Implementing smart reply suggestions in chat applications
Tutor / Supervisor
Fontanals Martinez, Ivan
Student
Herlyn, Jan
Document type
Master thesis
Date
2025
rights
Restricted access - confidentiality agreement
Publisher
Universitat Politècnica de Catalunya
UPCommons
Abstract
This thesis investigates the optimization of user communication patterns on Leboncoin, France's leading second-hand marketplace platform, through the implementation of context-sensitive message suggestions. The research addresses a critical business challenge: approximately 23% of conversations on the platform receive no initial response, a phenomenon known as “ghosting” that significantly impacts user experience and platform revenue. While previous experiments on the Italian sister company Subito, demonstrated that fixed message suggestions could improve reply rates, this research explores showing context-sensitive full text suggestions. At the core, these suggestions are based on classifying the latest message received and then providing a set of potential, manually created replies depending on the class. Since a wide range of product categories are sold on our market places, we have a particular interest in automating the discovery of new and existing classes through unsupervised clustering. This master's thesis describes our attempts to cluster the messages based on different types of embeddings and compare the results obtained to the classic text representations TF-IDF (term frequency - inverse document frequency) and BOW (bag of word). Additionally, we attempt to further improve the clusterings through outlier detection, normalizing and applying PCA (principle component analysis) to the embeddings and combining multiple “views” of text representations in order to improve results with mixed success.
Entitat col·laboradora
Adevinta
Participating teacher
- Fontanals Martinez, Ivan