Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Chen, Lei; Wu, Le; Hong, Richang; Zhang, Kun; Wang, Meng

doi:10.1609/aaai.v34i01.5330

Cited by 446 publications

(248 citation statements)

References 17 publications

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“…But different from LightGCN, we retain the inner product operation, whose effectiveness has been verified by experiments in NGCF. Moreover, we use the same layer fusion operation as that in NGCF, which, according to [4], is equivalent to using residual prediction. Specifically, for a connected user-item pair ( , ) in domain A, we define the propagation function of 's and 's features as follows,…”

Section: Feature Propagationmentioning

confidence: 99%

“…In recent years, inspired by the success of graph convolutional networks (GCN) in effectively extracting features in non-Euclidean spaces, some researchers try to exploit the user-item bipartite graph structure by propagating embeddings on it, aiming at achieving more effective embeddings [1,4,14,31]. For example, Wang et al [29] proposed NGCF, which follows the same propagation rules as in GCN (including feature transformation, neighborhood aggregation and nonlinear activation) to capture the high-order connectivity between users and items by stacking multiple feature propagation layers, and achieves promising results.…”

Section: Introductionmentioning

confidence: 99%

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Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Liu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

123

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Data sparsity is a challenge problem that most modern recommender systems are confronted with. By leveraging the knowledge from relevant domains, the cross-domain recommendation technique can be an effective way of alleviating the data sparsity problem. In this paper, we propose a novel Bi-directional Transfer learning method for cross-domain recommendation by using Graph Collaborative Filtering network as the base model (BiTGCF). BiT-GCF not only exploits the high-order connectivity in user-item graph of single domain through a novel feature propagation layer, but also realizes the two-way transfer of knowledge across two domains by using the common user as the bridge. Moreover, distinct from previous cross-domain collaborative filtering methods, BiTGCF fuses users' common features and domain-specific features during transfer. Experimental results on four couple benchmark datasets verify the effectiveness of BiTGCF over state-of-the-art models in terms of bi-directional cross domain recommendation. CCS CONCEPTS • Information systems → Recommender systems; • Computing methodologies → Transfer learning.

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Section: Feature Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Liu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

123

View full text Add to dashboard Cite

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“…For plain text, we can segment words by spaces. As for the TeX formulas, we develop a TeX parsing tool 5 which treats the TeX commands as special words. Then we clean the word sequences, e.g.…”

Section: Exercise Preprocessingmentioning

confidence: 99%

“…where SP and DP are respectively the sets of similar pairs and dissimilar pairs, S • is the similarity score S v or S s , and δ (x) is an indicator function that returns 1 iff x is true. The ranking performance is evaluated with the widely used metric NDCG@K [5,35]:…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Fine-Grained Similarity Measurement between Educational Videos and Exercises

Wang

Huang

Liu

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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In online learning systems, measuring the similarity between educational videos and exercises is a fundamental task with great application potentials. In this paper, we explore to measure the fine-grained similarity by leveraging multimodal information. The problem remains pretty much open due to several domain-specific characteristics. First, unlike general videos, educational videos contain not only graphics but also text and formulas, which have a fixed reading order. Both spatial and temporal information embedded in the frames should be modeled. Second, there are semantic associations between adjacent video segments. The semantic associations will affect the similarity and different exercises usually focus on the related context of different ranges. Third, the fine-grained labeled data for training the model is scarce and costly. To tackle the aforementioned challenges, we propose VENet to measure the similarity at both video-level and segment-level by just exploiting the video-level labeled data. Extensive experimental results on real-world data demonstrate the effectiveness of VENet. CCS CONCEPTS • Information systems → Multimedia and multimodal retrieval; • Computing methodologies → Neural networks; • Social and professional topics → K-12 education.

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Research on the application of bilateral collaborative algorithm in hotel tourism consumption preference recommendation

Wang

2024

Concurrency and Computation

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SummaryThe continuous expansion of the domestic tourism market has driven the development of the hotel related industry. It is a key focus of the hotel industry to provide accurate supporting services to tourists based on their preferences. However, the current recommendation model mainly considers tourist factors and lacks consideration for hotel factors. In order to address the concerns of recommendation services, a bilateral collaborative recommendation model is proposed, which first analyzes tourist feature data through comprehensive similarity calculation. Firstly, the comprehensive similarity calculation is used to analyze tourist feature data, and the K‐means Clustering Algorithm (K‐means) is introduced to improve the clustering effect between data. And to better distinguish tourist preferences, the Gale Shapley (G‐S) bilateral matching model is introduced to achieve preference matching of consumer features. In the Mean Absolute Error (MAE) analysis of the model, the User‐based Collaborative Filtering (UBCF) model performs the worst. When the k value is 90, the minimum MAE value is 0.7896. The Item‐based Collaborative Filtering (IBCF) model is 0.7776, with the best performance being the improved G‐S model of 0.7325. In the performance testing of three models' preference recommendations, the improved G‐S model achieves an accuracy of over 90% in all three types of feature data recommendations, with the lowest accuracy of 62.5% for UBCF and 71.3% for IBCF. It can be seen that the proposed improved G‐S model can accurately identify tourist and hotel preference data and accurately recommend corresponding projects. The research content provides important technical references for the digital transformation and development of the tourism industry.

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Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Cited by 446 publications

References 17 publications

Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Fine-Grained Similarity Measurement between Educational Videos and Exercises

Research on the application of bilateral collaborative algorithm in hotel tourism consumption preference recommendation

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