AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (2.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

A Novel Popularity Extraction Method Applied inSession-Based Recommendation

School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems, Sun Yat-sen University, Guangzhou 510006, China, and Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519080, China
Show Author Information

Abstract

Popularity plays a significant role in the recommendation system. Traditional popularity is only defined as a static ratio or metric (e.g., a ratio of users who have rated the item and the box office of a movie) regardless of the previous trends of this ratio or metric and the attribute diversity of items. To solve this problem and reach accurate popularity, we creatively propose to extract the popularity of an item according to the Proportional Integral Differential (PID) idea. Specifically, Integral (I) integrates a physical quantity over a time window, which agrees with the fact that determining the attributes of items also requires a long-term observation. The Differential (D) emphasizes an incremental change of a physical quantity over time, which coincidentally caters to a trend. Moreover, in the Session-Based Recommendation (SBR) community, many methods extract session interests without considering the impact of popularity on interest, leading to suboptimal recommendation results. To further improve recommendation performance, we propose a novel strategy that leverages popularity to enhance the session interest (popularity-aware interest). The proposed popularity by PID is further used to construct the popularity-aware interest, which consistently improves the recommendation performance of the main models in the SBR community. For STAMP, SRGNN, GCSAN, and TAGNN, on Yoochoose1/64, the metric P@20 is relatively improved by 0.93%, 1.84%, 2.02%, and 2.53%, respectively, and MRR@20 is relatively improved by 3.74%, 1.23%, 2.72%, and 3.48%, respectively. On Movieslen-1m, the relative improvements of P@20 are 7.41%, 15.52%, 8.20%, and 20.12%, respectively, and that of MRR@20 are 2.34%, 12.41%, 20.34%, and 19.21%, respectively.

Keywords

popularityProportional Integral Differential (PID) algorithmsession-based recommendationuser’s interests

References

[1]

Y. Ma, J. Mao, Z. Ba, and G. Li, Location recommendation by combining geographical, categorical, and social preferences with location popularity, Inf. Process. Manag., vol. 57, no. 4, p. 102251, 2020.
Crossref Google Scholar
[2]
E. Yalcin, Blockbuster: A new perspective on popularity-bias in recommender systems, in Proc. 2021 6th Int. Conf. Computer Science and Engineering (UBMK), Ankara, Turkey, 2021, pp. 107–112.
Crossref
[3]

Z. Zhao, J. Chen, S. Zhou, X. He, X. Cao, F. Zhang, and W. Wu, Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation, IEEE Trans. Knowl. Data Eng., vol. 35, no. 10, pp. 9920–9931, 2023.
Crossref Google Scholar
[4]
J. Oh, S. Park, H. Yu, M. Song, and S. T. Park, Novel recommendation based on personal popularity tendency, in Proc. 2011 IEEE 11th Int. Conf. Data Mining, Vancouver, Canada, 2011, pp. 507–516.
Crossref
[5]
H. Abdollahpouri, M. Mansoury, R. Burke, and B. Mobasher, The connection between popularity bias, calibration, and fairness in recommendation, in Proc. 14th ACM Conf. Recommender Systems, Virtual Event, 2020, pp. 726–731.
Crossref
[6]
H. Steck, Item populartity and recommendation accucy, in Proc. 5th ACM Conf. Recommender Systems, Chicago, IL, USA, 2011, pp. 125–132.
Crossref
[7]

S. Ekinci, B. Hekimoğlu, and D. Izci, Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor, Eng. Sci. Technol. Int. J., vol. 24, no. 2, pp. 331–342, 2021.
Crossref Google Scholar
[8]
L. Wang, PID Control System Design and Automatic Tuning Using MATLAB/Simulink. Chichester, UK: John Wiley & Sons Ltd, 2020.
Crossref
[9]

A. Li, Z. Cheng, F. Liu, Z. Gao, W. Guan, and Y. Peng, Disentangled graph neural networks for session-based recommendation, IEEE Trans. Knowl. Data Eng., vol. 35, no. 8, pp. 7870–7882, 2023.
Crossref Google Scholar
[10]
H. Abdollahpouri and R. Burke, Multistakeholder recommender systems, in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, eds. New York, NY, USA: Springer, 2022, pp. 647–677.
Crossref
[11]

F. Wei, S. Chen, J. Jin, S. Zhang, H. Zhou, and Y. Wu, Adaptive alleviation for popularity bias in recommender systems with knowledge graph, Secur. Commun. Netw., vol. 2022, p. 4264489, 2022.
Crossref Google Scholar
[12]
M. Bressan, S. Leucci, A. Panconesi, P. Raghavan, and E. Terolli, The limits of popularity-based recommendations, and the role of social ties, in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 745–754.
Crossref
[13]

I. Barjasteh, R. Forsati, D. Ross, A. H. Esfahanian, and H. Radha, Cold-start recommendation with provable guarantees: A decoupled approach, IEEE Trans. Knowl. Data Eng., vol. 28, no. 6, pp. 1462–1474, 2016.
Crossref Google Scholar
[14]
J. Wang, Y. Chen, Z. Wang, and W. Zhao, Popularity-enhanced news recommendation with multi-view interest representation, in Proc. 30th ACM Int. Conf. Information & Knowledge Management, Virtual Event, 2021, pp. 1949–1958.
Crossref
[15]
A. K. Singh and K. Lakshmanan, PILHNB: Popularity, interests, location used hidden Naive Bayesian-based model for link prediction in dynamic social networks, Neurocomputing, vol. 461, pp. 562–576, 2021.
Crossref
[16]

H. J. Ahn, Utilizing popularity characteristics for product recommendation, Int. J. Electron. Commer., vol. 11, no. 2, pp. 59–80, 2006.
Crossref Google Scholar
[17]

R. Kumar, B. K. Verma, and S. S. Rastogi, Social popularity based SVD++ recommender system, Int. J. Comput. Appl., vol. 87, no. 14, pp. 33–37, 2014.
Crossref Google Scholar
[18]
Y. Zhang, F. Feng, X. He, T. Wei, C. Song, G. Ling, and Y. Zhang, Causal intervention for leveraging popularity bias in recommendation, in Proc. 44th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Virtual Event, 2021, pp. 11–20.
Crossref
[19]

M. Kumar, S. Srivastava, and N. Uddin, Forgery detection using multiple light sources for synthetic images, Aust. J. Forensic Sci., vol. 51, no. 3, pp. 243–250, 2019.
Crossref Google Scholar
[20]

C. Dhasarathan, M. Kumar, A. K. Srivastava, F. Al-Turjman, A. Shankar, and M. Kumar, A bio-inspired privacy-preserving framework for healthcare systems, J. Supercomput., vol. 77, no. 10, pp. 11099–11134, 2021.
Crossref Google Scholar
[21]
D. Tiwari, B. Nagpal, B. S. Bhati, A. Mishra, and M. Kumar, A systematic review of social network sentiment analysis with comparative study of ensemble-based techniques, Artif. Intell. Rev.
[22]
B. Hidasi, M. Quadrana, A. Karatzoglou, and D. Tikk, Parallel recurrent neural network architectures for feature-rich session-based recommendations, in Proc. 10th ACM Conf. Recommender Systems, Boston, MA, USA, 2016, pp. 241–248.
Crossref
[23]
J. Li, P. Ren, Z. Chen, Z. Ren, T. Lian, and J. Ma, Neural attentive session-based recommendation, in Proc. 2017 ACM on Conf. Information and Knowledge Management, Singapore, 2017, pp. 1419–1428.
Crossref
[24]
Q. Liu, Y. Zeng, R. Mokhosi, and H. Zhang, STAMP: Short-term attention/memory priority model for session-based recommendation, in Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, London, UK, 2018, pp. 1831–1839.
Crossref
[25]

S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan, Session-based recommendation with graph neural networks, Proc. AAAI Conf. Artif. Intell., vol. 33, no. 1, pp. 346–353, 2019.
Crossref Google Scholar
[26]
C. Xu, P. Zhao, Y. Liu, V. S. Sheng, J. Xu, F. Zhuang, J. Fang, and X. Zhou, Graph contextualized self-attention network for session-based recommendation, in Proc. 28th Int. Joint Conf. Artificial Intelligence, Macao, China, 2019, pp. 3940–3946.
Crossref
[27]
F. Yu, Y. Zhu, Q. Liu, S. Wu, L. Wang, and T. Tan, TAGNN: Target attentive graph neural networks for session-based recommendation, in Proc. 43rd Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Virtual Event, 2020, pp. 1921–1924.
Crossref
[28]

P. K. Guchhait and A. Banerjee, Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm, Prot. Contr. Mod. Power Syst., vol. 5, no. 1, pp. 1–13, 2020.
Crossref Google Scholar
[29]

Z. Gao, A tuning method via Borges derivative of a neural network-based discrete-time fractional-order PID controller with Hausdorff difference and Hausdorff sum, Fractal Fract., vol. 5, no. 1, p. 23, 2021.
Crossref Google Scholar
[30]
W. Chen, P. Ren, F. Cai, F. Sun, and M. D. Rijke, Improving end-to-end sequential recommendations with intent-aware diversification, in Proc. 29th ACM Int. Conf. Information & Knowledge Management, Virtual Event, 2020, pp. 175–184.
Crossref
Tsinghua Science and Technology
Volume 29 Issue 4,
August 2024
Pages 971-984
DOI: 10.26599/TST.2023.9010061
Cite this article:
Peng Y, Xu S, Chen Q, et al. A Novel Popularity Extraction Method Applied inSession-Based Recommendation. Tsinghua Science and Technology, 2024, 29(4): 971-984. https://doi.org/10.26599/TST.2023.9010061

466

Views

67

Downloads

1

Crossref

1

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 12 April 2023
Revised: 26 May 2023
Accepted: 12 June 2023
Published: 09 February 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return