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

Abstract

The information overload on the World Wide Web results in the underuse of some existing egovernment 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. C 2011 Wiley Periodicals, Inc.

Recommender system application developments: A survey

“…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. A hybrid trust-enhanced CF recommendation (TeCF) approach, which integrates the implicit trust filtering and enhanced user-based CF approaches, was proposed [22,81] to alleviate the sparsity and cold start user problems and achieve better accuracy.…”

“…To improve similarity accuracy, an enhanced item-based CF approach was presented by combining the adjusted cosine approach with Jaccard metric as a weighting scheme. To compute the similarity between users, the Jaccard metric was used as a weighting scheme with the CPC to obtain a weighted CPC measure [22]. To deal with the disadvantage of the single-rating based approach, multicriteria collaborative filtering was developed [23].…”

Improved Recommendations Based on Trust Relationships in Social Networks

“…In this method, the value of trust is essentially computed based on the number of happy and unhappy interactions, but the definitions of the two statuses are not yet clear. In [16], Shambour and Lu developed an implicit trust filtering recommendation approach and an enhanced user-based collaborative filtering recommendation approach to select a trustworthy business partner to perform reliable business transactions. Since the paper computes the trust value based on mean ratings by users, its reliability and sensitivity is not high.…”

Evolving temporal association rules in recommender system

“…For example, as a special case of content-based systems, knowledge-based recommender systems directly compute users' favourite items based on their historic profile [11]. The computational intelligence methods apply intelligence techniques to construct proper recommendation models, including artificial neural networks [30], clustering techniques [9,31], evolutionary algorithms [17,28] and fuzzy set techniques [18]. Hybrid technologies integrate CF, CB or CI methods with the goal of developing better recommendations for items [4].…”