Semi-supervised Adversarial Learning for Complementary Item
Recommendation
- URL: http://arxiv.org/abs/2303.05812v1
- Date: Fri, 10 Mar 2023 09:39:18 GMT
- Title: Semi-supervised Adversarial Learning for Complementary Item
Recommendation
- Authors: Koby Bibas, Oren Sar Shalom, Dietmar Jannach
- Abstract summary: In certain online marketplaces, e.g., on online auction sites, constantly new items are added to the catalog.
We propose a novel approach that can leverage both item side-information and labeled complementary item pairs.
Experiments on three e-commerce datasets show that our method is highly effective.
- Score: 5.5174379874002435
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Complementary item recommendations are a ubiquitous feature of modern
e-commerce sites. Such recommendations are highly effective when they are based
on collaborative signals like co-purchase statistics. In certain online
marketplaces, however, e.g., on online auction sites, constantly new items are
added to the catalog. In such cases, complementary item recommendations are
often based on item side-information due to a lack of interaction data. In this
work, we propose a novel approach that can leverage both item side-information
and labeled complementary item pairs to generate effective complementary
recommendations for cold items, i.e., for items for which no co-purchase
statistics yet exist. Given that complementary items typically have to be of a
different category than the seed item, we technically maintain a latent space
for each item category. Simultaneously, we learn to project distributed item
representations into these category spaces to determine suitable
recommendations. The main learning process in our architecture utilizes labeled
pairs of complementary items. In addition, we adopt ideas from Cycle Generative
Adversarial Networks (CycleGAN) to leverage available item information even in
case no labeled data exists for a given item and category. Experiments on three
e-commerce datasets show that our method is highly effective.
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