Short Review of Sentiment-Based Recommender Systems

Barrière, Valentin; Kembellec, Gérald

doi:10.1145/3240117.3240120

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…The experiment was performed on Python 3.7 with various modules packages. Here, sentiment analysis is incorporated in recommendation systems to improve performance and deal with the sparsity problem [28], [29].…”

Section: Data Set and Experimental Setupmentioning

confidence: 99%

SARWAS Recommendation Framework Using Deep Learning

Choudhary

Singh

Kumar

2022

Preprint

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Sentiment-based recommendation systems are growing very fast nowadays , as users cannot express their opinion on the Likert scale from 1 to 5. Most of the current techniques work on either one of the parameters (ratings or reviews), mainly on reviews. This paper explored a new algorithm, SARWAS, involving both ratings and reviews for the recommendation system. This paper proposed a deep learning model using a sentiment and rating weighted association score (SARWAS) framework for combining ratings and reviews. We scraped reviews from e-commerce sites and calculated polarity and subjectivity for each review. Then a neural network model is further applied to calculate the weights and determine a combined score for a product. We evaluated the proposed model in terms of correlation between rating, review, and recommendation. It is being observed from the experiment that the proposed method produced satisfactory results in terms of accuracy. The correlation of reviews and recommendations is estimated to be more than ratings and recommendations with improved accuracy and precision of 95 percent and 89 percent.

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Section: Data Set and Experimental Setupmentioning

confidence: 99%

SARWAS Recommendation Framework Using Deep Learning

Choudhary

Singh

Kumar

2022

Preprint

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“…Consequently, in recent years, a variety of review-based recommender systems have been developed to incorporate valuable information embedded in user-generated textual comments [11]. Although the reviews are in unstructured textual forms, the advances in sentiment analysis make it possible to interpret and extract useful information [35].…”

Section: Use Of Text Information In Collaborative Filteringmentioning

confidence: 99%

A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector

Leiva

Tsai

2021

Applied Sciences

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Research regarding collaborative filtering recommenders has grown fast lately. However, little attention has been paid to discuss how the input data quality impacts the result. Indeed, some review-rating pairs that a user gave to an item are inconsistent and express a different opinion, making the recommendation result biased. To solve the above drawback, this study proposes a two-phase deep learning-based recommender system. Firstly, a sentiment predictor of textual reviews is created, serving as the quality inspector that cleans and improves the input for a recommender. To build accurate predictors, this phase tries and compares a set of deep learning-based algorithms. Secondly, besides only exploiting the consistent review-rating pairs generated by the quality inspector, this phase builds deep learning-based recommender engines. The experiments on a real-world dataset showed the proposed data quality inspector, based on textual reviews, improves the overall performance of recommenders. On average, applying deep learning-based quality inspectors result in an above 6% improvement in RMSE, and more than a 2% boost in F1 score, and accuracy. This is robust evidence to prove the importance of the input data cleaning process in this field. Moreover, empirical evidence indicates the deep learning approach is suitable for modeling the sentiment predictor, and the core recommendation process, clearly outperforming the traditional machine learning methods.

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“…Aspect-sentiment based studies generally mine the opinion of users towards different aspects of items and present the mined results [20] [21]. The extracted aspects and the related sentiments are left without further analysis as in the two studies in [21] and [22].…”

Section: Introductionmentioning

confidence: 99%

A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

Poudel¹,

Bikdash²

2023

JCC

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Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users' sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.

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Short Review of Sentiment-Based Recommender Systems

Cited by 6 publications

References 20 publications

SARWAS Recommendation Framework Using Deep Learning

SARWAS Recommendation Framework Using Deep Learning

A Two-Phase Deep Learning-Based Recommender System: Enhanced by a Data Quality Inspector

A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

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