Using multi-categorization semantic analysis and personalization for semantic search
This work addresses the need for more accurate and efficient semantic search for users, though it appears incremental as it builds on existing personalization and categorization techniques.
The paper tackles the problem of improving semantic search by combining multi-categorization semantic analysis with personalization, resulting in higher search accuracy and lower extra time cost compared to existing methods.
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.