Associated with the Linz Institute of Technology and the Institute of Computational Perception @ JKU Linz, Austria

SwapRec: Warming Up Cold Items Through Training-Time Similar-Content Swaps

SwapRec Teaser

Abstract

Interactions with cold items negatively impact real-time personalization of ID-based recommender systems. This is because the use of such interactions degrades user preference estimates, whereas excluding cold items from the user profile prevents real-time recommendation updates. In industrial scenarios, one heuristic often applied to address this shortcoming at inference time is to replace, i. e., “swap”, cold-start items by their most similar “warm” neighbor, where similarity is inferred from the items’ side information. In this paper, we demonstrate that sequential models, most often used for real-time personalization, are not robust to such swaps, and propose SwapRec , an approach to address this issue. SwapRec relies on using the same swap heuristics already at training time. We apply SwapRec to state-of-the-art models for sequential recommendation and analyze its impact by means of quantitative experiments in three recommendation domains (online shopping, movie, music). The experimental results show that, irrespective of the underlying sequential architecture, our easy-to-implement SwapRec approach allows for substantially more accurate recommendations when in presence of interactions with cold items, simultaneously leading to a larger percentage of cold items in the recommendation lists.


Citation

Marta Moscati, Jan Malte Lichtenberg, Davide Abbattista, Antonio De Candia, Laura Boggia, Matteo Ruffini
SwapRec: Warming Up Cold Items Through Training-Time Similar-Content Swaps
Proceedings of DaQuaMRec 2026, the 2nd International Workshop on Data Quality-Aware Multimodal Recommendation, co-located with the 20th ACM Conference on Recommender Systems (RecSys 2026), September 28 - October 2, 2026, Minneapolis, Minnesota, USA., 2026.

BibTeX

@inproceedings{Moscati2026SwapRec,
    title = {SwapRec: Warming Up Cold Items Through Training-Time Similar-Content Swaps},
    author = {Moscati, Marta and Jan Malte	Lichtenberg and Davide	Abbattista and Antonio	De Candia and Laura	Boggia and Matteo Ruffini},
    booktitle = {Proceedings of DaQuaMRec 2026, the 2nd International Workshop on Data Quality-Aware Multimodal Recommendation, co-located with the 20th ACM Conference on Recommender Systems (RecSys 2026), September 28 - October 2, 2026, Minneapolis, Minnesota, USA.},
    year = {2026}
}

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