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LSTM-Based Neural Network Model for Semantic Search

In: Smart Service Systems, Operations Management, and Analytics

Author

Listed:
  • Xiaoyu Guo

    (Nanjing University of Aeronautics and Astronautics)

  • Jing Ma

    (Nanjing University of Aeronautics and Astronautics)

  • Xiaofeng Li

    (Nanjing University of Aeronautics and Astronautics)

Abstract

To improve web search quality and serve a better search experience for users, it is important to capture semantic information from user query which contains user’s intention in web search. Long Short-Term MemoryLong Short-Term Memory (LSTM) (LSTM), a significant network in deep learningDeep learning has made tremendous achievements in capturing semantic information and predicting the semantic relatedness of two sentences. In this study, considering the similarity between predicting the relatedness of sentence pair task and semantic searchSemantic search, we provide a novel channel to process semantic search task: see semantic search as an atypical predicting the relatedness of sentence pair task. Furthermore, we propose an LSTM-Based Neural Network Model which is suitable for predicting the semantic relatedness between user query and potential documents. The proposed LSTM-Based Neural Network Model is trained by Home Depot dataset. Results show that our model outperforms than other models.

Suggested Citation

  • Xiaoyu Guo & Jing Ma & Xiaofeng Li, 2020. "LSTM-Based Neural Network Model for Semantic Search," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), Smart Service Systems, Operations Management, and Analytics, pages 17-25, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-30967-1_3
    DOI: 10.1007/978-3-030-30967-1_3
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