# Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation

arXiv: https://arxiv.org/abs/2311.09577 · HTML: https://mlsanity.com/paper/2311.09577/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Multi-Interest Network with Dynamic Routing for Recommendation at Tmall](https://mlsanity.com/paper/1904.08030/) (2019)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://mlsanity.com/paper/2101.06448/) (2021)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation](https://mlsanity.com/paper/2105.02254/) (2021)
- [Sparse-Interest Network for Sequential Recommendation](https://mlsanity.com/paper/2102.09267/) (2021)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation](https://mlsanity.com/paper/2211.10486/) (2022)
- [Temporal Meta-path Guided Explainable Recommendation](https://mlsanity.com/paper/2101.01433/) (2021)
- [Overcoming Data Sparsity in Group Recommendation](https://mlsanity.com/paper/2010.00813/) (2020)
- [Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation](https://mlsanity.com/paper/2107.03813/) (2021)
- [GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation](https://mlsanity.com/paper/2006.03736/) (2020)
- [Hyperbolic Hypergraphs for Sequential Recommendation](https://mlsanity.com/paper/2108.08134/) (2021)
- [Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation](https://mlsanity.com/paper/2208.08011/) (2022)
- [Basket Recommendation with Multi-Intent Translation Graph Neural Network](https://mlsanity.com/paper/2010.11419/) (2020)
- [Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network](https://mlsanity.com/paper/2202.03392/) (2022)
- [Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph](https://mlsanity.com/paper/2011.05061/) (2020)
- [When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation](https://mlsanity.com/paper/2205.01286/) (2022)
- [Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation](https://mlsanity.com/paper/1804.04327/) (2018)
- [Enumerating Fair Packages for Group Recommendations](https://mlsanity.com/paper/2105.14423/) (2021)
- [Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation](https://mlsanity.com/paper/2106.04415/) (2021)
- [Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations](https://mlsanity.com/paper/2110.09905/) (2021)
- [Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network](https://mlsanity.com/paper/2111.14036/) (2021)
- [Graph-based Alignment and Uniformity for Recommendation](https://mlsanity.com/paper/2308.09292/) (2023)
- [Time-aware Hyperbolic Graph Attention Network for Session-based Recommendation](https://mlsanity.com/paper/2301.03780/) (2023)
- [Impression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approach](https://mlsanity.com/paper/2309.03169/) (2023)

## Built on by (most cited first, 1 of 1)

- [Triple Modality Fusion: Aligning Visual, Textual, and Graph Data with Large Language Models for Multi-Behavior Recommendations](https://mlsanity.com/paper/2410.12228/) (2024)

## Abstract (arXiv)

Personalized recommender systems aim to predict users' preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form groups based on their common interests. The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems. Users join different groups out of different interests. In this paper, we generate group representation from the user's interests and propose IGRec (Interest-based Group enhanced Recommendation) to utilize the group information accurately. It consists of four modules. (1) Interest disentangler via self-gating that disentangles users' interests from their initial embedding representation. (2) Interest aggregator that generates the interest-based group representation by Gumbel-Softmax aggregation on the group members' interests. (3) Interest-based group aggregation that fuses user's representation with the participated group representation. (4) A dual-trained rating prediction module to utilize both user-item and group-item interactions. We conduct extensive experiments on three publicly available datasets. Results show IGRec can effectively alleviate the data sparsity problem and enhance the recommender system with interest-based group representation. Experiments on the group recommendation task further show the informativeness of interest-based group representation.