EMUCF: Enhanced multistage user-based collaborative filtering through non-linear similarity for recommendation systems

Similarity Measures for Recommender Systems: Drawbacks and Neighbors Formation

“…They concluded that these measures could not generate trustworthy neighbors if the rating matrix is sparse. Jain et al [15] noticed that PCC showed low similarity scores for similar patterns and high similarity scores for different patterns. In addition, they found that MSD degraded accuracy due to ignoring the number of shared histories between users, and Jaccard MSD cannot see the actual preference difference between users.…”

A precise bare simulation approach to the minimization of some distances. Foundations

“…There is a vast literature on very recent applications of the Bhattacharyya coefficient, for instance it appears exemplarily in Peng & Li [289] for object tracking from successive video frames, Ayed et al [26] for efficient graph cut algorithms, Patra et al [287] for collaborative filtering in sparse data, El Merabet et al [119] for region classification in intelligent transport systems in order to compensate the lack of performance of Global Navigation Satellites Systems, Chiu et al [86] for the design of interactive mobile augmented reality systems, Noh et al [274] for dimension reduction in interacting fluid flow models, Bai et al [29] for material defect detection through ultrasonic array imaging, Dixit & Jain [115] for the design of recommender systems on highly sparse context aware datasets, Guan et al [143] for visible light positioning methods based on image sensors, Lin et al [220] for probabilistic representation of color image pixels, Chen et al [80] for distributed compressive video sensing, Jain et al [162] for the enhancement of multistage user-based collaborative filtering in recommendation systems, Pascuzzo et al [285] for brain-diffusion-MRI based early diagnosis of the sporadic Creutzfeldt-Jakob disease, Sun et al [351] for the design of automatic detection methods multitemporal (e.g. landslide) point clouds, Valpione et al [377] for the investigation of T cell dynamics in immunotherapy, Wang et al [387] for the tracking and prediction of downbursts from meteorological data, Xu et al [403] for adaptive distributed compressed video sensing for coal mine monitoring, Zhao et al [424] for the shared sparse machine learning of the affective content of images, Chen et al [82] for image segmentation and domain partitioning, De Oliveira et al [105] for the prediction of cell-penetrating peptides, Eshaghi et al [122] for the identification of multiple sclerosis subtypes through machine learning of brain MRI scans, Feng et al [125] for improvements of MRI-based detection of epilepsy-causing cortical malformations, Hanli et al [153] for designing pilot protection schemes for transmission lines, Jiang et al [170] for flow-assisted visual tracking through event cameras, Lysiak & Szmajda …”

“…is strictly increasing and smooth on the respective ]a F gKL,α, c , b F gKL,α, c [, and thus, F gKL,α, c ∈ F. Since F gKL,α, c (1) = 0, let us choose the natural anchor point c := 0, which leads to ]λ − , λ (162) (see also (164))…”

Recommendation Model Based on Semantic Features and a Knowledge Graph

“…The item-based collaborative filtering algorithm is to find items that are similar to the user's preferred items for recommendation. Because these two collaborative filtering recommendation algorithms have some deficiencies, such as items with high popularity and cold start, scholars have proposed several recommendation algorithms based on hybrid strategies [10][11][12]. However, traditional recommendation algorithms mainly consider aspects such as ratings and user features and often ignore important semantic features.…”

“…Experiment 8. In the experiment, we choose the traditional collaborative filtering recommendation technology (CF), collaborative filtering recommendation integrating the usercentric natural nearest neighbor (CF3N) [5], enhanced multistage user-based collaborative filtering through nonlinear similarity (EMUCF) [12], and deep neural network-based recommendation algorithm (DNN) [31] in comparison with the method proposed in this paper (SF_EU_DBpedia), since the EMUCF and DNN do not perform experiments based on the numbers of the nearest neighbor; for comparison, we select the best experimental results of various algorithms. The experimental results are shown in Figures 6 and 7 as follows:…”