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Personalized product search based on user transaction history and hypergraph learning

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Abstract

As the e-commerce shopping websites like Amazon become more and more popular, amounts of products spring up on the internet and bring great difficulties to product search. However, the conventional text-based search is confined to retrieving products relevant to query and personalized product search is still a challenging problem in e-commerce. Consequently, in this paper, we propose a personalized product search approach, which combines personalized multimedia recommendation into searching. First, we construct a hypergraph based on products’ descriptions and user’s transaction history. Then the similarity between products and the user is calculated based on two kind of textural feature extraction methods. After that, iterative procedure is introduced to obtain the final relevance score of each product to the user. Experimental results on our collected Amazon dataset show the effectiveness of the proposed approach. The MAP@5 of our method can reach 0.48 and the MAP@10 can reach 0.44. We propose a new re-ranking method for personalized product search, in which we utilize user’s transaction history to choose products which is closer to the user’s preference into the higher positions. Experimental results on our collected dataset show that our method is much better than the comparison methods.

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References

  1. Ai Q, Zhang Y, Bi K, Chen X (2017) Learning a hierarchical embedding model for personalized product search[C]. ACM SIGIR, pp 645–654

  2. Bennett PN, Kelly D, White RW, Zhang Y (2015) Overview of the special issue on contextual search and recommendation[J]. ACM Trans Inf Syst

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

  4. Bu J, Tan S, Chen C, Wang C, Wu H, Zhang L, He X (2010) Music recommendation by unified hypergraph: combining social media information and music content[C]. ACM International conference on Multimedia:391–400

  5. Duan L, Li W, Tsang IW (2011) Improving web image search by bag-based reranking[J]. IEEE Trans Image Process 20(11):3280–3290

    MathSciNet  MATH  Google Scholar 

  6. Gao Y, Wang M, Tao D, Ji R, Dai Q (2012) 3-d object retrieval and recognition with hypergraph analysis[J]. IEEE Trans Image Process 21(9):4290–4303

    MathSciNet  MATH  Google Scholar 

  7. Gao Y, Wang M, Zha Z-J, Shen J (2013) Visual-textual joint relevance learning for tag-based social image search[J]. IEEE Trans Image Process 22(1):163–176

    MathSciNet  MATH  Google Scholar 

  8. Gray C, Beattie M, Belay H, Hill S, Lerch N (2015) Personalized online search for fashion products[C]. IEEE Systems and Information Engineering Design Symposium:91–96

  9. Gupta M, Kumar P, Mishra R (2017) Interest diffusion in heterogeneous information network for personalized item ranking[C]. ACM CIKM:2087–2090

  10. Hamano S, Ogawa T, Haseyama M (2018) Tag refinement based on multilingual tag hierarchies extracted from image folksonomy[C]. IEEE International Conference on Image Processing:1327–1331

  11. Jannach D, Ludewig M (2017) Investigating personalized search in E-commerce[C]. FLAIRS:645–650

  12. Jeh G, Widom J (2003) Scaling personalized web search[C]. WWW, pp 271–279

  13. Jiang D, Pai J, Li H (2013) Mining search and browse logs for web search: a survey[J]. ACM Trans Intell Syst Technol 4(4):1–37

    Google Scholar 

  14. Jiang S, Qian X, Fu Y, Mei T (2016) Personalized travel sequence recommendation on multi-source big social media[J]. IEEE Transactions on Big Data 1(2):43–56

    Google Scholar 

  15. Jiang S, Qian X, Shen J, Fu Y, Mei T (2018) Author topic model-based collaborative filtering for personalized POI recommendations[J]. IEEE Transactions on Multimedia 17(6):907–918

    Google Scholar 

  16. Khan MA, Jalal AS (2019) A fuzzy rule based multimodal framework for face sketch-to-photo retrieval[J]. Expert Syst Appl 134:138–152

    Google Scholar 

  17. Liu D, Hua XS, Yang L (2009) Tag ranking[C]. WWW, pp:351–360

  18. Liu Y, Shao J, Xiao J, Wu F, Zhuang Y (2013) Hypergraph spectral hashing for image retrieval with heterogeneous social contexts[J]. Neurocomputing.

  19. Liu Q, Sun Y, Wang C, Liu T, Tao D (2017) Elastic net hypergraph learning for image clustering and semi-supervised classification[J]. IEEE Trans Image Process 26(1):452–463

    MathSciNet  MATH  Google Scholar 

  20. Long B, Bian J, Dong A, Chang Y (2012) Enhancing product search by best-selling prediction in e-commerce[C]. ACM CIKM, pp:2479–2482

  21. Lu D, Liu X, Qian X (2016) Tag based image search by social re-ranking[J]. IEEE Transactions on Multimedia 18(8):1628–1639

    Google Scholar 

  22. Lu X, Zheng X, Li X (2017) Latent semantic minimal hashing for image retrieval[J]. IEEE Trans Image Process 26(1):355–368

    MathSciNet  MATH  Google Scholar 

  23. Manotumruksa J, Macdonald C, Ounis I (2017) Personalized ranking framework with multiple sampling criteria for venue recommendation[C]. ACM CIKM

    Google Scholar 

  24. Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites[J]. Mach Learn 27:313–331

    Google Scholar 

  25. Pliakos K, Kotropoulos C (2014) Personalized and geo-referenced image recommendation using unified hypergraph learning and group sparsity optimization[C]. ISCCSP:306–309

  26. Pliakos K, Kotropoulos C (2014) Social image search exploiting joint visual-textual information within a fuzzy hypergraph framework[C]. IEEE 16th International Workshop on Multimedia Signal Processing

  27. Qian X, Hua XS, Tang YY (2014) Social image tagging with diverse semantics[J]. IEEE Transactions on Cybernetics 44(12):2493–2508

    Google Scholar 

  28. Qian X, Feng H, Zhao G, Mei T (2014) Personalized recommendation combining user interest and social circle[J]. IEEE Trans Knowl Data Eng 26(7):1487–1502

    Google Scholar 

  29. Qian X, Lu D, Wang Y, Zhu L, Tang Y, Wang M (2017) Image re-ranking based on topic diversity[J]. IEEE Trans Image Process 26(8):3734–3747

    MathSciNet  MATH  Google Scholar 

  30. Qian X, Lu X, Han J, Du B (2017) On combining social media and spatial technology for personalized recommendation[C]. Proc IEEE 105(10):1937–1952

    Google Scholar 

  31. Qian X, Tan X, Zhang Y, Hong R, Wang M (2018) Enhancing sketch-based image retrieval by re-ranking and relevance feedback[J]. IEEE Trans Image Process 25(1):195–208

    MathSciNet  MATH  Google Scholar 

  32. Shi X, Qian X (2019) Exploring spatial and channel contribution for object based image retrieval[J]. Knowl-Based Syst

  33. Wang L, Zhao Z, Su F (2014) Tag-based social image search with Hyperedges correlation[C]. Visual Communication & Image Processing Conference:330–333

  34. Wang Y, Zhu L, Qian X (2018) Joint hypergraph learning for tag-based image retrieval[J]. IEEE Trans Image Process 27(9):4437–4451

    MathSciNet  MATH  Google Scholar 

  35. Wang L, Qian X, Zhang Y, Shen J, Cao C (2020) Enhancing sketch-based image retrieval by CNN semantic re-ranking[J]. IEEE Transactions on Cybernetics

  36. Wang L, Qian X, Zhang X, Hou X (2020) Sketch-based image retrieval with multi-clustering re-ranking[J]. IEEE Transactions on Circuits and Systems for Video Technology

  37. Wu C, Yan M, Si L (2017) Ensemble methods for personalized e-commerce search challenge at CIKM cup 2016[C]. ACM CIKM.

  38. Yu S, Niu D, Zhao X, Liu M (2017) Color image retrieval based on the hypergraph and the fusion of two descriptors[C]. CISP-BMEI.

  39. Zhai H, Lai S, Jin H, Qian X, Mei T (2020) Deep transfer hashing for image retrieval[J]. IEEE Transactions on Circuits and Systems for Video Technology

  40. Zhang Y, Qian X, Tan X, Han J, Tang Y (2016) Sketch-based image retrieval by salient contour reinforcement[J]. IEEE Transactions on Multimedia 18(8):1604–1615

    Google Scholar 

  41. Zhao G, Lei X, Qian X, Mei T (2019) Exploring users’internal influence from reviews for social recommendation[J]. IEEE Transactions on Multimedia 21(3):771–781

    Google Scholar 

  42. Zhao G, Liu Z, Chao Y, Qian X (2020) CAPER: context-aware personalized emoji recommendation[J]. IEEE Trans Knowl Data Eng

  43. Zhou D, Huang J, Schölkopf B (2006) Learning with hypergraphs: clustering, classification, and embedding[C]. NIPS.

  44. Zhu L, Shen J, Jin H, Zheng R, Xie L (2015) Content-based visual landmark search via multimodal hypergraph learning[J]. IEEE Transactions on Cybernetics 45(12):2756–2769

    Google Scholar 

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Funding

This work was supported in part by the NSFC under Grant 61772407, Grant 61732008; in part by the National Key R&D Program of China under Grant 2016A010101005; and in part by the World-Class Universities (Disciplines) and the Characteristic Development Guidance Funds for the Central Universities (PY3A022).

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Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

    Xuxiao Bu & Jihua Zhu

  2. Ministry of Education Key Laboratory for Intelligent Networks and Network Security, SMILES LAB, School of Information and Communication Engineering, Xi’an Jiaotong University, Xi’an, China

    Xueming Qian

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  1. Xuxiao Bu
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  2. Jihua Zhu
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Correspondence to Xueming Qian.

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Bu, X., Zhu, J., Qian, X. et al. Personalized product search based on user transaction history and hypergraph learning. Multimed Tools Appl 79, 22157–22175 (2020). https://doi.org/10.1007/s11042-020-08963-x

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  • DOI: https://doi.org/10.1007/s11042-020-08963-x

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