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Unveiling consumer preferences: a theory-based multimodal deep learning architecture for explainable recommendation

Xinyu Liu et al.

International Journal of Research in Marketing2026https://doi.org/10.1016/j.ijresmar.2026.01.007article
AJG 4ABDC A*
Weight
0.50

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https://doi.org/https://doi.org/10.1016/j.ijresmar.2026.01.007

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@article{xinyu2026,
  title        = {{Unveiling consumer preferences: a theory-based multimodal deep learning architecture for explainable recommendation}},
  author       = {Xinyu Liu et al.},
  journal      = {International Journal of Research in Marketing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ijresmar.2026.01.007},
}

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Unveiling consumer preferences: a theory-based multimodal deep learning architecture for explainable recommendation

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Evidence weight

0.50

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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.