Experimental interpretation of adequate weight-metric combination for dynamic user-based collaborative filtering

Okyay, Savas; Aygün, Sercan

doi:10.7717/peerj-cs.784

Cited by 4 publications

(2 citation statements)

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“…Hence, any anomalies detected in these dataset can be considered natural noise. The statistics of the Movielens dataset are summarized in Table 2 [39]. All ratings in the datasets take the form of integer values ranging from 1 to 5.…”

Section: B Recommendation After Removing Anomaliesmentioning

confidence: 99%

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Autoencoder-Based Recommender System Exploiting Natural Noise Removal

Park

Jeong

et al. 2023

IEEE Access

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Collaborative filtering (CF) is a widely used technique in recommender systems by automatically predicting the user's latent interests based on many users' historical rating data. To improve the performance of the CF-based recommender systems, users' rating data should be pre-processed to avoid noise and enhance data reliability. Many researchers studied anomaly detection to remove malicious noise caused by shilling attacks, but anomalies can still exist in non-attacked real user data, which is called natural noise, as the ratings of users can be impacted by unpredictable factors such as other users' ratings and anchoring bias. In this paper, we propose an autoencoder-based recommendation system for exploiting the ability of both anomaly detection and CF. The proposed system detects the natural noise in the rating data based on the reconstruction errors after training. By removing the detected natural noise, CF can predict the unrated ratings with noise-free data. Our experiments show that the proposed model showed better performance than the traditional method by reducing the error by up to 5% compared to the method that does not consider natural noise detection and reducing the error by up to 4% compared to the conventional rating classification based natural noise detection methods.

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Section: B Recommendation After Removing Anomaliesmentioning

confidence: 99%

“…where TP γ , FP γ , and FN γ are the sets of the true positive ratings, false positive ratings, and false negative ratings, respectively that satisfy ε < γ in the test set [39]. We set the ratings greater than 3.5 are positive, which was typically adopted in other studies [43], [44].…”

Section: ) Baseline Modelmentioning

confidence: 99%