Using multi-categorization semantic analysis and personalization for semantic search

TL;DR

This paper introduces a novel semantic search method combining Multi-Categorization Semantic Analysis with personalization, improving search accuracy and efficiency by classifying documents into multiple categories and tailoring results to user history.

Contribution

The paper presents a new approach that integrates multi-categorization semantic analysis with personalization techniques for enhanced semantic search performance.

Findings

Outperforms existing methods in search accuracy

Reduces extra time cost in search process

Effectively classifies documents into multiple categories

Abstract

Semantic search technology has received more attention in the last years. Compared with the keyword based search, semantic search is used to excavate the latent semantics information and help users find the information items that they want indeed. In this paper, we present a novel approach for semantic search which combines Multi-Categorization Semantic Analysis with personalization technology. The MCSA approach can classify documents into multiple categories, which is distinct from the existing approaches of classifying documents into a single category. Then, the search history and personal information for users are significantly considered in analysing and matching the original search result by Term Vector DataBase. A series of personalization algorithms are proposed to match personal information and search history. At last, the related experiments are made to validate the effectiveness and efficiency of our method. The experimental results show that our method based on MCSA and personalization outperforms some existing methods with the higher search accuracy and the lower extra time cost.