BOXREC: Recommending a Box of Preferred Outfits in Online Shopping
<|reference_start|>BOXREC: Recommending a Box of Preferred Outfits in Online Shopping: Over the past few years, automation of outfit composition has gained much attention from the research community. Most of the existing outfit recommendation systems focus on pairwise item compatibility prediction (using visual and text features) to score an outfit combination having several items, followed by recommendation of top-n outfits or a capsule wardrobe having a collection of outfits based on user's fashion taste. However, none of these consider user's preference of price-range for individual clothing types or an overall shopping budget for a set of items. In this paper, we propose a box recommendation framework - BOXREC - which at first, collects user preferences across different item types (namely, top-wear, bottom-wear and foot-wear) including price-range of each type and a maximum shopping budget for a particular shopping session. It then generates a set of preferred outfits by retrieving all types of preferred items from the database (according to user specified preferences including price-ranges), creates all possible combinations of three preferred items (belonging to distinct item types) and verifies each combination using an outfit scoring framework - BOXREC-OSF. Finally, it provides a box full of fashion items, such that different combinations of the items maximize the number of outfits suitable for an occasion while satisfying maximum shopping budget. Empirical results show superior performance of BOXREC-OSF over the baseline methods.<|reference_end|>
@article{banerjee2024boxrec:,
    title={BOXREC: Recommending a Box of Preferred Outfits in Online Shopping},
    author={Debopriyo Banerjee, Krothapalli Sreenivasa Rao, Shamik Sural, Niloy
  Ganguly},
    journal={ACM Trans. Intell. Syst. Technol. 11, 6, Article 69 (December
  2020), pages 69:1-69:28},
    year={2024},
    doi={10.1145/3408890},
    archivePrefix={arXiv},
    eprint={2402.16660},
    primaryClass={cs.IR}
}