skip to main content
10.1145/3340017.3340025acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiceegConference Proceedingsconference-collections
research-article

Research on Product Novelty Recommendation Based on User Demands

Published: 18 June 2019 Publication History

Abstract

The recommender systems play an important role in alleviating information overload, extracting interesting commodities for users and improving user personalized experience. Today, with the increasing abundant of products and the personalized needs of users, the tradition recommendation systems fail to enhance user satisfaction because they overemphasis the accuracy of results. The recommendation researches have increasingly focused towards introducing novelty in user recommendation lists. Existing methods aim to find the right balance between the similarity and novelty of the recommended items. However, they ignore the different user demands for the accuracy and novelty. Therefore, this paper further analyzes user characteristics according to product types and quantity selected by users, constructs users' demands using the information entropy theory and proposes an adaptive random walk model based on user demands. The experimental results show that the proposed model can adaptively meet users' needs for novelty while ensuring accuracy and enrich the models of the novelty recommendation.

References

[1]
Mcnee, S., Riedl, J., and Konstan, J. 2006. Being accurate is not enough: How accuracy metrics have hurt recommender systems. Conference on Human Factors in Computing Systems, (Montréal, Québec, Canada. April 22-27, 2006) CHI. ACM, New York, NY, 1097--1101.
[2]
Lü, L., Medo, M., Chi, H. Y., Zhang, Y. C., Zhang, Z. K., and Zhou, T.2012 Recommender systems. Physics Reports.519, 1 (Oct. 2012), 1--49.
[3]
Hurley, N. and Zhang, M. 2011. Novelty and diversity in top-n recommendation -- analysis and evaluation. ACM Transactions on Internet Technology.10, 4 (March 2011), 1--30.
[4]
Onuma, K., Tong, H., and Faloutsos, C. 2009.TANGENT: a novel, 'Surprise me', recommendation algorithm. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Paris, France. Paris, June 28- July, 2009). KDD'09. ACM, New York, NY, 657--666.
[5]
Weng, L.-T., Xu, Y., Li, Y., and Nayak, R. 2007. Improving recommendation novelty based on topic taxonomy. In Proceedings 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Workshops (WI-IATW). 115-118.
[6]
Oh, J., Park, S., Yu, H., Song, M., and Park, S. T. 2011. Novel Recommendation Based on Personal Popularity Tendency. In Proceedings of the IEEE 11th International Conference on Data Mining (ICDM'11). 507--516.
[7]
Chen, L.-J. and Gao, J. 2018.A trust-based recommendation method using network diffusion processes. Physica A: Statistical Mechanics and its Applications. (Mar. 2018), 679--691.
[8]
Oku, K. and Hattori, F. 2013.Fusion-based Recommender System for Serendipity-Oriented Recommendations. Journal of Japan Society for Fuzzy Theory & Intelligent Informatics. 25, 1 (2013), 524--539.
[9]
Burt, R. S. 1992. Structural holes: the social structure of competition. Cambridge: Harvard University Press.
[10]
Yu, Q., Peng, Z.-Y., Hong, L., and Wan Y.-l. 2016. Novel Web Community Recommendation Based on User Neighborhood and Topic. Journal of Software.27, 5 (2016), 1266--1284.
[11]
Newman, M. J. A measure of betweenness centrality based on random walks. Social Networks, 2003, 27(1): 39--54.
[12]
Shan, S., Shi, J. and Qi, Y. 2017. Blog Recommendation and Management Implications in an Emergency Context: An Information Entropy Perspective. Asia-Pacific Journal of Operational Research.34, 1 (2017), 1740007.
[13]
Kapoor, K., Kumar, V., Terveen, L., Konstan, J.A., and Schrater, P. 2015. "I like to explore sometimes": Adapting to dynamic user novelty preferences. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys '15). 19-26.
[14]
Yu, B., Chen, G.-W. Wang, A.-L., and Lin, C. 2017. Hybrid Recommendation Algorithm Combined with the Project Properties. Computer Systems & Applications.26, 1 (Feb. 2017), 147--151.
[15]
Chou, S.-Y., Yang, Y.-H., Jang, J. S. R., and Lin, Y.-C.2016. Addressing cold start for next-song recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. (Boston, MA, USA, September 15-19, 2016). RecSys '16, ACM, New York, NY, 2016: 115--118.
[16]
Kotkov, D., Wang, S., and Veijalainen, J. 2016. A survey of serendipity in recommender systems. Knowledge-Based Systems.111, 1(Nov.2016), 180--19.

Cited By

View all
  • (2023)A Hybrid Laptop Recommendation System for Engineering Undergraduates2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)10.1109/CISES58720.2023.10183587(967-973)Online publication date: 28-Apr-2023
  • (2020)A Meta-Level Hybrid Recommendation Method Based on User NoveltyProceedings of the 3rd International Conference on Information Technologies and Electrical Engineering10.1145/3452940.3453060(616-625)Online publication date: 3-Dec-2020

Index Terms

  1. Research on Product Novelty Recommendation Based on User Demands

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICEEG '19: Proceedings of the 3rd International Conference on E-commerce, E-Business and E-Government
    June 2019
    113 pages
    ISBN:9781450362375
    DOI:10.1145/3340017
    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]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 June 2019

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. accuracy
    2. novelty
    3. recommendation model
    4. user demand

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ICEEG 2019

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)6
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 16 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)A Hybrid Laptop Recommendation System for Engineering Undergraduates2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)10.1109/CISES58720.2023.10183587(967-973)Online publication date: 28-Apr-2023
    • (2020)A Meta-Level Hybrid Recommendation Method Based on User NoveltyProceedings of the 3rd International Conference on Information Technologies and Electrical Engineering10.1145/3452940.3453060(616-625)Online publication date: 3-Dec-2020

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media