Multimodal Deep Learning and Fast Retrieval for Recommendation

Daniele Ciarlo, Luigi Portinale

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We 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.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 26th International Symposium, ISMIS 2022, Proceedings
EditorsMichelangelo Ceci, Sergio Flesca, Elio Masciari, Giuseppe Manco, Zbigniew W. Raś, Zbigniew W. Raś
PublisherSpringer Science and Business Media Deutschland GmbH
Pages52-60
Number of pages9
ISBN (Print)9783031165634
DOIs
Publication statusPublished - 2022
Event26th International Symposium on Methodologies for Intelligent Systems, ISMIS 2022 - Rende, Italy
Duration: 3 Oct 20225 Oct 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13515 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Symposium on Methodologies for Intelligent Systems, ISMIS 2022
Country/TerritoryItaly
CityRende
Period3/10/225/10/22

Keywords

  • Locality sensitive hashing
  • Multimodal embeddings
  • Recommender systems

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