A Deep Learning Framework to Predict Rating for Cold Start Item Using Item Metadata

Anwar, Fahad; Iltaf, Naima; Afzal, Hammad; Abbas, Haider

doi:10.1109/wetice.2019.00071

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Five evaluation criteria have been calculated to evaluate the proposed recommendation system: Root mean square error (RMSE), mean absolute error (MAE), precision @N, recall @N and accuracy. a) Root mean square error (RMSE) is calculated as [42], where Rui is the predicted rating of an item for a user, R ꞈ ui is the actual rating and N is the total number of ratings in the item set. b) Mean absolute error (MAE) is used to calculate the error between values of predictions and target values as [43],…”

Section: Evaluation Metricsmentioning

confidence: 99%

An enhanced framework for solving cold start problem in movie recommendation systems

Elzeheiry

Mekky²,

Atwan³

et al. 2021

IJEECS

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<p>Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining principal component analysis with logistic regression to predict the probability of genres of the movies. Finally, combining the most relevant tags based on similarity score with probability and find top N movies with high scores to the user. The proposed model is assessed using the root mean square error (RMSE), the mean absolute error (MAE), recall@N and precision@N and it is applied to 1M, 10M and 20M MovieLens datasets, resulting in an accuracy of 0.8806, 0.8791 and 0.8739.</p>

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Section: Evaluation Metricsmentioning

confidence: 99%