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A probabilistic model for semantic advertising

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Abstract

Contextual advertising focuses on placing suitable advertisements on web pages. To attract user’s intention, the advertisements should be highly related to the target web page. The most effective way to do contextual advertising is ontology-based matching algorithms. The main problem of such algorithms is the difficulty in constructing and populating the ontology for matching advertisements. In this paper, we propose an automatic construction method for advertisement ontology. The construction method searches related documents from Web, extracts keywords and weights keywords for concepts. The weighted keywords are treated as instances of concepts and used to generate centroid vectors for concepts. In order to weight keywords in a proper way, we raise a formula WebSSR (Super-Subordinate Relation by Web). WebSSR weights words based on the probabilities that they have Specific Relations with the target concept. We compare our formula with LDA, NGD, WebJaccard, WebOverlap, WebDice and WebPMI, and our formula outperforms all of them. Experiment results also show that our method is more effective than five baseline methods: Bayesian, SVM, SLSA, LDA and Paragraph2Vec.

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Notes

  1. www.giiso.com.

  2. https://business.yahoo.com/.

  3. http://www.taobao.com.

  4. http://www.baidu.com.

  5. http://search.tabao.com.

  6. http://yun.baidu.com/s/1kTxoEV1.

  7. http://yun.baidu.com/s/1qW5lXKC.

References

  1. Anagnostopoulos A, Broder AZ, Gabrilovich E, Josifovski V, Riedel L (2007) Just-in-time contextual advertising. In: Proceedings of the sixteenth ACM conference on conference on information and knowledge management. CIKM ’07, ACM, New York, NY, USA, pp 331–340

  2. Apache (2000) Apache lucene. http://lucene.apache.org/

  3. Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  4. Bollegala D, Matsuo Y, Ishizuka M (2007) Measuring semantic similarity between words using web search engines. In: Proceedings of WWW, vol 766

  5. Broder A, Fontoura M, Josifovski V, Riedel L (2007) A semantic approach to contextual advertising. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. SIGIR ’07, ACM, New York, NY, USA, pp 559–566

  6. Chen J-Y, Zheng H-T, Jiang Y, Xia S-T (2011) An adaptive approach to chinese semantic advertising. In: Lu B-L, Zhang L, Kwok J (eds) Neural Information Processing, vol 7063 of Lecture Notes in Computer Science, Springer, Berlin, pp 169–176

  7. Ciaramita M, Murdock V, Plachouras V (2008) Semantic associations for contextual advertising. J Electr Commer Res 9(1):1–15

    Google Scholar 

  8. Cilibrasi RL, Vitanyi PM (2007) The google similarity distance. IEEE Trans Knowl Data Eng 19(3):370–383

    Article  Google Scholar 

  9. Clark D (1998) Start-up plans internet search service tying results to advertising spending. Wall Street J 1

  10. Fain DC, Pedersen JO (2006) Sponsored search: a brief history. Bull Am Soc Inf Sci Technol 32(2):12–13

    Article  Google Scholar 

  11. Fan T-K, Chang C-H (2010) Sentiment-oriented contextual advertising. Knowl Inf Syst 23(3):321–344

    Article  Google Scholar 

  12. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci USA 101(Suppl 1):5228–5235

    Article  Google Scholar 

  13. Jaccard P (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaudoise Sci Nat 37:547–579

  14. Joachims T (2002) Optimizing search engines using clickthrough data. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 133–142

  15. Kazienko P, Adamski M (2007) AdROSA-adaptive personalization of web advertising. Inf Sci 177:2269–2295

    Article  Google Scholar 

  16. Le QV, Mikolov T (2014) Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053

  17. Lee J-H, Ha J, Jung J-Y, Lee S (2013) Semantic contextual advertising based on the open directory project. ACM Trans Web (TWEB) 7(4):24

    Google Scholar 

  18. Li T, Liu N, Yan J, Wang G, Bai F, Chen Z (2009) A Markov chain model for integrating behavioral targeting into contextual advertising. In: Proceedings of the third international workshop on data mining and audience intelligence for advertising, ADKDD ’09, ACM, New York, NY, USA, pp 1–9

  19. Lin J (1991) Divergence measures based on the Shannon entropy. IEEE Trans Inf Theory 37(1):145–151

    Article  MathSciNet  MATH  Google Scholar 

  20. Manning CD, Raghavan P, Schütze H et al (2008) Introduction to information retrieval, vol 1. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  21. Mei T, Hua X-S, Li S (2008) Contextual in-image advertising. In: Proceeding of the 16th ACM international conference on Multimedia, MM ’08, ACM, New York, NY, USA, pp 439–448

  22. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  23. Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41

    Article  Google Scholar 

  24. Murdock V, Ciaramita M, Plachouras V (2007) A noisy-channel approach to contextual advertising. In: Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising. ADKDD ’07, ACM, New York, NY, USA, pp 21–27

  25. ODP (2008) The open directory project. http://www.dmoz.org

  26. Ribeiro-Neto B, Cristo M, Golgher PB, Silva de Moura E (2005) Impedance coupling in content-targeted advertising. In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’05, ACM, New York, NY, USA, pp 496–503

  27. Salton G, McGill MJ (1986) Introduction to modern information retrieval. McGraw-Hill Inc., New York

    MATH  Google Scholar 

  28. Tanimoto TT (1958) An elementary mathematical theory of classification and prediction. International Business Machines Corporation, New York

    Google Scholar 

  29. Van Rijsbergen CJ (1979) Information retrieval. Butterworths, Oxford

    MATH  Google Scholar 

  30. Vargiu E, Giuliani A, Armano G (2013) Improving contextual advertising by adopting collaborative filtering. ACM Trans Web (TWEB) 7(3):13

    Google Scholar 

  31. Wang C, Zhang P, Choi R, Eredita MD (2002) Understanding consumers attitude toward advertising. In: Eighth Americas conference on information systems, Citeseer, pp 1143–1148

  32. Wikipedia (2001) Wikipedia. http://www.wikipedia.org/

  33. Wojdynski BW, Bang H (2016) Distraction effects of contextual advertising on online news processing: an eye-tracking study. Behav Inf Technol 35(8):654–664

    Article  Google Scholar 

  34. Wu Z, Xu G, Lu C, Chen E, Zhang Y, Zhang H (2013) Position-wise contextual advertising: placing relevant ads at appropriate positions of a web page. Neurocomputing 120:524–535

    Article  Google Scholar 

  35. Wu Z, Xu G, Zhang Y, Dolog P, Lu C (2012) An improved contextual advertising matching approach based on Wikipedia knowledge. Comput J 55(3):277–292

    Article  Google Scholar 

  36. Xu G, Wu Z, Li G, Chen E (2015) Improving contextual advertising matching by using wikipedia thesaurus knowledge. Knowl Inf Syst 43(3):599–631

    Article  Google Scholar 

  37. Zhang W, Tian L, Sun X, Wang H, Yu Y (2012) A semantic approach to recommending text advertisements for images. In: Proceedings of the sixth ACM conference on Recommender systems, ACM, pp 179–186

  38. Zhang W, Wang D, Xue G-R, Zha H (2012) Advertising keywords recommendation for short-text web pages using wikipedia. ACM Trans Intell Syst Technol (TIST) 3(2):36

    Google Scholar 

  39. Zheng H-T, Chen J-Y, Jiang Y (2012) An ontology-based approach to Chinese semantic advertising. Inf Sci 216(0):138–154

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by National Natural Science Foundation of China (Grant nos. 61773229 and 61771273), Natural Science Foundation of Guangdong Province (Grant no. 2014A030313745), Basic Scientific Research Program of Shenzhen City (Grant no. JCYJ20160331184440545) and Cross fund of Graduate School at Shenzhen, Tsinghua University (Grant no. JC20140001).

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Authors and Affiliations

  1. Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, Guangdong, China

    Jin-Yuan Chen, Hai-Tao Zheng, Yong Jiang & Shu-Tao Xia

  2. Shenzhen Giiso Information Technology Co., Ltd., Shenzhen, 518055, Guangdong, China

    Cong-Zhi Zhao

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  1. Jin-Yuan Chen
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  2. Hai-Tao Zheng
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  3. Yong Jiang
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  4. Shu-Tao Xia
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Correspondence to Hai-Tao Zheng.

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Chen, JY., Zheng, HT., Jiang, Y. et al. A probabilistic model for semantic advertising. Knowl Inf Syst 59, 387–412 (2019). https://doi.org/10.1007/s10115-018-1160-7

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