Learning to identify similar products in the e-commerce domain has widespread applications such as ensuring consistent grouping of the products in the catalog, avoiding duplicates in the search results, etc. Here, we address the problem of learning product similarity for highly challenging real-world data from the Amazon catalog. We define it as a metric learning problem, where similar products are projected close to each other and dissimilar ones are projected further apart. To this end, we propose a scalable end-to-end multimodal framework for product representation learning in a weakly supervised setting using raw data from the catalog. This includes product images as well as textual attributes like product title and category information. The model uses the image as the primary source of information, while the title helps the model focus on relevant regions in the image by ignoring the background clutter. To validate our approach, we created multimodal datasets covering three broad product categories, where we achieve up to 10% improvement in precision compared to state-of-the-art multimodal benchmark. Along with this, we also incorporate several effective heuristics for training data generation, which further complements the overall training. Additionally, we demonstrate that incorporating the product title makes the model scale effectively across multiple product categories.
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