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Session-Based Recommender System for Sustainable Digital Marketing

Author

Listed:
  • Hyunwoo Hwangbo

    (Graduate School of Information, Yonsei University, 50 Yonsei-ro, Seodaemun-Gu, Seoul 03722, Korea)

  • Yangsok Kim

    (Department of Management Information Systems, KeiMyung University, 1095 Dalgubeol-daero, Dalseo-Gu, Daegu 42061, Korea)

Abstract

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

Suggested Citation

  • Hyunwoo Hwangbo & Yangsok Kim, 2019. "Session-Based Recommender System for Sustainable Digital Marketing," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3336-:d:240384
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    References listed on IDEAS

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    1. Xiao Fu & Guanghua Han, 2017. "Trust-Embedded Information Sharing among One Agent and Two Retailers in an Order Recommendation System," Sustainability, MDPI, vol. 9(5), pages 1-18, April.
    2. Geng Sun & Tingru Cui & Ghassan Beydoun & Shiping Chen & Fang Dong & Dongming Xu & Jun Shen, 2017. "Towards Massive Data and Sparse Data in Adaptive Micro Open Educational Resource Recommendation: A Study on Semantic Knowledge Base Construction and Cold Start Problem," Sustainability, MDPI, vol. 9(6), pages 1-21, May.
    3. Yan Guo & Chengxin Yin & Mingfu Li & Xiaoting Ren & Ping Liu, 2018. "Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business," Sustainability, MDPI, vol. 10(1), pages 1-13, January.
    4. Francisco Diez-Martin & Alicia Blanco-Gonzalez & Camilo Prado-Roman, 2019. "Research Challenges in Digital Marketing: Sustainability," Sustainability, MDPI, vol. 11(10), pages 1-13, May.
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    Cited by:

    1. B. R. Sreenivasa & C. R. Nirmala, 2023. "Hybrid time centric recommendation model for e-commerce applications using behavioral traits of user," Information Technology and Management, Springer, vol. 24(2), pages 133-146, June.
    2. Jose Ramon Saura & Pedro Palos-Sanchez & Beatriz Rodríguez Herráez, 2020. "Digital Marketing for Sustainable Growth: Business Models and Online Campaigns Using Sustainable Strategies," Sustainability, MDPI, vol. 12(3), pages 1-5, January.
    3. Sheen Low & Fahim Ullah & Sara Shirowzhan & Samad M. E. Sepasgozar & Chyi Lin Lee, 2020. "Smart Digital Marketing Capabilities for Sustainable Property Development: A Case of Malaysia," Sustainability, MDPI, vol. 12(13), pages 1-40, July.

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