scholarly journals NAIS: Neural Attentive Item Similarity Model for Recommendation

2018 ◽  
Vol 30 (12) ◽  
pp. 2354-2366 ◽  
Author(s):  
Xiangnan He ◽  
Zhankui He ◽  
Jingkuan Song ◽  
Zhenguang Liu ◽  
Yu-Gang Jiang ◽  
...  
Keyword(s):  
Similarity Model ◽  
Item Similarity Model

scholarly journals An inter-item similarity model unifying feature and conjunction search

2006 ◽  
Vol 46 (22) ◽  
pp. 3867-3880 ◽  
Author(s):  
Steven Phillips ◽  
Yuji Takeda ◽  
Takatsune Kumada
Keyword(s):  
Conjunction Search ◽  
Similarity Model ◽  
Item Similarity Model

scholarly journals Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 12809-12821 ◽  
Author(s):  
Junmei Lv ◽  
Bin Song ◽  
Jie Guo ◽  
Xiaojiang Du ◽  
Mohsen Guizani
Keyword(s):  
Related Item ◽  
Multimodal Data ◽  
Model Based ◽  
Similarity Model ◽  
Item Similarity Model

scholarly journals Personalized treatment options for chronic diseases using precision cohort analytics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenney Ng ◽  
Uri Kartoun ◽  
Harry Stavropoulos ◽  
John A. Zambrano ◽  
Paul C. Tang
Keyword(s):  
Decision Making ◽  
Chronic Diseases ◽  
Primary Care Physicians ◽  
Treatment Guidelines ◽  
Point Of Care ◽  
Data Extraction ◽  
Treatment Options ◽  
Personalized Treatment ◽  
Medical Decision ◽  
Similarity Model

AbstractTo support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.

Towards effective link prediction: A hybrid similarity model

2020 ◽  
pp. 1-14
Author(s):  
Longjie Li ◽  
Lu Wang ◽  
Hongsheng Luo ◽  
Xiaoyun Chen
Keyword(s):  
Link Prediction ◽  
Structural Similarity ◽  
Research Direction ◽  
Structural Features ◽  
Proposed Model ◽  
Similarity Model ◽  
Weight Calculation ◽  
Stable Performance ◽  
Grey Relation ◽  
Important Research Direction

Link prediction is an important research direction in complex network analysis and has drawn increasing attention from researchers in various fields. So far, a plethora of structural similarity-based methods have been proposed to solve the link prediction problem. To achieve stable performance on different networks, this paper proposes a hybrid similarity model to conduct link prediction. In the proposed model, the Grey Relation Analysis (GRA) approach is employed to integrate four carefully selected similarity indexes, which are designed according to different structural features. In addition, to adaptively estimate the weight for each index based on the observed network structures, a new weight calculation method is presented by considering the distribution of similarity scores. Due to taking separate similarity indexes into account, the proposed method is applicable to multiple different types of network. Experimental results show that the proposed method outperforms other prediction methods in terms of accuracy and stableness on 10 benchmark networks.

scholarly journals A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 731
Author(s):  
Mengxia Liang ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Price ◽  
Multivariate Time Series ◽  
Temporal Features ◽  
Proposed Model ◽  
Time Series Segmentation ◽  
Similarity Model ◽  
Dynamic Time ◽  
Investment Portfolios

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

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