RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

Zhao, Wayne Xin; Mu, Shanlei; Hou, Yupeng; Lin, Zihan; Li, Kaiyuan; Chen, Yushuo; Lu, Yujie; Wang, Hui; Tian, Changxin; Pan, Xingyu; Min, Yingqian; Feng, Zhichao; Fan, Xinyan; Chen, Xu; Wang, Pengfei; Ji, Wendi; Li, Yaliang; Wang, Xiaoling; Wen, Ji-Rong

doi:10.48550/arxiv.2011.01731

Cited by 12 publications

(5 citation statements)

References 77 publications

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“…We utilize the RecBole [29] open-source framework to develop our model, as well as all baseline algorithms. For a balanced comparison, we employ the Adam optimizer across all methods and meticulously fine-tune the hyperparameters for each baseline.…”

Section: Implementation Detailsmentioning

confidence: 99%

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Cao,

Chen,

Hao

et al. 2023

Mathematics

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Graph Collaborative Filtering (GCF) methods have emerged as an effective recommendation approach, capturing users’ preferences over items by modeling user–item interaction graphs. However, these methods suffer from data sparsity in real scenarios, and their performance can be improved using contrastive learning. In this paper, we propose an optimized method, named LoRA-NCL, for GCF based on Neighborhood-enriched Contrastive Learning (NCL) and low-rank dimensionality reduction. We incorporate low-rank features obtained through matrix factorization into the NCL framework and employ LightGCN to extract high-dimensional representations. Extensive experiments on five public datasets demonstrate that the proposed method outperforms a competitive graph collaborative filtering base model, achieving 4.6% performance gains on the MovieLens dataset, respectively.

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Section: Implementation Detailsmentioning

confidence: 99%

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Cao,

Chen,

Hao

et al. 2023

Mathematics

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“…Although comprehensive evaluations on conventional recommendation models or graph embedding-based recommendation models did by existed research or published program libraries [400], [401], there still lacks sufficient comparisons between both of them under a unified experimental framework, providing little prospect on analyzing and utilizing their respective strengths to complement each other. For that gap, this section designs six recommendation tasks for predicting both implicit and explicit user-item interactions, based on which several graph embedding-based and conventional recommendation models will be evaluated on five common-used metrics.…”

Section: A Experiments Setupsmentioning

confidence: 99%

Recommender Systems Based on Graph Embedding Techniques: A Review

Deng

2022

IEEE Access

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As a pivotal tool to alleviate the information overload problem, recommender systems aim to predict user's preferred items from millions of candidates by analyzing observed user-item relations. As for alleviating the sparsity and cold start problems encountered by recommender systems, researchers generally resort to employing side information or knowledge in recommendation as a strategy for uncovering hidden (indirect) user-item relations, aiming to enrich observed information (or data) for recommendation. However, in the face of the high complexity and large scale of side information and knowledge, this strategy largely relies for efficient implementation on the scalability of recommendation models. Not until after the prevalence of machine learning did graph embedding techniques be a recent concentration, which can efficiently utilize complex and large-scale data. In light of that, equipping recommender systems with graph embedding techniques has been widely studied these years, appearing to outperform conventional recommendation implemented directly based on graph topological analysis (or resolution). As the focus, this article systematically retrospects graph embedding-based recommendation from embedding techniques for bipartite graphs, general graphs and knowledge graphs, and proposes a general design pipeline of that. In addition, after comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models on simulations, this article manifests that the conventional models can still overall outperform the graph embedding-based ones in predicting implicit user-item interactions, revealing the comparative weakness of graph embedding-based recommendation in these tasks. To foster future research, this article proposes constructive suggestions on making a trade-off between graph embedding-based recommendation and conventional recommendation in different tasks, and puts forward some open questions.

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“…Meanwhile, according to the investigation from [37], experiments of some other models are carried out with the different processes, which brings difficulties to repeating them. Hence, all the experiments in this paper are all under the uniformed experimental framework Recbole [38] to make fair comparisons.…”

Section: Setupmentioning

confidence: 99%

LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation

Zhang¹,

Xu²,

Wu³

et al. 2022

Preprint

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Tag-aware recommendation is a task of predicting a personalized list of items for a user by their tagging behaviors. It is crucial for many applications with tagging capabilities like last.fm or movielens. Recently, many efforts have been devoted to improving Tag-aware recommendation systems (TRS) with Graph Convolutional Networks (GCN), which has become new state-of-the-art for the general recommendation. However, some solutions are directly inherited from GCN without justifications, which is difficult to alleviate the sparsity, ambiguity, and redundancy issues introduced by tags, thus adding to difficulties of training and degrading recommendation performance.In this work, we aim to simplify the design of GCN to make it more concise for TRS. We propose a novel tag-aware recommendation model named Light Folksonomy Graph Collaborative Filtering (LFGCF), which only includes the essential GCN components. Specifically, LFGCF first constructs Folksonomy Graphs from the records of user assigning tags and item getting tagged. Then we leverage the simple design of aggregation to learn the high-order representations on Folksonomy Graphs and use the weighted sum of the embeddings learned at several layers for information updating. We share tags embeddings to bridge the information gap between users and items. Besides, a regularization function named TransRT is proposed to better depict user preferences and item features. Extensive hyperparameters experiments and ablation studies on three real-world datasets show that LFGCF uses fewer parameters and significantly outperforms most baselines for the tag-aware top-N recommendations.

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RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

Cited by 12 publications

References 77 publications

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

LoRA-NCL: Neighborhood-Enriched Contrastive Learning with Low-Rank Dimensionality Reduction for Graph Collaborative Filtering

Recommender Systems Based on Graph Embedding Techniques: A Review

LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation

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