User preference learning in multi-criteria recommendations using stacked auto encoders

Tallapally, Dharahas; Sreepada, Rama Syamala; Patra, Binoy Krishna; Babu, Korra Sathya

doi:10.1145/3240323.3240412

Cited by 28 publications

(13 citation statements)

References 22 publications

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“…The works by Tallapally et al [65] and Zarzour et al [66] provided an approach to use a deep neural network to predict user ratings, scores, and preferences. The main idea of building the music emotion representation model with a deep neural network is using the music feature vector V music to predict the emotional response it brings about.…”

Section: The Deep Neural Network For the Music Emotion Representation Modelmentioning

confidence: 99%

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Wang

Ding

et al. 2021

Electronics

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Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.

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Section: The Deep Neural Network For the Music Emotion Representation Modelmentioning

confidence: 99%

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

Wang

Ding

et al. 2021

Electronics

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“…Criterion-based predictions are generated by matrix factorization [23], fuzzy Bayesian approach [21], autoencoders [25], multi-layer neural networks [26]. Rest of the researchers try to enhance the accuracy of predictions by integrating more precise aggregation function [27][28][29][30][31]. Support vector regression [28], feedforward neural networks [26,29], autoencoders [30], adaptive genetic algorithm [31], genetic programming [32],…”

Section: Related Workmentioning

confidence: 99%

“…Rest of the researchers try to enhance the accuracy of predictions by integrating more precise aggregation function [27][28][29][30][31]. Support vector regression [28], feedforward neural networks [26,29], autoencoders [30], adaptive genetic algorithm [31], genetic programming [32],…”

Section: Related Workmentioning

confidence: 99%

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

2021

Turk J Elec Eng & Comp Sci

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Recommender systems provide their users an efficient way to handle with information overload problem by offering personalized suggestions. Traditional recommender systems are based on two-dimensional user-item preference matrix which constructed depending on the users' overall evaluations over items. However, they have begun to present their preferences over under various circumstances. Thus, traditional recommendation techniques fail to process multicriteria ratings during the recommendation process. Multi-criteria recommender systems are an extension of traditional recommender systems that utilize multi-criteria-based user preferences. Multi-criteria recommender systems provide more personalized and accurate predictions compared to traditional recommender systems. However, the increased amount of data dimension causes sparsity to be a major problem of such systems. Especially, the similarity-based multi-criteria recommender systems may fail to find similar neighbors to an active user due to the lack of co-rated items among users.Therefore, we propose a new similarity-based multi-criteria collaborative filtering approach based on autoencoders. In order to handle with sparsity, the proposed method extracts non-linear, low-dimensional, dense features from raw and sparse users'/items' preferences. Our experimental outcomes show that the proposed work can amortize the negative impacts of sparsity over the accuracy comparing with the state-of-the-art multi-criteria recommendation techniques.

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“…For example, Zheng [37] proposed to construct the list of aspects as a chain and predict a user's rating on each aspect one by one. Tallapally et al [28] extends the stacked auto-encoders with modified input layer and loss function to enable the learning of multi-aspect ratings. But such solutions cannot directly optimize item ranking resulted from the predicted ratings, and therefore there is no guarantee for their multi-aspect recommendation quality.…”

Section: Related Workmentioning

confidence: 99%

Directional Multivariate Ranking

Wang

2020

Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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User-provided multi-aspect evaluations manifest users' detailed feedback on the recommended items and enable fine-grained understanding of their preferences. Extensive studies have shown that modeling such data greatly improves the effectiveness and explainability of the recommendations. However, as ranking is essential in recommendation, there is no principled solution yet for collectively generating multiple item rankings over different aspects.In this work, we propose a directional multi-aspect ranking criterion to enable a holistic ranking of items with respect to multiple aspects. Specifically, we view multi-aspect evaluation as an integral effort from a user that forms a vector of his/her preferences over aspects. Our key insight is that the direction of the difference vector between two multi-aspect preference vectors reveals the pairwise order of comparison. Hence, it is necessary for a multi-aspect ranking criterion to preserve the observed directions from such pairwise comparisons. We further derive a complete solution for the multiaspect ranking problem based on a probabilistic multivariate tensor factorization model. Comprehensive experimental analysis on a large TripAdvisor multi-aspect rating dataset and a Yelp review text dataset confirms the effectiveness of our solution.

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User preference learning in multi-criteria recommendations using stacked auto encoders

Cited by 28 publications

References 22 publications

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network

A new similarity-based multi-criteria recommendation algorithm based on autoencoders

Directional Multivariate Ranking

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