A Comparative Study on Prediction Approaches of Item-Based Collaborative Filtering in Neighborhood-Based Recommendations

Singh, Pradeep Kumar; Ahmed, Rafeeq; Rajput, Ishwari Singh; Choudhury, Prasenjit

doi:10.1007/s11277-021-08662-2

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…Here, MC is selected as the comparative method of our proposed prediction method. The main reason is MC performs more efficient than other traditional prediction methods, which has proved by a comparative study in [27]. And it should be noted that case3 algorithm is our proposed model ISP.…”

Section: The Effectiveness Test Of the Proposed Modelmentioning

confidence: 87%

“…In addition to the fundamental prediction methods, many researchers have put forward improvement strategies from different perspectives. Singh et al [27] presented a predictive approach called Z-Score, which considered the rating differences by converting the ratings to z-scores and calculating the weighted average of z-scores. A new User Rating Prediction (URP) [19] algorithm was proposed to predict ratings for items, which assumed that similar users may be interested in similar items.…”

Section: Item-based Prediction Methodsmentioning

confidence: 99%

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A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data

Zhao

Tian

et al. 2022

Int J Comput Intell Syst

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In memory-based collaborative filtering (CF) algorithms, the similarity and prediction method have a significant impact on the recommendation results. Most of the existing recommendation techniques have improved different similarity measures to alleviate inaccurate similarity results in sparse data, however, ignored the impact of sparse data on prediction results. To enhance the adaptability to sparse data, we propose a new item-based CF algorithm, which consists of the item similarity measure based vague sets and item-based prediction method with the new neighbor selection strategy. First, in the stage of similarity calculation, the Kullback–Leibler (KL) divergence based on vague sets is proposed from the perspective of user preference probability to measure item similarity. Following this, the impact of rating quantity is further considered to improve the accuracy of similarity results. Next, in the prediction stage, we relax the limit of depending on explicitly ratings and integrate more rating information to adjust prediction results. Experimental results on benchmark data sets show that, compared with other representative algorithms, our algorithm has better prediction and recommendation quality, and effectively alleviates the data sparseness problem.

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Section: The Effectiveness Test Of the Proposed Modelmentioning

confidence: 87%

Section: Item-based Prediction Methodsmentioning

confidence: 99%

A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data

Zhao

Tian

et al. 2022

Int J Comput Intell Syst

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“…Its core assumption is that the items that can arouse users' interest are similar to the items that users have previously scored high. For example, Singh et al compared some item-based filters [14]. Specifically, it first uses different measurement functions to calculate the first k similar items.…”

Section: Collaborative Filtering Recommendationmentioning

confidence: 99%

Piecewise Weighting Function for Collaborative Filtering Recommendation

Zhang¹,

Li²

2022

Preprint

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<p>It is not always reasonable to set a fixed weight value to an attribute (or a variable) because this cannot accurately maintain the similarity between users and leads to decrease the performance of collaborative filtering recommendation algorithms. In this paper, a piecewise weighting method is proposed with the hyper-class representation for improving the collaborative filtering recommendation. Specifically, a kernel function is first applied to map the original data into the kernel space, and to learn the weight of each attribute. And then, the hyper-class representation of the data is constructed to learn the weight of the segmented attribute value (hyper-class) for each attribute, so as to construct a piecewise weighting function. In addition, the piecewise weighting function is applied to calculate the similarity between users for a collaborative filtering recommendation. Finally, experiments are conducted to examine the collaborative filtering recommendation algorithm, and show that the proposed algorithm with piecewise weighting functions is more efficient than the compared algorithm with fixed weight values, in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Precision.</p>

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“…The latest innovations in Industry 4.0 have introduced a wide range of breakthrough technologies. Machine learning is a subset of artificial intelligence that provides cutting‐edge technological solutions to problems in different application domains 6 . Healthcare is one of the most important areas, leveraging the potential of these technologies for predictive analytics 7,8 .…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning is a subset of artificial intelligence that provides cutting-edge technological solutions to problems in different application domains. 6 Healthcare is one of the most important areas, leveraging the potential of these technologies for predictive analytics. 7,8 Different machine learning-based classification techniques are used to classify data into different categories based on available features.…”

mentioning

confidence: 99%

NSGA‐II‐XGB: Meta‐heuristic feature selection with XGBoost framework for diabetes prediction

Gupta

Rajput

Gunjan

et al. 2022

Concurrency and Computation

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Diabetes is one of the most prevalent causes of casualties in the modern world. Early diagnosis of diabetes is the most promising way for increasing the chances of patients' survival. The ever-growing technology of the current era, machine learning-based algorithms pave the door in the healthcare industry by delivering efficient decision support services in real-time. However, high-dimensionality of the data obtained using multiple sources increases the computation time and significantly impacts the models' efficiency in classifying the results. Feature selection improves learning performance and reduces the computational cost by selecting subsets of features and eliminating unnecessary and irrelevant features. In this article, an attempt has been made to develop a hybrid machine learning model based on non-dominated sorting genetic algorithm (NSGA-II) and ensemble learning for the efficient categorization of diabetes. The proposed work uses various data preprocessing techniques, such as missing data handling and normalization, prior to model training. The most prominent and salient features are selected by exploiting the potential of the NSGA-II in the diabetes dataset. Finally, an ensemble learning-based extreme gradient boosting (XGBoost) model is modeled using features selected by NSGA-II to classify patients as diabetic or non-diabetic. Theproposed methodology is experimentally validated using a hybridized dataset comprising 23 features, with 1288 instances of both male and female patients between the ages of 21 and 65. In addition, for performance evaluation, the results of statistical parameters are compared with several state-of-the-art decision-making models in the current domain. Experiment findings exemplify that the proposed NSGA-II-XGB approach gives better classification results with an average accuracy of 98.86%. Furthermore, the statistical results of specificity (88.6%), sensitivity (96.36%), and F-score (97.84%) also support the utility of the proposed methodology in the early diagnosis of diabetes.

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A Comparative Study on Prediction Approaches of Item-Based Collaborative Filtering in Neighborhood-Based Recommendations

Cited by 9 publications

References 45 publications

A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data

A New Item-Based Collaborative Filtering Algorithm to Improve the Accuracy of Prediction in Sparse Data

Piecewise Weighting Function for Collaborative Filtering Recommendation

NSGA‐II‐XGB: Meta‐heuristic feature selection with XGBoost framework for diabetes prediction

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