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Found 7 papers, 0 papers with code
Towards An Efficient LLM Training Paradigm for CTR Prediction
Despite such promising results, the computational inefficiency inherent in the current training paradigm makes it particularly challenging to train LLMs for ranking-based recommendation tasks on large datasets.
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System
In industrial recommendation systems, multi-task learning (learning multiple tasks simultaneously on a single model) is a predominant approach to save training/serving resources and improve recommendation performance via knowledge transfer between the joint learning tasks.
Graph-based Extractive Explainer for Recommendations
Explanations in a recommender system assist users in making informed decisions among a set of recommended items.
Comparative Explanations of Recommendations
As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i. e., comparative explanations about the recommended items.
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning
Combinatorial optimization problem (COP) over graphs is a fundamental challenge in optimization.
Learning to Structure Long-term Dependence for Sequential Recommendation
To better model the long-term dependence structure, we propose a GatedLongRec solution in this work.
Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation
Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems.
