A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

Sánchez-Moreno, Diego; Batista, Vivian F. López; Vicente, María Dolores Muñoz; González, Ana Belén Gil

doi:10.1155/2020/7309453

Cited by 14 publications

(10 citation statements)

References 41 publications

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“…This means that it can be used and ran well on mobile applications using the voice input feature and internet connection. This result is different from research to provide song recommendations only with collaborative filtering methods [19], and Cosine Similarity Algorithms [20].…”

Section: System Testingcontrasting

confidence: 59%

Song Recommendation Application Using Speech Emotion Recognition

Setiawan

Dzulfiqar²

2021

IJID

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This research was conducted to facilitate the interaction between radio broadcasters and radio listeners during the song request process. This research was triggered by the difficulty of the broadcasters in monitoring song requests from listeners. The system is made to accommodate all song requests by listeners. The application produced in this study uses speech emotion recognition technology based on a person's mood obtained from the spoken words. This technology can change the voice into one of the mood categories: neutral, angry, sad, and afraid. The k-Nearest Neighbor method is used to get recommendations for recommended song titles by looking for the closeness of the value between the listener's mood and the availability of song playlists. kNN is used because this method is suitable for user-based collaborative problems. kNN will recommend three songs which then be offered to listeners by broadcasters. Based on tests conducted to the broadcasters and radio listeners, this study has produced a song request application by recommending song titles according to the listener's mood, the text message, the searching songs, and the song requests and the song details that have been requested. Functional test that has been carried out has received 100 because all test components have succeeded as expected.

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Section: System Testingcontrasting

confidence: 59%

Song Recommendation Application Using Speech Emotion Recognition

Setiawan

Dzulfiqar²

2021

IJID

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“…Each recommendation approach has advantages and limitations; for example, Collaborative Filtering has sparseness, scalability and cold-start problems [ 5 , 23 , 24 ]. A sparseness problem occurs when we have a vast amount of data.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Methods in recommender systems are based on information filtering, and they can be classified into three categories: content-based; collaborative filtering (CF); and hybrid. Sparsity and gray-sheep problems are two of the main reasons CF methods do not provide the reliability required in some recommender systems [ 5 ]. In particular, when only sparse ratings data is available, sentiment analysis can play a key role in improving recommendation quality.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Integrating Sentiment Analysis into Recommender Systems

Dang

García

Prieta

2021

Sensors

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Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

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“…Each recommendation approach has advantages and limitations; for example, Collaborative Filtering has sparseness, scalability and cold-start problems [5,38,39]. A sparseness problem occurs when we have a vast amount of data.…”

Section: Recommender Systemmentioning

confidence: 99%

An Approach to Integrating Sentiment Analysis into Recommender Systems

Dang¹,

Moreno²,

Prieta³

2021

Preprint

Self Cite

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Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

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A Session-Based Song Recommendation Approach Involving User Characterization along the Play Power-Law Distribution

Cited by 14 publications

References 41 publications

Song Recommendation Application Using Speech Emotion Recognition

Song Recommendation Application Using Speech Emotion Recognition

An Approach to Integrating Sentiment Analysis into Recommender Systems

An Approach to Integrating Sentiment Analysis into Recommender Systems

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