Affect of Data Filter on Performance of Latent Semantic Analysis based Research Paper Recommender System

Abstract

Latent Semantic Analysis uses Singular Value Decomposition (SVD) to effectively retrieve relevant information from the information corpus. However, LSA has a high computational cost. In order to address this aspect, it is proposed to filter only those words carrying high semantic importance. The aim is to improve the execution time of semantic space construction and dimensionality reduction. We present how the use of data filter can effectively meet the proposed goals in comparison to baseline method of performing recommendations. The proposed system was assessed over a dataset of 80 articles (Titles and Abstracts). The results of the experiments show that the proposed system performed better in terms of elapsed time with an average precision of 85.54% (78.64% for baseline method) and an average recall of 92.96% (89.70% for baseline method).