Item Relevance

Item relevance in recommender systems focuses on accurately predicting items users will find valuable, addressing challenges like noisy user data and biases towards frequently purchased items. Current research emphasizes developing sophisticated models, such as graph neural networks and coupled autoencoders, to capture nuanced user preferences and contextual information, often incorporating techniques from causal inference to mitigate biases. These advancements aim to improve recommendation accuracy and diversity, leading to enhanced user experience and increased engagement for businesses deploying recommender systems.

Papers

October 11, 2024

Personalized Item Representations in Federated Multimodal Recommendation
Zhiwei Li, Guodong Long, Jing Jiang, Chengqi Zhang
Multimodal Data Recommendation System Federated Recommendation Item Embeddings Multimodal Recommendation Item Relevance

October 14, 2023

Context-aware Session-based Recommendation with Graph Neural Networks
Zhihui Zhang, JianXiang Yu, Xiang Li
Graph Neural Network Contextual Information Session Graph Item Relevance Session Context

July 24, 2023

Personalized Category Frequency prediction for Buy It Again recommendations
Amit Pande, Kunal Ghosh, Rankyung Park
Movie Recommendation Hierarchical Model Item Relevance Category Specific Model

May 18, 2023

Integrating Item Relevance in Training Loss for Sequential Recommender Systems
Andrea Bacciu, Federico Siciliano, Nicola Tonellotto, Fabrizio Silvestri
Recommender System Sequential Recommender System Training Loss Evaluation Protocol Item Relevance Class Relevance Learning

November 16, 2022

Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Xiaohan Li, Zheng Liu, Luyi Ma, Kaushiki Nag, Stephen Guo, Philip Yu, Kannan Achan
Recommendation System Debiased Recommendation Frequency Bias Item Relevance Basket Recommendation

October 7, 2022

Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems
Parikshit Bansal, Yashoteja Prabhu, Emre Kiciman, Amit Sharma
Distribution Generalization Recommendation Task Health Intervention Item Based Collaborative Filtering Item Relevance Text Aware Causal Interventional Regularizer

February 28, 2022

WSLRec: Weakly Supervised Learning for Neural Sequential Recommendation Models
Jingwei Zhuo, Bin Liu, Xiang Li, Han Zhu, Xiaoqiang Zhu
Weakly Supervised Learning Implicit Feedback Item Based Collaborative Filtering Item Relevance

December 8, 2021

A cross-domain recommender system using deep coupled autoencoders
Alexandros Gkillas, Dimitrios Kosmopoulos
Supervised Autoencoder Cross Domain Recommendation Item Relevance