Effective rating prediction based on selective contextual information

Dridi, Rim; Zammali, Saloua; Alsulimani, Tagreed; Arour, Khedija

doi:10.1016/j.ins.2019.09.008

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…Thus, using item labels to measure user similarity can effectively improve the prediction accuracy. In Figure 5, it is obvious that Case 3 has the smaller MAE and RMSE than Case 2, which confirm that the proposed prediction method is better than the commonly used method in equation (9). The tests are also performed in the dataset Eachmovie, and the results are illustrated in Figure 6.…”

Section: Evaluation Metricsmentioning

confidence: 73%

“…Collaborative filtering (CF) is one of the most common and promising algorithms in the recommender system [5][6][7][8]. The CF algorithms generally can be divided into two main types [9,10]: the model-based CF algorithms and the neighbourhood-based CF algorithms. The model-based CF algorithm constructs a model by learning skills from training data and then outputs the prediction results based on this model [11].…”

Section: Introductionmentioning

confidence: 99%

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A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

Wang

Zhao

Zhang

et al. 2021

Journal of Information Science

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The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.

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Section: Evaluation Metricsmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

Wang

Zhao

Zhang

et al. 2021

Journal of Information Science

View full text Add to dashboard Cite

show abstract

“…Recommender system (RS) handles the overload issues by providing adequate data on a variety of information according to the user preference or the observed actions regarding items [1]- [3]. With the exponential development in e-commerce and social networks, consumers are now contributing by writing reviews, suggestions on some types of products or services, or by putting them online.…”

Section: Introductionmentioning

confidence: 99%

Improving collaborative filtering using lexicon-based sentiment analysis

Sallam

Hussein

Mousa

2022

IJECE

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<span>Since data is available increasingly on the Internet, efforts are needed to develop and improve recommender systems to produce a list of possible favorite items. In this paper, we expand our work to enhance the accuracy of Arabic collaborative filtering by applying sentiment analysis to user reviews, we also addressed major problems of the current work by applying effective techniques to handle the scalability and sparsity problems. The proposed approach consists of two phases: the sentiment analysis and the recommendation phase. The sentiment analysis phase estimates sentiment scores using a special lexicon for the Arabic dataset. The item-based and singular value decomposition-based collaborative filtering are used in the second phase. Overall, our proposed approach improves the experiments’ results by reducing average of mean absolute and root mean squared errors using a large Arabic dataset consisting of 63,000 book reviews.</span>

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Contextual Recommender Systems in Business from Models to Experiments

Arour

Dridi

2022

Machine Learning and Data Analytics for Solving Business Problems

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Effective rating prediction based on selective contextual information

Cited by 15 publications

References 30 publications

A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

A collaborative filtering algorithm based on item labels and Hellinger distance for sparse data

Improving collaborative filtering using lexicon-based sentiment analysis

Contextual Recommender Systems in Business from Models to Experiments

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