PaperMetrixEvidence-aware discovery
EN
Interface language English EN العربية AR Türkçe TR
Create account
Researcher profile

Guibing Guo

5 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Pairwise Preference Over Mixed-Type Item-Sets Based Bayesian Personalized Ranking for Collaborative Filtering

    2017

    Nowadays, providing high quality recommendation services to users is an essential component in online Web applications, including shopping, making friends, healthcare, etc. In some recent works, the recommendation problem of one-class collaborative filtering has been …

  2. Efficient and Adaptive Recommendation Unlearning: A Guided Filtering Framework to Erase Outdated Preferences

    2024 · ACM Transactions on Information Systems

    Recommendation unlearning is an emerging task to erase the influences of user-specified data from a trained recommendation model. Most existing research follows the paradigm of partitioning the original dataset into multi-fold and then retraining corresponding …

  3. Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging

    2025 · IEEE Transactions on Pattern Analysis and Machine Intelligence

    Multi-task learning (MTL) leverages a shared model to accomplish multiple tasks and facilitate knowledge transfer. Recent research on task arithmetic-based MTL demonstrates that merging the parameters of independently fine-tuned models can effectively achieve MTL. However, …

  4. TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings

    2015 · Proceedings of the AAAI Conference on Artificial Intelligence

    Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust …

  5. A Novel Recommendation Model Regularized with User Trust and Item Ratings

    2016 · IEEE Transactions on Knowledge and Data Engineering

    We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation …