Description

Book Synopsis

.- Recommender System.
.- Hierarchical Review-based Recommendation with Contrastive Collaboration.
.- Adaptive Augmentation and Neighbor Contrastive Learning for Multi-Behavior Recommendation.
.- Automated Modeling of Influence Diversity with Graph Convolutional Network for Social Recommendation.
.- Contrastive Generator Generative Adversarial Networks for Sequential Recommendation.
.- Distribution-aware Diversification for Personalized Re-ranking in Recommendation.
.- KMIC: A Knowledge-aware Recommendation with Multivariate Intentions Contrastive Learning.
.- Logic Preference Fusion Reasoning on Recommendation.
.- MHGNN: Hybrid Graph Neural Network with Mixers for Multi-interest Session-aware Recommendation.
.- Mixed Augmentation Contrastive Learning for Graph Recommendation System.
.- Noise-Resistant Graph Neural Networks for Session-based Recommendation.
.- S2DNMF: A Self-supervised Deep Nonnegative Matrix Factorization Recommenda

Web and Big Data

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Order before 4pm today for delivery by Thu 22 Jan 2026.

A Paperback by Wenjie Zhang

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    View other formats and editions of Web and Big Data by Wenjie Zhang

    Publisher: Springer
    Publication Date: 8/28/2024
    ISBN13: 9789819772346, 978-9819772346
    ISBN10: 9819772346
    Also in:
    Databases / Data management

    Description

    Book Synopsis

    .- Recommender System.
    .- Hierarchical Review-based Recommendation with Contrastive Collaboration.
    .- Adaptive Augmentation and Neighbor Contrastive Learning for Multi-Behavior Recommendation.
    .- Automated Modeling of Influence Diversity with Graph Convolutional Network for Social Recommendation.
    .- Contrastive Generator Generative Adversarial Networks for Sequential Recommendation.
    .- Distribution-aware Diversification for Personalized Re-ranking in Recommendation.
    .- KMIC: A Knowledge-aware Recommendation with Multivariate Intentions Contrastive Learning.
    .- Logic Preference Fusion Reasoning on Recommendation.
    .- MHGNN: Hybrid Graph Neural Network with Mixers for Multi-interest Session-aware Recommendation.
    .- Mixed Augmentation Contrastive Learning for Graph Recommendation System.
    .- Noise-Resistant Graph Neural Networks for Session-based Recommendation.
    .- S2DNMF: A Self-supervised Deep Nonnegative Matrix Factorization Recommenda

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