TY - GEN
T1 - An Improved Weighted-Removal Sentence Embedding Based Approach for Service Recommendation
AU - Li, Jingxuan
AU - Xu, Hanchuan
AU - Wang, Xiao
AU - Nie, Lanshun
AU - Xu, Xiaofei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Currently, there is a large amount of information about user requirements and service in natural language. How to measure the semantic similarity between user requirements and service description is a critical issue in service recommendation and service solution construction. In this paper, we propose a service recommendation method based on the improved Weighted-Removal(WR) sentence embedding to solve the shortcomings of traditional information retrieval methods. After data preprocessing, we use the GloVe method to obtain the word vectors and use the improved WR sentence embedding method to obtain the sentence vectors. The similarity between the vectors can be better measured. The experimental results show that the proposed improved WR method is significantly better than the traditional methods in terms of recommendation accuracy, richness, and ranking.
AB - Currently, there is a large amount of information about user requirements and service in natural language. How to measure the semantic similarity between user requirements and service description is a critical issue in service recommendation and service solution construction. In this paper, we propose a service recommendation method based on the improved Weighted-Removal(WR) sentence embedding to solve the shortcomings of traditional information retrieval methods. After data preprocessing, we use the GloVe method to obtain the word vectors and use the improved WR sentence embedding method to obtain the sentence vectors. The similarity between the vectors can be better measured. The experimental results show that the proposed improved WR method is significantly better than the traditional methods in terms of recommendation accuracy, richness, and ranking.
KW - GloVe Based Word Embedding
KW - Semantic Similarity Metrics
KW - Sentence Embedding
KW - Service Recommendation
UR - https://www.scopus.com/pages/publications/85099465095
U2 - 10.1109/ICSS50103.2020.00015
DO - 10.1109/ICSS50103.2020.00015
M3 - 会议稿件
AN - SCOPUS:85099465095
T3 - Proceedings of International Conference on Service Science, ICSS
SP - 44
EP - 50
BT - Proceedings - 2020 International Conference on Service Science, ICSS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Service Science, ICSS 2020
Y2 - 24 August 2020 through 26 August 2020
ER -