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Found 17 papers, 3 papers with code
Recommendation with Generative Models
Generative models are a class of AI models capable of creating new instances of data by learning and sampling from their statistical distributions.
Multi-modal Generative Models in Recommendation System
Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance.
Large Language Model Driven Recommendation
While previous chapters focused on recommendation systems (RSs) based on standardized, non-verbal user feedback such as purchases, views, and clicks -- the advent of LLMs has unlocked the use of natural language (NL) interactions for recommendation.
A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)
Traditional recommender systems (RS) typically use user-item rating histories as their main data source.
On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection
However, obtaining a large amount of training annotations specific to a particular task, i. e., fine-grained annotations, is costly in practice.
A Review of Modern Fashion Recommender Systems
The textile and apparel industries have grown tremendously over the last few years.
Main Product Detection with Graph Networks for Fashion
Computer vision has established a foothold in the online fashion retail industry.
T-VSE: Transformer-Based Visual Semantic Embedding
Transformer models have recently achieved impressive performance on NLP tasks, owing to new algorithms for self-supervised pre-training on very large text corpora.
Orderless Recurrent Models for Multi-label Classification
Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification.
Learning Metrics from Teachers: Compact Networks for Image Embedding
In this paper, we propose to use network distillation to efficiently compute image embeddings with small networks.
Visually-Aware Personalized Recommendation using Interpretable Image Representations
Visually-aware recommender systems use visual signals present in the underlying data to model the visual characteristics of items and users' preferences towards them.
The BreakingNews Dataset
We present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (e. g. GPS coordinates and popularity metrics).
BreakingNews: Article Annotation by Image and Text Processing
Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation.
Structured Prediction with Output Embeddings for Semantic Image Annotation
We address the task of annotating images with semantic tuples.
