A Web‐based Personalized Business Partner Recommendation System Using Fuzzy Semantic Techniques

Abstract

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.

Fuzzy Tools in Recommender Systems: A Survey

“…It was also identified that several proposals such as [36,53,77,88] have focused on combining fuzzy logic with semantic web technologies, in order to improve recommendations.…”

“…Lu et al [77] Combine item-based fuzzy semantic similarity and item-based fuzzy collaborative filtering similarity…”

“…Even though, they were initially conceived to cover e-commerce domains [106,113], today they are successfully expanded to diverse scenarios such as e-learning [147], tourism [95], libraries [146], e-government [77], financial investment [89], and other application areas [76].…”

E-Assessment and Computer-Aided Prediction Methodology for Student Admission Test Score

“…Another problem arises when students are waiting for admission in specific university, meanwhile, other universities finish their admission processes and select the students, but some students can't take admission in any university due to no prediction system for admission in Universities (Lu et al, 2013). In this work, we would like to develop an online decision support system that enables the student to predict the entry test numbers by giving the age, gender, Metric Maximum marks, Metric passing year, Metric marks obtained and as well as Intermediate data.…”

“…The presented system 'BizSeeker' provides a list of suggested partners by calculating semantic similarities. This system is further extended in and named as Smart BizSeeker based on a hybrid fuzzy semantic recommendation (HFSR) (Lu, Shambour, Xu, Lin, & Zhang, 2013).…”