JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (12): 10-21.doi: 10.6040/j.issn.1671-9352.2.2022.484

Previous Articles     Next Articles

A memory network model based on semantic expansion of text for query suggestion

Naizhou ZHANG*(),Wei CAO   

  1. School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450046, Henan, China
  • Received:2022-12-26 Online:2023-12-20 Published:2023-12-19
  • Contact: Naizhou ZHANG E-mail:zhangnz@126.com

Abstract:

A novel memory network model based on the semantic expansion of text is proposed to generate context-aware query suggestions. An attention-based hierarchical encoder-decoder model is employed, utilizing an external memory network to generate the neural attention vector between the query and the related document. The model fuses query-layer, session-layer, and document-layer semantic information. Compared with state-of-the-art approaches, our model can generate context-aware query suggestions with higher relevance. Extensive experiments using real commercial search engine query logs demonstrate the effectiveness of the proposed model.

Key words: query suggestion, semantic expansion of text, context-aware, memory network, encoder-decoder model

CLC Number: 

  • TP391

Fig.1

Architecture diagram of the memory network based on semantic expansion of text for query suggestion"

Fig.2

Framework of MNEncoder"

Table 1

Statistical information of Data Set"

数据集 划分时间区间(年-月-日) 数据集大小
ModelTrainSet 2006-03-01~2006-04-30 2 184 095
L2RTrainSet 2006-05-01~2006-05-14 638 362
ValidSet 2006-05-15~2006-05-24 328 924
TestSet 2006-05-25~2006-05-31 219 890

Table 2

Results of performance comparison of various models generating query suggestions  单位: %"

模型 BLEU-1 BLEU-2 BLEU-3 BLEU-4
HRED[2] 32.605 9.698 10.278 8.554
Transformer 31.068 7.408 8.947 7.663
SETMN+Transformer 32.102 8.326 9.311 7.832
SETMN+HRED+Att 36.005 11.815 10.071 9.685

Table 3

Results of performance comparison of various models over MS MARCO  单位: %"

模型 BLEU-1 BLEU-2 BLEU-3 BLEU-4
HRED[2] 39.126 18.426 17.417 13.685
Transformer 36.039 14.075 14.315 13.793
SETMN+Transformer 38.522 17.485 16.994 14.231
SETMN+HRED+Att 43.566 25.048 17.117 16.185

Table 4

Statistical information of data set for learn to rank"

数据集 数据集大小
L2RTrainSet* 19 108
ValidSet* 13 189
TestSet* 10 288

Table 5

MRR of query suggestion sorted by various models of learn to rank"

模型 MRR
ADJ[2] 0.511 4
Baseline Ranker(BR)[2] 0.545 9
HRED+BR[2] 0.553 3
Transformer+BR 0.548 6
SETMN+Transformer+BR 0.550 1
SETMN+HRED+Att+BR 0.566 8

Table 6

The samples of query suggestions produced by various models"

# 查询环境+尾查询不同模型生成的查询建议
ADJ HRED Transformer SETMN+Transformer SETMN+HRED+Att
1 verizoncom verizon wireless verizon wireless verizon wireless verizon wireless
verizonwirelesscom verizon phone service verizon phone next com best buy
verizon wireless verizon home phone singular wireless verizon ringtones circuit city
wwwverizoncom verizon central com aol phone verizon phone comcast net
2 frontier airlines com frontier airlines com frontier airlines southwest airlines continental airlines
frontier airlines america west airlines northwest airlines delta airlines frontier airlines
unitedairlinescom united airlines delta airlines northwest airlines southwest airlines
southwestairlinescom southwest airlines alaska airlines airline tickets delta airlines
3 aol browser aol media player internet explorer aol browser aol browser
aol cookes aol browser aol browser windows media browser settings
google aol live help aol explorer aol spyware web unlock
setup windows media player aol upgrade aol antivirus microsoft internet browser
4 baseball bats baseball bats baseball scores sporting goods pottery barn
training aids for baseball baseball glasses baseball bats baseball bats baseball tickets
batting gloves baseball warehouse com sports authority baseball hats white sox
animated bats baseball tools com baseball shoes baseball players dickssportinggoods com
5 enterprise national car rental car rental kelly rentacar toyota financial
hertz enterprise car rental avis car car rental boston city toyota
budget car rental rates avis rental auto repair car rentals
alamo budget car rental enterprise rental auto insurance leader toyota
6 map of australia us map www map united states map of america
maps of nigra falls united airlines united airlines map of canada
united states map www map weather channel list of hotels
world atlas cheap flights washington state falls of canada
7 balls fitness american idol total fitness gold gym fitness
fitness equipment american eagle american eagle weekend black fitness
fitness clubs best buy fitness equipment german wet spa
fitness arts outlet home depot total gym tv fitness for children
8 toyota cars new toyota cars mercedes benz used cars toyota dealers
nissan cars nissan dealers used cars toyota corolla honda dealers
new cars new dodge toyota suv toyota cars used toyota dealers
toyota deals nissan cars toyota dealers nissan dealers top car dealers
1 ARAMPATZIS Avi, KAMPS Jaap. A study of query length[C]//Proceedings of the 31th Annual International ACM SIGIR Conference (SIGIR). Singapore: ACM, 2008: 811-812.
2 SORDONI Alessandro, BENGIO Yoshua, VAHABI Hossein, et al. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (CIKM). Melbourne: ACM, 2015: 553-562.
3 MEI Qiaozhu, ZHOU Dengyong, CHURCH Kenneth. Query suggestion using hitting time[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM). California: ACM, 2008: 469-478.
4 CAO Huanhuan, JIANG Daxin, PEI Jian, et al. Context-aware query suggestion by mining click-through and session data[C]//Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD). Las Vegas: ACM, 2008: 875-883.
5 HU Hao, ZHANG Mingxi, HE Zhenying, et al. Diversifying query suggestions by using topics from wikipedia[C]//ACM International Conferences on Web Intelligence(WI). Atlanta: ACM, 2013: 139-146.
6 JAIN Alpa, OZERTEM Umut, VELIPASAOGLU Emre. Synthesizing high utility suggestions for rare web search queries[C]//Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR). Beijing: ACM, 2011: 805-814.
7 SHOKOUHI Milad. Learning to personalize query auto-completion[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR). Dublin: ACM, 2013: 103-112.
8 JIANG J Y, KE Y Y, CHIEN P Y, et al. Learning user reformulation behavior for query auto-completion[C]//Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR). Gold Coast: ACM, 2014: 445-454.
9 HE Qi, JIANG Daxin, LIAO Zhen, et al. Web query recommendation via sequential query prediction[C]//Proceedings of the 25th International Conference on Data Engineering(ICDE). Shanghai: IEEE, 2009: 1443-1454.
10 BAHDANAU Dzmitry, CHO Kyunghyun, BENGIO Yoshua. Neural machine translation by jointly learning to align and translate[C]//The 3rd International Conference on Learning Representations(ICLR). San Diego: ICLR, 2015.
11 VASWANI Ashish, SHAZEER Noam, PARMAR Niki, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems 30(NIPS). Long Beach: MIT, 2017: 5998-6008.
12 AHMAD Uddin Wasi, CHANG Kaiwei, WANG Hongning. Context attentive document ranking and query suggestion[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR). Paris: ACM, 2019: 385-394
13 HAN F X, NIU D, LAI K F, et al. Inferring search queries from web documents via a graph-augmented sequence to attention network[C]//Proceedings of the 2019 World Wide Web Conference on World Wide Web (WWW). San Francisco: ACM, 2019: 2792-2798.
14 ZHONG Jianling, GUO Weiwei, GAO Huiji, et al. Personalized query suggestions[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). Paris: ACM, 2020: 1645-1648.
15 MUSTAR Agnès, LAMPRIER Sylvain, PIWOWARSKI Benjamin. On the study of transformers for query suggestion[J]. ACM Transactions on Information Systems, 2022, 40(1): 18: 1-18: 27.
16 YU Shi, LIU Jiahua, YANG Jingqin, et al. Few-shot generative conversational query rewriting[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR). [S.l.]: ACM, 2020: 1933-1936.
17 NOGUEIRA Rodrigo, YANG Wei, LIN Jimmy, et al. Document expansion by query prediction[J/OL]. arXiv. 2019. http://arxiv.org/abs/1904.08375.
18 DAI Zhuyun, CALLAN Jamie. Deeper text understanding for IR with contextual neural language modeling[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR). Paris: ACM, 2019: 985-988.
19 YANG Wei, ZHANG Haotian, LIN Jimmy. Simple applications of BERT for ad hoc document retrieval[J/OL]. arXiv. 2019. https://arxiv.org/abs/1903.10972.
20 WESTON Jason, CHOPRA Sumit, BORDES Antoine. Memory networks[C]//The 3rd International Conference on Learning Representations (ICLR). San Diego: ICLR, 2015.
21 CHEN Peng, SUN Zhongqian, BING Lidong, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing(EMNLP). Copenhagen Copenhagen: [s. n. ], 2017: 452-461.
22 TANG Duyu, QIN Bing, LIU Ting. Aspect level sentiment classification with deep memory network[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP). Austin: MIT, 2016: 214-224.
23 PAPINENI Kishore, ROUKOS Salim, WARD Todd, et al. Bleu: a method for automatic evaluation of machine translation[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002). Philadelphia: MIT, 2002: 311-318.
24 HAGEN Matthias, POTTHAST Martin, BEYER Anna, et al. Towards optimum query segmentation: in doubt without[C]//Proceedings of the 21st ACM International on Conference on Information and Knowledge Management (CIKM). Maui: ACM, 2012: 1015-1024.
[1] CHEN Gui-ying. Stability analysis of generalized fuzzy bidirectional associative #br# memory networks with thresholds [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(1): 80-85.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] YANG Jun. Characterization and structural control of metalbased nanomaterials[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2013, 48(1): 1 -22 .
[2] HE Hai-lun, CHEN Xiu-lan* . Circular dichroism detection of the effects of denaturants and buffers on the conformation of cold-adapted protease MCP-01 and  mesophilic protease BP01[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2013, 48(1): 23 -29 .
[3] ZHAO Jun1, ZHAO Jing2, FAN Ting-jun1*, YUAN Wen-peng1,3, ZHANG Zheng1, CONG Ri-shan1. Purification and anti-tumor activity examination of water-soluble asterosaponin from Asterias rollestoni Bell[J]. J4, 2013, 48(1): 30 -35 .
[4] SUN Xiao-ting1, JIN Lan2*. Application of DOSY in oligosaccharide mixture analysis[J]. J4, 2013, 48(1): 43 -45 .
[5] LUO Si-te, LU Li-qian, CUI Ruo-fei, ZHOU Wei-wei, LI Zeng-yong*. Monte-Carlo simulation of photons transmission at alcohol wavelength in  skin tissue and design of fiber optic probe[J]. J4, 2013, 48(1): 46 -50 .
[6] YANG Lun, XU Zheng-gang, WANG Hui*, CHEN Qi-mei, CHEN Wei, HU Yan-xia, SHI Yuan, ZHU Hong-lei, ZENG Yong-qing*. Silence of PID1 gene expression using RNA interference in C2C12 cell line[J]. J4, 2013, 48(1): 36 -42 .
[7] MAO Ai-qin1,2, YANG Ming-jun2, 3, YU Hai-yun2, ZHANG Pin1, PAN Ren-ming1*. Study on thermal decomposition mechanism of  pentafluoroethane fire extinguishing agent[J]. J4, 2013, 48(1): 51 -55 .
[8] YANG Ying, JIANG Long*, SUO Xin-li. Choquet integral representation of premium functional and related properties on capacity space[J]. J4, 2013, 48(1): 78 -82 .
[9] LI Yong-ming1, DING Li-wang2. The r-th moment consistency of estimators for a semi-parametric regression model for positively associated errors[J]. J4, 2013, 48(1): 83 -88 .
[10] DONG Wei-wei. A new method of DEA efficiency ranking for decision making units with independent subsystems[J]. J4, 2013, 48(1): 89 -92 .