This paper presents the submission of the team MASlab-ZNU to the MMRecSys movie recommendation task, as part of MediaEval 2019. The task involved predicting average movie ratings, standard deviation of ratings, and the number of ratings by using audio and visual features extracted from trailers and the associated metadata. In the proposed work, we model the rating prediction problem as a regression problem and employ different learning models for the prediction task, including ridge regression (RR), support vector regression (SVR), shallow neural network (SNN) and deep neural network (DNN). The results of fairly large amount of experiments on various models and features indicate that combination of DNN+tag features produce the best results for prediction of avgRating and StdRating while for numRating (popularity) it is the combination of RR+tag that significantly outperforms the other competitors, with a large margin.

A regression approach to movie rating prediction using multimedia content and metadata

Deldjoo Y.;Schedl M.
2019

Abstract

This paper presents the submission of the team MASlab-ZNU to the MMRecSys movie recommendation task, as part of MediaEval 2019. The task involved predicting average movie ratings, standard deviation of ratings, and the number of ratings by using audio and visual features extracted from trailers and the associated metadata. In the proposed work, we model the rating prediction problem as a regression problem and employ different learning models for the prediction task, including ridge regression (RR), support vector regression (SVR), shallow neural network (SNN) and deep neural network (DNN). The results of fairly large amount of experiments on various models and features indicate that combination of DNN+tag features produce the best results for prediction of avgRating and StdRating while for numRating (popularity) it is the combination of RR+tag that significantly outperforms the other competitors, with a large margin.
2019 Working Notes of the MediaEval Workshop, MediaEval 2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/243905
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