Improving collaborative recommender systems via emotional features

Lazemi, Soghra; Ebrahimpour-Komleh, Hossein

doi:10.1109/icaict.2016.7991703

Cited by 4 publications

(1 citation statement)

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“…For that reason, recommender systems (RSs) have become very necessary for users. RSs exclude unnecessary items to generate a list of recommended items for users based on their historical preferences [2]. Users' preferences can be represented in form of user-item interactions, where the interaction could be rate, buy, like, or click.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Rate Prediction Model for Recommender System Using Clustering Techniques

2023

IJIES

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Many deep learning-based recommender systems have been proposed recently. Where they involve all the users in datasets to build the latent representation of input data to be used later for predicting the missing rates. Despite the fact that, users have different interests, and these differences reduce the model prediction accuracy. This paper proposed a novel cluster-based denoising autoencoder model (cluster-based DAE) for rate prediction recommender systems. Instead of building a single model, it builds K models by using k-means algorithm to divide the users into groups based on their preferences. Each group trains a DAE model to generate recommendations for its members. The proposed method was trained and tested with MovieLens (100K, 1M, and 10M) datasets where 80% of the data are used for training and 20% for testing. The performance of the proposed method compared against other state-of-the-art methods that use deep learning to build rate prediction models. It outperformed the other compared methods in term of mean absolute error (with 12.9%, 14.7%, and 22.3%) and root mean squared error (with 24.2%, 18%, and 21.1%) using MovieLens 100K, 1M, and 10M datasets respectively.

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Section: Introductionmentioning

confidence: 99%