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A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services | IEEE Journals & Magazine | IEEE Xplore

A Fuzzy Preference Tree-Based Recommender System for Personalized Business-to-Business E-Services

Publisher: IEEE

Abstract:

The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate perso...View more

Abstract:

The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate personalized suggestions of products/services to customers (businesses or individuals). Although recommender systems have been well studied, there are still two challenges in the development of a recommender system, particularly in real-world B2B e-services: 1) items or user profiles often present complicated tree structures in business applications, which cannot be handled by normal item similarity measures and 2) online users’ preferences are often vague and fuzzy, and cannot be dealt with by existing recommendation methods. To handle both these challenges, this study first proposes a method for modeling fuzzy tree-structured user preferences, in which fuzzy set techniques are used to express user preferences. A recommendation approach to recommending tree-structured items is then developed. The key technique in this study is a comprehensive tree matching method, which can match two tree-structured data and identify their corresponding parts by considering all the information on tree structures, node attributes, and weights. Importantly, the proposed fuzzy preference tree-based recommendation approach is tested and validated using an Australian business dataset and the MovieLens dataset. Experimental results show that the proposed fuzzy tree-structured user preference profile reflects user preferences effectively and the recommendation approach demonstrates excellent performance for tree-structured items, especially in e-business applications. This study also applies the proposed recommendation approach to the development of a web-based business partner recommender system.
Published in: IEEE Transactions on Fuzzy Systems ( Volume: 23, Issue: 1, February 2015)
Page(s): 29 - 43
Date of Publication: 04 April 2014

ISSN Information:

Publisher: IEEE

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I. Introduction

With the use of recommendation methods, recommender systems [1], [2], which are web-based support systems, actively suggest a set of limited and ranked items from all available items without the direct input of users. These systems are widely used to overcome the problems created by the so-called “information explosion” in a variety of web-based applications in e-commerce [3], e-learning [4], and e-tourism, as well as in such areas as the recommendation of news, movies, books, videos, resources [5], and real estate [6]. Prior to making a recommendation, recommender systems use background data, such as historical data consisting of ratings from users, and input data, such as features of items or user ratings, to initiate a recommendation; models and algorithms combine the two and generate a recommendation [7], [8].

References

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