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Evaluating the Effectiveness of Recommendation Engines on Customer Experience Across Product Categories

Katsunobu Sasanuma & Gyung Yeol Yang

International Journal of Technology and Human Interaction2024https://doi.org/10.4018/ijthi.345928article
ABDC C
Weight
0.60

What the paper says

Artificial intelligence (AI)-powered tools such as recommendation engines are widely used in online marketing and e-commerce; however, online retailers often deploy these tools without understanding which human factors play a role in which products and at which stage of the customer journey. Understanding the interaction between AI-powered tools and humans can help practitioners create more effective online marketing platforms and improve human interaction with e-commerce tools. This paper examines customers' reliance on recommendation engines when purchasing fashion goods, electronics, and media content such as video and music. This paper also discusses the potential for improvement in recommendation engines in online marketing and e-commerce.

4 citations

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Cite this paper

https://doi.org/https://doi.org/10.4018/ijthi.345928

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@article{katsunobu2024,
  title        = {{Evaluating the Effectiveness of Recommendation Engines on Customer Experience Across Product Categories}},
  author       = {Katsunobu Sasanuma & Gyung Yeol Yang},
  journal      = {International Journal of Technology and Human Interaction},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.4018/ijthi.345928},
}

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Evaluating the Effectiveness of Recommendation Engines on Customer Experience Across Product Categories

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Evidence weight

0.60

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.72 × 0.4 = 0.29
M · momentum0.60 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.