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Found 7 papers, 0 papers with code
Large Language Models for User Interest Journeys
We argue, and demonstrate through extensive experiments, that LLMs as foundation models can reason through user activities, and describe their interests in nuanced and interesting ways, similar to how a human would.
Reward Shaping for User Satisfaction in a REINFORCE Recommender
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction?
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become an emerging topic in recommender systems.
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities
We then build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions.
Adversarial Recommendation: Attack of the Learned Fake Users
We propose a framework for generating fake user profiles which, when incorporated in the training of a recommendation system, can achieve an adversarial intent, while remaining indistinguishable from real user profiles.
Glass-Box Program Synthesis: A Machine Learning Approach
Our results show that (i) performing 4 rounds of our framework typically solves about 70% of the target problems, (ii) our framework can improve itself even in domain agnostic scenarios, and (iii) it can solve problems that would be otherwise too slow to solve with brute-force search.
Recommendation under Capacity Constraints
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 (POI) or size of an item's inventory.
