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Proceedings of the 22nd International Database Engineering & Applications Symposium, IDEAS 2018, Villa San Giovanni, Italy, June 18-20, 2018
No full text
Multimodal Deep Learning and Fast Retrieval for Recommendation
No full textWe propose a retrieval architecture in the context of
recommender systems for e-commerce applications, based on a multi-modal representation of the items
of interest (textual description and images of the products), paired with a
locality-sensitive hashing (LSH) indexing scheme for the fast retrieval of the
potential recommendations.
In particular, we learn a latent multimodal representation
of the items through
the use of CLIP architecture, combining text and images
in a contrastive way. The item embeddings thus generated
are then searched by means of different types of LSH.
We report on the experiments we performed on two real-world datasets from e-commerce sites, containing both images and textual descriptions of the products
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