid user-item trust-based (HUIT) recommendation approach in this paper that fuses the users' and items' implicit trust information. We have also considered and computed user and item global reputations into this approach. This approach allows the recommender system to make an increased number of accurate predictions, especially in circumstances where users and items have few ratings. Experiments on four real-world datasets, particularly a business-to-business (B2B) case study, show that the proposed HUIT recommendation approach significantly outperforms state-of-the-art recommendation algorithms in terms of recommendation accuracy and coverage, as well as significantly alleviating data sparsity, cold-start user and cold-start item problems

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

Control Theoretical Modeling of Trust-Based Decision Making in Food-Energy-Water Management

We propose a hybrid Human-Machine decision making to manage Food-Energy-Water resources. In our system trust among human actors during decision making is measured and managed. Furthermore, such trust is used to pressure human actors to choose among the solutions generated by algorithms that satisfy the community’s preferred trade-offs among various objectives. We model the trust-based loops in decision making by using control theory. In this system, the feedback information is the trust pressure that actors receive from peers. Using control theory, we studied the dynamics of the trust of an actor. Then, we presented the modeling of the change of solution distances. In both scenarios, we also calculated the settling times and the stability using the transfer functions and their Z-transforms as the number of rounds to show whether and when the decision making is finalized

A framework of hybrid recommendation system for government-to-business personalized e-services

One of the challenges facing e-governments is how to provide businesses with services and information specific to their needs, rather than an undifferentiated mass of information. One way to achieve this is through the design and development of personalized government e-services using recommendation systems. To this purpose, this study presents a personalized hybrid recommender system framework to handle personalized recommendations in G2B e-services, in particular, business partner matching e-services. The proposed framework employs a hybrid trust-based multi-criteria recommendation model which integrates the techniques of trust-based filtering with the multi-criteria CF. The proposed system can be used to reduce the time, cost and risk of businesses involved in entering international markets and thus improve the quality of G2B e-services. © 2010 IEEE

A hybrid Multi-Criteria Semantic-enhanced collaborative filtering approach for personalized recommendations

Recommender systems aim to assist web users to find only relevant information to their needs rather than an undifferentiated mass of information. Collaborative filtering (CF) techniques are probably the most popular and widely adopted techniques in recommender systems. Despite of their success in various applications, CF-based techniques still encounter two major limitations, namely sparsity and coldstart problems. More recently, semantic information of items has been successfully used in recommender systems to alleviate such problems. Moreover, the incorporation of multi-criteria ratings in recommender systems can help to produce more accurate recommendations. Thereby, in this paper, we propose a hybrid Multi-Criteria Semantic-enhanced CF (MC-SeCF) approach. The MC-SeCF approach integrates the enhanced MC item-based CF and the item-based semantic filtering approaches to alleviate current limitations of the item-based CF techniques. Experimental results demonstrate the effectiveness of the proposed MC-SeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold-start items compared to benchmark item-based CF techniques. © 2011 IEEE

A trust-semantic fusion-based recommendation approach for e-business applications

Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsity, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust-Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach. © 2012 Elsevier B.V. All rights reserved

A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services

The information overload on the World Wide Web results in the underuse of some existing e-government services within the business domain. Small-to-medium businesses (SMBs), in particular, are seeking "one-to-one'' e-services from government in current highly competitive markets, and there is an imperative need to develop Web personalization techniques to provide business users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-governments can support businesses on the problem of selecting a trustworthy business partner to perform reliable business transactions. In the business partner selection process, trust or reputation information is crucial and has significant influence on a business user's decision regarding whether or not to do business with other business entities. For this purpose, an intelligent trust-enhanced recommendation approach to provide personalized government-to-business (G2B) e-services, and in particular, business partner recommendation e-services for SMBs is proposed. Accordingly, in this paper, we develop (1) an implicit trust filtering recommendation approach and (2) an enhanced user-based collaborative filtering (CF) recommendation approach. To further exploit the advantages of the two proposed approaches, we develop (3) a hybrid trust-enhanced CF recommendation approach (TeCF) that integrates both the proposed implicit trust filtering and the enhanced user-based CF recommendation approaches. Empirical results demonstrate the effectiveness of the proposed approaches, especially the hybrid TeCF recommendation approach in terms of improving accuracy, as well as in dealing with very sparse data sets and cold-start users. © 2011 Wiley Periodicals, Inc

Government-to-Business personalized e-services using semantic-enhanced recommender system

The information overload problem results in the under-use of some existing e-Government services. Recommender systems have proven to be an effective solution to the information overload problem by providing users with information and services specific to their needs, rather than an undifferentiated mass of information. This paper focuses on how e-Governments can support businesses, which are seeking 'one-to-one' e-services, on the problem of finding adequate business partners. For this purpose, a Hybrid Semantic-enhanced Collaborative Filtering (HSeCF) recommendation approach to provide personalized Government-to-Business (G2B) e-services, and in particular, business partner recommendation e-services for Small to Medium Businesses is proposed. Experimental results on two data sets, MovieLens and BizSeeker, show that the proposed HSeCF approach significantly outperforms the benchmark item-based CF algorithms, especially in dealing with sparsity or cold-start item problems. © 2011 Springer-Verlag Berlin Heidelberg

Integrating Multi-Criteria Collaborative Filtering and Trust filtering for personalized Recommender Systems

Recommender Systems are information systems that attempt to recommend items of interest to particular users based on their explicit and implicit preferences. Multi-Criteria Decision Making (MCDM) aims at assisting the decision maker in the decision making process, or giving the decision maker a recommendation, concerning a set of actions, alternatives, items etc. Thus, despite their differences, Recommender Systems and Multi-Criteria Decision Making share the same objective which is supporting the decision making process and reducing information overload. In this paper we propose a novel hybrid Multi-Criteria Trust-enhanced CF (MC-TeCF) approach. The proposed MC-TeCF approach combines the MC user-based CF and the MC user-based Trust filtering approaches to alleviate the standard Single-Criteria user-based CF limitations. Empirical results demonstrate the significance and effectiveness of the proposed MC-TeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold start users compared with the standard Single-Criteria user-based CF approach. © 2011 IEEE

A web-based personalized business partner recommendation system using fuzzy semantic techniques

The web provides excellent opportunities to businesses in various aspects of development such as finding a business partner online. However, with the rapid growth of web information, business users struggle with information overload and increasingly find it difficult to locate the right information at the right time. Meanwhile, small and medium businesses (SMBs), in particular, are seeking "one-to-one" e-services from government in current highly competitive markets. How can business users be provided with information and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this study is the development of personalized e-services. Recommender systems is an effective approach for the implementation of Personalized E-Service which has gained wide exposure in e-commerce in recent years. Accordingly, this paper first presents a hybrid fuzzy semantic recommendation (HFSR) approach which combines item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering (CF) similarity techniques. This paper then presents the implementation of the proposed approach into an intelligent recommendation system prototype called Smart BizSeeker, which can recommend relevant business partners to individual business users, particularly for SMBs. Experimental results show that the HFSR approach can help overcome the semantic limitations of classical CF-based recommendation approaches, namely sparsity and new "cold start" item problems. © 2012 Wiley Periodicals, Inc