Abstract
Product search has been receiving significant attention with the development of e-commerce . Existing works recognize the importance of personalization and focus on personalized product search. While these works have confirmed that personalization can improve the performance of product search, they all ignore the few-shot learning problems caused by personalization. Under the few-shot setting, personalized methods may suffer from the data-hungry issue. In this paper, we explore the data-hungry issue in personalized product search. We find that data-hungry issue exists under the few-shot setting caused by personalization, and degrades the performance under the few-shot setting when the input query consists of diverse intents. Furthermore, we illustrate that with such a data-hungry issue, the returned search results tend to be close to the products the user purchases most often, or the products the most users purchase in the market given the same query. The result in the further experiment confirms our conclusions.
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Ai, Q., Hill, D.N., Vishwanathan, S., Croft, W.B.: A zero attention model for personalized product search. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 379–388 (2019)
Ai, Q., Zhang, Y., Bi, K., Chen, X., Croft, W.B.: Learning a hierarchical embedding model for personalized product search. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 645–654 (2017)
Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice, vol. 520. Addison-Wesley Reading (2010)
Duan, H., Zhai, C., Cheng, J., Gattani, A.: Supporting keyword search in product database: a probabilistic approach. Proc. VLDB Endow. 6(14), 1786–1797 (2013)
Guo, Y., Cheng, Z., Nie, L., Wang, Y., Ma, J., Kankanhalli, M.: Attentive long short-term preference modeling for personalized product search. ACM Trans. Inf. Syst. (TOIS) 37(2), 1–27 (2019)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422–446 (2002)
Liang, S., Tang, S., Meng, Z., Zhang, Q.: Cross-temporal snapshot alignment for dynamic networks. IEEE Trans. Knowl. Data Eng. (TKDE) (2022, to appear)
Liang, S., Yilmaz, E., Kanoulas, E.: Collaboratively tracking interests for user clustering in streams of short texts. IEEE Trans. Knowl. Data Eng. (TKDE) 31(2), 257–272 (2019)
Liu, S., Gu, W., Cong, G., Zhang, F.: Structural relationship representation learning with graph embedding for personalized product search. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 915–924 (2020)
McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 43–52 (2015)
Pan, Y., Meng, Z., Liang, S.: Personalized, sequential, attentive, metric-aware product search. ACM Tran. Inf. Syst. (TOIS) 10, 1–29 (2022)
van der Ploeg, T., Austin, P.C., Steyerberg, E.W.: Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med. Res. Methodol. 14(1), 1–13 (2014)
Sondhi, P., Sharma, M., Kolari, P., Zhai, C.: A taxonomy of queries for e-commerce search. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1245–1248 (2018)
Su, N., He, J., Liu, Y., Zhang, M., Ma, S.: User intent, behaviour, and perceived satisfaction in product search. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining, pp. 547–555 (2018)
Van Gysel, C., de Rijke, M., Kanoulas, E.: Learning latent vector spaces for product search. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 165–174 (2016)
Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. (CSUR) 53(3), 1–34 (2020)
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Wu, B., Wu, Y., Liang, S. (2022). Data-Hungry Issue in Personalized Product Search. In: Shen, H., et al. Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science(), vol 13148. Springer, Cham. https://doi.org/10.1007/978-3-030-96772-7_45
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DOI: https://doi.org/10.1007/978-3-030-96772-7_45
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