Enhancing recommendation systems performance using highly-effective similarity measures

Document Type

Article

Source of Publication

Knowledge-Based Systems

Publication Date

4-6-2021

Abstract

© 2021 Elsevier B.V. In Recommendation Systems (RS) and Collaborative Filtering (CF), the similarity measures have been the operating component upon which CF performance is essentially reliant. A dozen of similarity measures have been proposed to reach the desired performance particularly under the circumstances of data sparsity (the cold-start problem). Nevertheless, these measures still suffer the cold-start problem, and have a complex design. Moreover, a comprehensive experimental work to study the impact of the cold-start problem on CF performance is still missing. To these ends, therefore, this paper introduces three simply-designed similarity measures, namely, difference-based similarity measure (SMD), hybrid difference-based similarity measure (HSMD), and, triangle-based cosine measure (TA). Along with proposing these measures, a comprehensive experimental guide for CF measures using the K-fold cross validation is also presented. In contrary to all previous CF studies, the evaluation process is split into two sub-processes: the estimation process and recommendation process to accurately obtain the desired appropriateness in the evaluation. In addition, a new formula to calculate the dynamic recommendation count is developed depending on both the dataset and rating vectors. To draw a comprehensive experimental analysis, a dozen state-of-the-art similarity measures (30 similarity measures) including the proposed and the most widely-used traditional measures are comparatively tested. The experimental study has critically been made on three datasets with five-fold cross-validation grounded on the K nearest neighbor algorithm (KNN). The obtained results on both estimation and recommendation processes prove unquestionably that SMD and TA are preeminent measures with the lowest computational complexity outperforming all state-of-the-art CF measures.

ISSN

0950-7051

Publisher

Elsevier BV

Volume

217

Disciplines

Computer Sciences

Keywords

Collaborating filtering, Cross validation, Empirical evaluation, KNN algorithm, Recommendation systems, Similarity

Scopus ID

85100971504

Indexed in Scopus

yes

Open Access

no

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