Retrieval and Ranking of Scientific Documents Based on LSB and TOPSIS

Fusing mathematical expressions, related text features, and document attributes is vital for improving the retrieval and ranking performance of scientific documents based on mathematical information. However, due to the specificity of mathematical expressions and their contextual relationships, as well as the diversity of document attributes, they are challenging to utilize fully in existing models. To address these issues, a retrieval and ranking model of scientific documents based on LSB LDA-SBERT and TOPSIS Technique for Order Preference by Similarity to an Ideal Solution is proposed. Firstly, the mathematical expressions are analyzed using a symbol-level multidimensional parsing algorithm, and hesitant fuzzy sets are introduced to calculate the similarity of mathematical expressions. Then, the LSB model is used to analyze the mathematical expression contexts, extract the contextual features, and calculate text similarity accordingly. By integrating the similarity of mathematical expressions and contexts, the preliminary retrieval results are obtained. Finally, the document attribute set is constructed, and the TOPSIS is used to calculate the influence weight of documents and weight it against the preliminary results to achieve a more precise and influential ranking of scientific documents. Experimental results show that the average MAP_10 is 85.5 and the average NDCG@10 is 87.8 .