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Found 11 papers, 5 papers with code
Graph-based Aspect Representation Learning for Entity Resolution
Entity Resolution (ER) identifies records that refer to the same real-world entity.
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation
Therefore, the data sparsity issue of reward signals and state transitions is very severe, while it has long been overlooked by existing RL recommenders. Worse still, RL methods learn through the trial-and-error mode, but negative feedback cannot be obtained in implicit feedback recommendation tasks, which aggravates the overestimation problem of offline RL recommender.
LLaMA-E: Empowering E-commerce Authoring with Multi-Aspect Instruction Following
E-commerce authoring involves creating attractive, abundant, and targeted promotional content to drive product sales.
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
However, such a manner inevitably learns unstable feature interactions, i. e., the ones that exhibit strong correlations in historical data but generalize poorly for future serving.
Fairness-aware Differentially Private Collaborative Filtering
Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks.
Transferable Fairness for Cold-Start Recommendation
The proposed method is able to transfer the knowledge of a fair model learned from the source users to the target users with the hope of improving the recommendation performance and keeping the fairness property on the target users.
Causal Inference for Knowledge Graph based Recommendation
Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model.
Mitigating Hidden Confounding Effects for Causal Recommendation
Towards this goal, we propose a Hidden Confounder Removal (HCR) framework that leverages front-door adjustment to decompose the causal effect into two partial effects, according to the mediators between item features and user feedback.
Learning Intents behind Interactions with Knowledge Graph for Recommendation
In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN).
Explainable Reasoning over Knowledge Graphs for Recommendation
Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest.
BiRank: Towards Ranking on Bipartite Graphs
In this paper, we study the problem of ranking vertices of a bipartite graph, based on the graph's link structure as well as prior information about vertices (which we term a query vector).
