4 research outputs found
Tell Me What Is Good About This Property: Leveraging Reviews For Segment-Personalized Image Collection Summarization
Image collection summarization techniques aim to present a compact
representation of an image gallery through a carefully selected subset of
images that captures its semantic content. When it comes to web content,
however, the ideal selection can vary based on the user's specific intentions
and preferences. This is particularly relevant at Booking.com, where presenting
properties and their visual summaries that align with users' expectations is
crucial. To address this challenge, we consider user intentions in the
summarization of property visuals by analyzing property reviews and extracting
the most significant aspects mentioned by users. By incorporating the insights
from reviews in our visual summaries, we enhance the summaries by presenting
the relevant content to a user. Moreover, we achieve it without the need for
costly annotations. Our experiments, including human perceptual studies,
demonstrate the superiority of our cross-modal approach, which we coin as
CrossSummarizer over the no-personalization and image-based clustering
baselines
Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities
Multi-label text classification is a critical task in the industry. It helps
to extract structured information from large amount of textual data. We propose
Text to Topic (Text2Topic), which achieves high multi-label classification
performance by employing a Bi-Encoder Transformer architecture that utilizes
concatenation, subtraction, and multiplication of embeddings on both text and
topic. Text2Topic also supports zero-shot predictions, produces domain-specific
text embeddings, and enables production-scale batch-inference with high
throughput. The final model achieves accurate and comprehensive results
compared to state-of-the-art baselines, including large language models (LLMs).
In this study, a total of 239 topics are defined, and around 1.6 million
text-topic pairs annotations (in which 200K are positive) are collected on
approximately 120K texts from 3 main data sources on Booking.com. The data is
collected with optimized smart sampling and partial labeling. The final
Text2Topic model is deployed on a real-world stream processing platform, and it
outperforms other models with 92.9% micro mAP, as well as a 75.8% macro mAP
score. We summarize the modeling choices which are extensively tested through
ablation studies, and share detailed in-production decision-making steps
Towards personalized data-driven bundle design with QoS constraint
Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiativ
MuMIC – Multimodal Embedding for Multi-Label Image Classification with Tempered Sigmoid
Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representation learning abilities and is robust to natural distribution shifts. This success inspires us to leverage multimodal learning for multi-label classification tasks, and benefit from contrastively learnt pretrained models.
We propose the Multimodal Multi-label Image Classification (MuMIC) framework, which utilizes a hardness-aware tempered sigmoid based Binary Cross Entropy loss function, thus enables the optimization on multi-label objectives and transfer learning on CLIP. MuMIC is capable of providing high classification performance, handling real-world noisy data, supporting zero-shot predictions, and producing domain-specific image embeddings.
In this study, a total of 120 image classes are defined, and more than 140K positive annotations are collected on approximately 60K Booking.com images. The final MuMIC model is deployed on Booking.com Content Intelligence Platform, and it outperforms other state-of-the-art models with 85.6% GAP@10 and 83.8% GAP on all 120 classes, as well as a 90.1% macro mAP score across 32 majority classes. We summarize the modelling choices which are extensively tested through ablation studies. To the best of our knowledge, we are the first to adapt contrastively learnt multimodal pretraining for real-world multi-label image classification problems, and the innovation can be transferred to other domains