Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

Chen, Chong; Zhang, Min; Zhang, Yongfeng; Ma, Weigang; Liu, Yiqun; Ma, Shaoping

doi:10.1609/aaai.v34i01.5329

Cited by 137 publications

(92 citation statements)

References 19 publications

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“…Figure 13 shows the other approaches' comparison with the proposed system. As it shows in Figure 13, we compare our results with three related publications [74][75][76]. In the first related study, the proposed approach is using the PMF model for a recommendation system, and the proposed model successfully obtained the accuracy of 0.72 percent for a recommendation.…”

Section: Comparative Analysismentioning

confidence: 85%

Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

et al. 2020

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With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.

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Section: Comparative Analysismentioning

confidence: 85%

Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

et al. 2020

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“…Later on, researchers found that users interact with items mainly through implicit feedback, such as purchases on Ecommerce sites and watches on online video platforms. Then a surge of recommendation methods were proposed for learning from implicit feedback [9,12,30,31,42]. Specifically, Hu et al [31] proposed a non-sampling based method WMF, which assumes that all unobserved items are negative samples.…”

Section: Related Work 21 Item Recommendationmentioning

confidence: 99%

Recommendation Unlearning

Chen¹,

Sun²,

Zhang³

et al. 2022

Preprint

Self Cite

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Recommender systems provide essential web services by learning users' personal preferences from collected data. However, in many cases, systems also need to forget some training data. From the perspective of privacy, users desire a tool to erase the impacts of their sensitive data from the trained models. From the perspective of utility, if a system's utility is damaged by some bad data, the system needs to forget such data to regain utility. While unlearning is very important, it has not been well-considered in existing recommender systems. Although there are some researches have studied the problem of machine unlearning, existing methods can not be directly applied to recommendation as they are unable to consider the collaborative information.In this paper, we propose RecEraser, a general and efficient machine unlearning framework tailored to recommendation tasks. The main idea of RecEraser is to divide the training set into multiple shards and train submodels with these shards. Specifically, to keep the collaborative information of the data, we first design three novel data partition algorithms to divide training data into balanced groups. We then further propose an adaptive aggregation method to improve the global model utility. Experimental results on three public benchmarks show that RecEraser can not only achieve efficient unlearning but also outperform the state-of-the-art unlearning methods in terms of model utility. The source code can be found at https://github.com/chenchongthu/Recommendation-Unlearning CCS CONCEPTS• Information systems → Recommender systems; • Security and privacy → Privacy protections.

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“…As to future work, we would like to further improve the efficiency of EGCF, such as adopting the non-sampling strategies [ 37 , 38 ] for model optimization. Moreover, we are also interested in incorporating the side information such as knowledge graph [ 39 , 40 ] to alleviate the sparsity and cold start problems in collaborative filtering.…”

Section: Conclusion and Future Workmentioning

confidence: 99%

Efficient Graph Collaborative Filtering via Contrastive Learning

Pan

Chen

2021

Sensors

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Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.

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Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

Cited by 137 publications

References 19 publications

Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Recommendation Unlearning

Efficient Graph Collaborative Filtering via Contrastive Learning

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