An Approach of Semantic Similarity Measure between Documents Based on Big Data

Abstract

Semantic indexing and document similarity is an important information retrieval system problem in Big Data with broad applications. In this paper, we investigate MapReduce programming model as a specific framework for managing distributed processing in a large of amount documents. Then we study the state of the art of different approaches for computing the similarity of documents. Finally, we propose our approach of semantic similarity measures using WordNet as an external network semantic resource. For evaluation, we compare the proposed approach with other approaches previously presented by using our new MapReduce algorithm. Experimental results review that our proposed approach outperforms the state of the art ones on running time performance and increases the measurement of semantic similarity.

A Comprehensive Survey on Comparisons across Contextual Pre-filtering, Contextual Post-filtering and Contextual Modelling Approaches

“…We summarised these comparisons in Table 2. The number of overall recommendable items (3) The performance metrics (4) The number of items in the recommendations list (5) The Type of recommendation data.…”

“…However, the excessive availability of web resources leads to the problem of information overload [2,3], in which users can easily be lost over the Cyber Ocean of information [4,5]. Recommender systems (RS) that personalise suggestions of various items and services to users emerged in the mid of 1990s to remediate the problem of information overload [6][7][8][9].…”