research-article

A Meta-Level Hybrid Recommendation Method Based on User Novelty

Authors:

Yong JiangAuthors Info & Claims

ICITEE '20: Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering

Pages 616 - 625

https://doi.org/10.1145/3452940.3453060

Published: 17 May 2021 Publication History

Abstract

In current online recommender systems, multiple recommendation methods are commonly performed to meet user personalized needs. However, these methods usually ignore each individual's differentiated latent psychological factors, such as user personality on openness, which reflects the potential probability of selecting new items. Nowadays, more and more users with no clear goals, called blind-novel-hunting users, explore the novel content on pages instead of purchasing to enhance their self-pleasure. This may indicate that people with high novelty tend to try new and unseen things, while ones with low novelty prefer to choose the familiar objects repeatedly. This paper proposes a Meta-level hybrid recommendation method considering of user novelty (UNB-HM). The method could generate the final personalized recommendation TOP-N0/1 by introducing the real-time prediction on openness and a secondary filtering algorithm in accordance with the law of memory forgetting. In order to validate the efficiency to the existing recommendation algorithms, we use e-commercial user behaviors to complete comparative experiments. The results show that, our method is suitable for the user's openness differences, and after the superimposing usage, several algorithms can effectively im-prove the personalized recommendation effectiveness. Addi-tionally, it has also indicated the advantages in scalability and time flexibility

References

[1]

W. Hill, L. Stead, M. Rosenstein, and G. Furnas, "Recommending and evaluating choices in a virtual community of use", Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp. 194--201, 1995.

Digital Library

[2]

G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions", Knowledge and Data Engineering, IEEE Transactions, vol.17, no.6, pp. 734--749, 2005.

Digital Library

[3]

T. Ma, L. Guo, M. Tang, et al., "A Collaborative Filtering Recommendation Algorithm Based on Hierarchical Structure and Time Awareness", IEICE Transactions on Information and Systems, pp. 1512--1520, 2016.

[4]

M.R. McLaughlin and J.L. Herlocker, "A collaborative filtering algorithm and evaluation metric that accurately model the user experience", Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, ACM New York, pp. 329--336, 2004.

Digital Library

[5]

M.A. Ghazanfar, A. Prugel-Bennett, "A scalable, accurate hybrid recommender system", Knowledge Discovery and Data Mining, pp. 94--98, 2010.

[6]

M.D. Ekstrand, J. Riedl and J.A. Konstan, "Collaborative Filtering Recommender Systems", Foundations and Trends® in Human-Computer Interaction, pp. 175--243, 2011.

[7]

Hongwu Ye, "A Personalized Collaborative Filtering Recommendation Using Association Rules Mining and Self-Organizing Map", Computer Science, 2011.

[8]

Ozgur Cakir, Murat Efe Aras, "A recommendation engine by using association rules", Social and Behavioral Sciences, World Conference on Business, Economics and Management 2012, pp. 452--456, 2012.

[9]

Souvik Debnath, Niloy Ganguly, Pabitra Mitra, "Feature Weighting in Content Based Recommendation System Using Social Network Analysis", Proceedings of the 17th international conference on World Wide Web, pp. 1041--1042, 2008.

Digital Library

[10]

Márcio M. Soares, Paula Viana "Tuning Metadata for Better Movie Content-based Recommendation Systems", International Journals, Multimedia Tools and Applications, vol.74, pp. 7015--7036, 2015.

Digital Library

[11]

Chuan Shi, Chong Zhou, Xiangnan Kong, et al., "HeteRecom: A semantic-based recommendation system in heterogeneous networks", Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1552--1555, 2012.

Digital Library

[12]

Jizhe Wang, Pipei Huang, Huan Zhao, et al., "Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba", Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 839--848, 2018.

Digital Library

[13]

W. Sen, Z. Xiaonan, and D. Yanan, "A Collaborative Filtering Recommender System Integrated with Interest Drift based on Forgetting Function", International Journal of u- and e- Service, Science and Technology, pp. 247--264, 2015.

[14]

Fuzheng Zhang, Kai Zheng, Nicholas Jing Yuan, et al., "A Novelty-Seeking based Dining Recommender System", Proceedings of the 24th International Conference on World Wide Web, pp. 1362--1372, 2015.

Digital Library

[15]

Komal Kapoor, Vikas Kumar, Loren Terveen, et al., "I like to explore sometimes: Adapting to Dynamic User Novelty Preferences", Proceedings of the 9th ACM Conference on Recommender Systems, pp. 19--26, 2015.

Digital Library

[16]

K Punyavathi, Jyothi P, "Recommendation Techniques to Improve Diversity and Novelty Based on User Behaviour", International Journal of Engineering Research & Technology, 2013.

[17]

Aarushi Gaur, Krystian Mikolajczyk, "Ranking Images Based on Aesthetic Qualities", 22nd International Conference on Pattern Recognition, IEEE, 2014.

[18]

Juliana Alves Pereira, Pawel Matuszyk, Sebastian Krieter, et al., "A feature-based personalized recommender systems for product-line configuration processes", Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, pp. 120--131, 2016

Digital Library

[19]

Wen Wu, Li Chen, Yu Zhao, "Personalizing recommendation diversity based on user personality", User Modeling and User-Adapted Interaction, pp. 237--276, 2018.

Digital Library

[20]

Alan M, "A Hybrid Recommendation System Based on Human Curiosity", Proceedings of the 9th ACM Conference on Recommender Systems, pp. 367--370, 2015.

[21]

Saúl Vargas, "Novelty and diversity enhancement and evaluation in recommender systems and information retrieval", Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp.1281, 2014.

Digital Library

[22]

Meera Komarraju, Steven J. Karau, Ronald R. Schmeck, et al., "The Big Five personality traits, learning styles, and academic achievement", Personality and Individual Differences, pp. 472--477, 2011.

[23]

Liang Zhang, "The Definition of Novelty in Recommendation System", Journal of Engineering Science and Technology Review, pp. 141--145, 2013.

[24]

Yan Guo, Minxi Wang, Xin Li, "An Interactive Personalized Recommendation System: Using the Hybrid Algorithm Model", Computer Science-Symmetry, pp. 9(10)-216, 2017.

[25]

Arun Kumar, Paul Schrater, "Novelty Learning via Collaborative Proximity Filtering", Proceedings of the 22nd International Conference on Intelligent User Interfaces, pp. 601--610, 2017.

Digital Library

[26]

Xiyu Wu, Qimai Chen, Hai Liu, et al., "Collaborative filtering recommendation algorithm based on representation learning of knowledge graph", Computer Engineering, vol. 44, no. 2, pp. 226--232, 263, 2018.

[27]

Pietro Gravino, Bernardo Monechi, "Vittorio Loreto Towards novelty-driven recommender systems", Comptes Rendus Physique, pp. 371--379, 2019.

[28]

Xu Yuanping, Chen Xiang, "Research on Product Novelty Recommendation Based on User Demands", Proceedings of the 2019 3rd International Conference on E-commerce, E-Business and E-Government, pp. 68--73, 2019

Digital Library

Cited By

Bodduluri KPalma FKurti AJusufi ILöwenadler H(2024)Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.336582812(28273-28296)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3365828
Stalidis GKaraveli IDiamantaras KDelianidi MChristantonis KTektonidis DKatsalis ASalampasis M(2023)Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable MarketingSustainability10.3390/su15231615115:23(16151)Online publication date: 21-Nov-2023
https://doi.org/10.3390/su152316151

Index Terms

A Meta-Level Hybrid Recommendation Method Based on User Novelty
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Collaborative filtering
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

ICITEE '20: Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering

December 2020

687 pages

ISBN:9781450388665

DOI:10.1145/3452940

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 May 2021

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the Shenzhen Nanshan District Ling-Hang Team Grant

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ICITEE2020

ICITEE2020: The 3rd International Conference on Information Technologies and Electrical Engineering

December 3 - 5, 2020

Hunan, Changde City, China

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Cited By

Bodduluri KPalma FKurti AJusufi ILöwenadler H(2024)Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2024.336582812(28273-28296)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3365828
Stalidis GKaraveli IDiamantaras KDelianidi MChristantonis KTektonidis DKatsalis ASalampasis M(2023)Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable MarketingSustainability10.3390/su15231615115:23(16151)Online publication date: 21-Nov-2023
https://doi.org/10.3390/su152316151

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