Recommendation By Generation: Generation Augmented Complementary Fashion Item Retrieval Using Incomplete Outfit

Gaurab Bhattacharya, Vivek B S, P. Rajith Bhargav, Jayavardhana Gubbi, Bagyalakshmi V, Arpan Pal; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 2358-2367

Complementary item retrieval is an important problem in e-commerce platforms that gives compatible suggestions based on pre-selected items. The user preferences are subjective and there is no ground truth label for this. Hence, this is an ill-posed problem where the definition of compatibility is specific to context. However, existing works consider datasets labelled by one-or-more annotators; hence these models are biased towards annotators preference. To address this problem, we aim to generate compatible images of target category and then perform retrieval based on user's preference without using the ground-truth positive and negative images to avoid annotator's preference. To this end, we propose a novel fashion item generation network conditioned on target category and outfit compatibility. The proposed solution enforces the generator to create the compatible latent representation of the target item. To alleviate the conditioning of target categor and outfit compatibility, we have incorporated classifier guidance and compatibility prediction module with generator. The proposed solution does not require positive and negative target image annotations and hence can be free from annotator's preference, while maintaining its variation. Use of our proposed method on two subsets of large Polyvore dataset demonstrates high image quality, variability and compatibility.