Abstract
E-commerce provides rich multimodal data that is barely leveraged in practice. The majority of e-commerce search mechanisms are uni-modal, which are cumbersome and often fail to grasp the customer’s needs. For the Ph.D. we conduct research aimed at combining information across multiple modalities to improve search and recommendations in e-commerce. The research plans are organized along the two principal lines. First, motivated by the mismatch between a textual and a visual representation of a given product category, we propose the task of category-to-image retrieval, i.e., the problem of retrieval of an image of a category expressed as a textual query. Besides, we propose a model for the task. The model leverages information from multiple modalities to create product representations. We explore how adding information from multiple modalities impacts the model’s performance and compare our approach with state-of-the-art models. Second, we consider fine-grained text-image retrieval in e-commerce. We start off by considering the task in the context of reproducibility. Moreover, we address the problem of attribute granularity in e-commerce. We select two state-of the-art (SOTA) models with distinct architectures, a CNN-RNN model and a Transformer-based model, and consider their performance on various e-commerce categories as well as on object-centric data from general domain. Next, based on the lessons learned from the reproducibility study, we propose the model for the fine-grained text-image retrieval.
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Hendriksen, M. (2022). Multimodal Retrieval in E-Commerce. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_62
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