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Found 27 papers, 3 papers with code
Large Language Models as Data Augmenters for Cold-Start Item Recommendation
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems.
Correction with Backtracking Reduces Hallucination in Summarization
The results show that CoBa is effective and efficient in reducing hallucination, and offers great adaptability and flexibility.
Online Matching: A Real-time Bandit System for Large-scale Recommendations
In this paper, we introduce Online Matching: a scalable closed-loop bandit system learning from users' direct feedback on items in real time.
Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation
The multi-funnel setup effectively balances between coverage and relevance.
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations
For the aspect-based recommendation component, the extracted aspects are concatenated with the usual user and item features used by the recommendation model.
Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems
Hierarchical structure, on the other hand, exists in a user's novelty-seeking intent, which is manifested as a static and intrinsic user preference for seeking novelty along with a dynamic session-based propensity.
Hierarchical Reinforcement Learning
Recommendation Systems
+1
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.
Long-Term Value of Exploration: Measurements, Findings and Algorithms
We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
Latent User Intent Modeling for Sequential Recommenders
We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.
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?
Learning to Augment for Casual User Recommendation
Users who come to recommendation platforms are heterogeneous in activity levels.
Recency Dropout for Recurrent Recommender Systems
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories.
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.
Batch Reinforcement Learning Through Continuation Method
Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting new interactions.
Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
We investigate batch policy optimization for cost-sensitive classification and contextual bandits---two related tasks that obviate exploration but require generalizing from observed rewards to action selections in unseen contexts.
The Dynamics of Signal Propagation in Gated Recurrent Neural Networks
We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower.
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs
Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data.
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
In this paper, we draw connections between recurrent networks and ordinary differential equations.
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Our approach employs a mixture of models, each with a different temporal range.
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower.
Top-K Off-Policy Correction for a REINFORCE Recommender System
The contributions of the paper are: (1) scaling REINFORCE to a production recommender system with an action space on the orders of millions; (2) applying off-policy correction to address data biases in learning from logged feedback collected from multiple behavior policies; (3) proposing a novel top-K off-policy correction to account for our policy recommending multiple items at a time; (4) showcasing the value of exploration.
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
We develop a theory for signal propagation in recurrent networks after random initialization using a combination of mean field theory and random matrix theory.
MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks
We introduce MinimalRNN, a new recurrent neural network architecture that achieves comparable performance as the popular gated RNNs with a simplified structure.
Efficient Vector Representation for Documents through Corruption
The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks.
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Marginalizing Corrupted Features
In this paper, we propose a third, alternative approach to combat overfitting: we extend the training set with infinitely many artificial training examples that are obtained by corrupting the original training data.
Cost-Sensitive Tree of Classifiers
Recently, machine learning algorithms have successfully entered large-scale real-world industrial applications (e. g. search engines and email spam filters).
Co-Training for Domain Adaptation
Our algorithm is a variant of co-training, and we name it CODA (Co-training for domain adaptation).
