2 citations · 5 across the 5 of their papers we have counts for
6 papers
DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models
Yotam Eshel, Guy Hadad, Guy Feigenblat +3
We explore predicting eCommerce user preferences for product aspects such as brand, size, and color - a task we define as Aspect Affinity. Solving this task improves customer under…
X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation
Guy Hadad, Haggai Roitman, Yotam Eshel +2
As new products are emerging daily, recommendation systems are required to quickly adapt to possible new domains without needing extensive retraining. This work presents ``X-Cross'…
Learning to Diversify for Product Question Generation
Haggai Roitman, Uriel Singer, Yotam Eshel +2
We address the product question generation task. For a given product description, our goal is to generate questions that reflect potential user information needs that are either mi…
Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce
Uriel Singer, Haggai Roitman, Yotam Eshel +5
In e-commerce, the watchlist enables users to track items over time and has emerged as a primary feature, playing an important role in users' shopping journey. Watchlist items typi…
PreSizE: Predicting Size in E-Commerce using Transformers
Yotam Eshel, Or Levi, Haggai Roitman +1
Recent advances in the e-commerce fashion industry have led to an exploration of novel ways to enhance buyer experience via improved personalization. Predicting a proper size for a…
Named Entity Disambiguation for Noisy Text
Yotam Eshel, Noam Cohen, Kira Radinsky +3
We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly n…