5 citations · 9 across the 3 of their papers we have counts for
6 papers · 1 filter
Sliding Window Training -- Utilizing Historical Recommender Systems Data for Foundation Models
Swanand Joshi, Yesu Feng, Ko-Jen Hsiao +2
Long-lived recommender systems (RecSys) often encounter lengthy user-item interaction histories that span many years. To effectively learn long term user preferences, Large RecSys…
Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)
Moumita Bhattacharya, Vito Ostuni, Sudarshan Lamkhede
Search and recommendation systems are essential in many services, and they are often developed separately, leading to complex maintenance and technical debt. In this paper, we pres…
IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning
Sejoon Oh, Moumita Bhattacharya, Yesu Feng +1
Recommender systems have played a critical role in diverse digital services such as e-commerce, streaming media, social networks, etc. If we know what a user's intent is in a given…
Synergistic Signals: Exploiting Co-Engagement and Semantic Links via Graph Neural Networks
Zijie Huang, Baolin Li, Hafez Asgharzadeh +5
Given a set of candidate entities (e.g. movie titles), the ability to identify similar entities is a core capability of many recommender systems. Most often this is achieved by col…
Recommendations and Results Organization in Netflix Search
Sudarshan Lamkhede, Christoph Kofler
Personalized recommendations on the Netflix Homepage are based on a user's viewing habits and the behavior of similar users. These recommendations, organized for efficient browsing…
Challenges in Search on Streaming Services: Netflix Case Study
Sudarshan Lamkhede, Sudeep Das
We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search…