Abstract
As the number of digital academic items increases dramatically, it is more and more difficult for a student or researcher to find the expected references in a large academic literature database. Although collaborative filtering and content-based recommendation approaches perform well in some applications, they do not produce satisfactory recommendations for academic items because they fail to reflect researchers’ unique characteristics in terms of authority, popularity, recentness, etc. In this paper, we propose two novel data structures, ALVector, which expresses various objective attributes of an article, and AUVector, which expresses users’ subjective weights for different attributes. Then, we propose a novel personalization-oriented recommendation method that utilizes both the content and non-content attributes in ALVector and AUVector for making recommendations. In order to make the overall best recommendation, the VIKOR algorithm is used with a personalization-oriented method to achieve a compromise solution. A real-world literature data set is used in the experiments. The experimental results show that our method better meets the user’s preference in multiple dimensions simultaneously.
Highlights
As the amount of digital academic literature, such as conference proceedings, journals, books, magazines, etc., increases dramatically, it has become a time-consuming task for a student or researcher to discover a needed reference
As some statistical analyses have found that readers only read the top recommendations no matter how many items are recommended, we compared the top 10 articles recommended by our personalizationoriented method (PO) with that of the content-based method (CB) (Opricovic & Tzeng, 2004)
In order to overcome the weaknesses of traditional content-based recommendation methods in the context of academic literature recommendation, in this paper, we propose a novel personalization-oriented method, which considers content text and non-content attributes
Summary
As the amount of digital academic literature, such as conference proceedings, journals, books, magazines, etc., increases dramatically, it has become a time-consuming task for a student or researcher to discover a needed reference. Extensive work has been conducted in business recommendation systems as well as academia over the past two decades (Adomavicius & Tuzhilin, 2005). Speaking, these approaches can be classified into two categories, content-based filtering and collaborative filtering. Collaborative filtering approaches that estimate a user’s preferred item from an item preferred by users who share similar interests do not perform well in academic literature recommendation. The content-based approaches, which recommend by content or topic similarity, lack the capability to consider a user’s non-content preference and are not satisfactory in academic literature recommendation. A recommendation method that allows a user to express both content interest and personalized preference is in demand
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