6 citations · 21 across the 10 of their papers we have counts for
10 papers
LLM-Based User Personas for Recommendations at Scale
Haoting Wang, Haokai Lu, Zheyun Feng +14
Large Language Models (LLMs) offer unprecedented potential for enhancing recommendation systems through their world knowledge and reasoning capabilities. However, existing approach…
Conversational Planning for Personal Plans
Konstantina Christakopoulou, Iris Qu, John Canny +4
The language generation and reasoning capabilities of large language models (LLMs) have enabled conversational systems with impressive performance in a variety of tasks, from code…
Agents Thinking Fast and Slow: A Talker-Reasoner Architecture
Konstantina Christakopoulou, Shibl Mourad, Maja Matarić
Large language models have enabled agents of all kinds to interact with users through natural conversation. Consequently, agents now have two jobs: conversing and planning/reasonin…
Large Language Models for User Interest Journeys
Konstantina Christakopoulou, Alberto Lalama, Cj Adams +10
Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation. Their potential for deeper user understanding and improved persona…
Reward Shaping for User Satisfaction in a REINFORCE Recommender
Konstantina Christakopoulou, Can Xu, Sai Zhang +10
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction? Three research questions are key…
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
Xin Xin, Tiago Pimentel, Alexandros Karatzoglou +3
Modern recommender systems aim to improve user experience. As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become…
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
Ruohan Zhan, Konstantina Christakopoulou, Ya Le +6
Most existing recommender systems focus primarily on matching users to content which maximizes user satisfaction on the platform. It is increasingly obvious, however, that content…
Adversarial Recommendation: Attack of the Learned Fake Users
Konstantina Christakopoulou, Arindam Banerjee
Can machine learning models for recommendation be easily fooled? While the question has been answered for hand-engineered fake user profiles, it has not been explored for machine l…
Glass-Box Program Synthesis: A Machine Learning Approach
Konstantina Christakopoulou, Adam Tauman Kalai
Recently proposed models which learn to write computer programs from data use either input/output examples or rich execution traces. Instead, we argue that a novel alternative is t…
Recommendation under Capacity Constraints
Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee
In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest…