Dementia-related user-based collaborative filtering for imputing missing data and generating a reliability scale on clinical test scores

Okyay, Savas; Adar, Nihat

doi:10.7717/peerj.13425

“…Collaborative filtering is a main algorithm of e-commerce recommendation system [1]. It can be divided into three types: userbased collaborative filtering [6], item-based collaborative Filtering [7] and model-based collaborative filtering [8]. Next, we will introduce these three methods respectively.…”

Section: Collaborative Filtering Recommendationmentioning

confidence: 99%

Piecewise Weighting Function for Collaborative Filtering Recommendation

Zhang¹,

Li²

2022

Preprint

0

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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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“…Collaborative filtering is a main algorithm of e-commerce recommendation system [1]. It can be divided into three types: userbased collaborative filtering [6], item-based collaborative Filtering [7] and model-based collaborative filtering [8]. Next, we will introduce these three methods respectively.…”

Section: Collaborative Filtering Recommendationmentioning

confidence: 99%

Piecewise Weighting Function for Collaborative Filtering Recommendation

Zhang¹,

Li²

2022

Preprint

0

View full text Add to dashboard Cite

<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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Score filtering for contextualized noise suppression of Poisson‐distributed geophysical signals

Altdorff,

Schrön

2024

Near Surface Geophysics

0

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Geophysical and remote sensing products that rely on Poisson‐distributed measurement signals, such as cosmic‐ray neutron sensing (CRNS) and gamma spectrometry, often face challenges due to inherent Poisson noise. Common techniques to enhance signal stability include data aggregation or smoothing (e.g., moving averages and interpolation). However, these methods typically reduce the ability to resolve detailed temporal (stationary data) and spatial (mobile data) features. In this study, we introduced a method for contextual noise suppression tailored to Poisson‐distributed data, utilizing a discrete score attribution system. This score filter evaluates each observation against eight different criteria to assess its consistency with surrounding values, assigning a score between 0 (very unlikely) and 8 (very likely) to indicate whether the observation is likely to act as noise. These scores can then be used to flag or remove data points based on user‐defined thresholds. We tested the score filter's effectiveness on both stationary and mobile CRNS data, as well as on gamma‐ray spectrometry and electromagnetic induction (EMI) recordings. In our examples, the score filter consistently outperformed established filters, for example Savitzky–Golay and Kalman, in direct competition when applied to CRNS time series data. Additionally, the score filter substantially reduced Poisson noise in mobile CRNS, gamma‐ray spectrometry and EMI data. The scoring system also provides a context‐sensitive evaluation of individual observations or aggregates, assessing their conformity within the dataset. Given its general applicability, customizable criteria and very low computational demands, the proposed filter is easy to implement and holds promise as a valuable tool for denoising geophysical data and applications in other fields.

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Dementia-related user-based collaborative filtering for imputing missing data and generating a reliability scale on clinical test scores

Cited by 4 publications

References 36 publications

Piecewise Weighting Function for Collaborative Filtering Recommendation

Piecewise Weighting Function for Collaborative Filtering Recommendation

Piecewise Weighting Function for Collaborative Filtering Recommendation

Score filtering for contextualized noise suppression of Poisson‐distributed geophysical signals

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