scholarly journals A Novel Neighborhood Calculation Method by Assessing Users' Varying Preferences in Collaborative Filtering

10.29007/3xfj ◽  
2019 ◽  
Author(s):  
Pradeep Kumar Singh ◽  
Pijush Kanti Dutta Pramanik ◽  
Narayan C. Debnath ◽  
Prasenjit Choudhury
Keyword(s):  
Collaborative Filtering ◽  
Calculation Method ◽  
Performance Metrics ◽  
Personalized Recommendation ◽  
Continuous Change ◽  
Traditional Methods ◽  
Time Period ◽  
Target User ◽  
Time Periods ◽  
Neighbor Hood

To recommend an item to a target user, Collaborative Filtering (CF) considers the preferences of other similar users or neighbors. The accuracy of the recommendation depends on the effectiveness of assessing the neighbors. But over the time, the mutual likings of two individuals change; hence, the neighbors of the target user also should change. However, this shifting of preferences is not considered by traditional methods of calculating neighborhood in CF. As a result, the calculated set of neighbors does not always reflect the optimal neighborhood at any given point of time. In this paper, we argue for considering the continuous change in likings of the previous similar users and calculating the neighbor- hood of a target user based on different time periods. We propose a method that assesses the similarity between users in the different time period by using K-means clustering. This approach significantly improves the accuracy in the personalized recommendation. The performance of the proposed algorithm is tested on the MovieLens datasets (ml-100k and ml-1m) using different performance metrics viz. MAE, RMSE, Precision, Recall, F-score, and accuracy.

scholarly journals An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaofeng Li ◽  
Dong Li
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Recommendation System ◽  
Detection Algorithm ◽  
Personalized Recommendation ◽  
Filtering Algorithm ◽  
Collaborative Recommendation ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Community Detection Algorithm

The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

A Collaborative Filtering Recommendation Algorithm Based on Time Weight

2010 ◽  
Vol 159 ◽  
pp. 667-670
Author(s):  
Yae Dai
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Web Based ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Time Periods ◽  
Filtering Approach

Personalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. Collaborative filtering algorithm is one of the most successful technologies for building personalized recommendation system. But traditional collaborative filtering algorithm does not consider the problem of drifting users interests and the nearest neighbor user set in different time periods, leading to the fact that neighbors may not be the nearest set. In view of this problem, a collaborative filtering recommendation algorithm based on time weight is presented. In the algorithm each rating is assigned a weight gradually decreasing along with time and the weighted rating is used to produce recommendation. The collaborative filtering approach based on time weight not only reduced the data sparsity, but also narrowed the area of the nearest neighbor.

Leveraging Collaborative-Filtering for Personalized Behavior Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 1-27
Author(s):  
Xuhai Xu ◽  
Prerna Chikersal ◽  
Janine M. Dutcher ◽  
Yasaman S. Sefidgar ◽  
Woosuk Seo ◽  
...  
Keyword(s):  
College Students ◽  
Collaborative Filtering ◽  
Human Behavior ◽  
Work Performance ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Model Performance ◽  
Majority Voting ◽  
Behavior Modeling ◽  
Depression Detection

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of-the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.

scholarly journals Longitudinal birth cohort study finds that life-course frailty associates with later-life heart size and function

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Constantin-Cristian Topriceanu ◽  
James C. Moon ◽  
Rebecca Hardy ◽  
Nishi Chaturvedi ◽  
Alun D. Hughes ◽  
...  
Keyword(s):  
Life Course ◽  
Birth Cohort ◽  
Left Ventricular ◽  
Step Change ◽  
Deficit Accumulation ◽  
Cardiac Phenotype ◽  
Time Period ◽  
Time Periods ◽  
The Life Course ◽  
Health Deficit

AbstractA frailty index (FI) counts health deficit accumulation. Besides traditional risk factors, it is unknown whether the health deficit burden is related to the appearance of cardiovascular disease. In order to answer this question, the same multidimensional FI looking at 45-health deficits was serially calculated per participant at 4 time periods (0–16, 19–44, 45–54 and 60–64 years) using data from the 1946 Medical Research Council (MRC) British National Survey of Health and Development (NSHD)—the world’s longest running longitudinal birth cohort with continuous follow-up. From these the mean and total FI for the life-course, and the step change in deficit accumulation from one time period to another was derived. Echocardiographic data at 60–64 years provided: ejection fraction (EF), left ventricular mass indexed to body surface area (LVmassi, BSA), myocardial contraction fraction indexed to BSA (MCFi) and E/e′. Generalized linear models assessed the association between FIs and echocardiographic parameters after adjustment for relevant covariates. 1375 participants were included. For each single new deficit accumulated at any one of the 4 time periods, LVmassi increased by 0.91–1.44% (p < 0.013), while MCFi decreased by 0.6–1.02% (p < 0.05). A unit increase in FI at age 45–54 and 60–64, decreased EF by 11–12% (p < 0.013). A single health deficit step change occurring between 60 and 64 years and one of the earlier time periods, translated into higher odds (2.1–78.5, p < 0.020) of elevated LV filling pressure. Thus, the accumulation of health deficits at any time period of the life-course associates with a maladaptive cardiac phenotype in older age, dominated by myocardial hypertrophy and poorer function.

scholarly journals Comorbid disease burden among MS patients 1968–2012: A Swedish register–based cohort study

2020 ◽  
pp. 135245852091049 ◽  
Author(s):  
Kelsi A Smith ◽  
Sarah Burkill ◽  
Ayako Hiyoshi ◽  
Tomas Olsson ◽  
Shahram Bahmanyar ◽  
...  
Keyword(s):  
General Population ◽  
Age Groups ◽  
Comorbid Disease ◽  
Hazard Ratios ◽  
Time Period ◽  
Younger Age ◽  
Cardiovascular Depression ◽  
Prevalence Prevalence ◽  
Time Periods ◽  
Prevalence Ratios

Background: People with multiple sclerosis (pwMS) have increased comorbid disease (CMD) risk. Most previous studies have not considered overall CMD burden. Objective: To describe lifetime CMD burden among pwMS. Methods: PwMS identified using Swedish registers between 1968 and 2012 ( n = 25,476) were matched by sex, age, and county of residence with general-population comparators ( n = 251,170). Prevalence, prevalence ratios (PRs), survival functions, and hazard ratios by MS status, age, and time period compared seven CMD: autoimmune, cardiovascular, depression, diabetes, respiratory, renal, and seizures. Results: The magnitude of the PRs for each CMD and age group decreased across time, with higher PRs in earlier time periods. Before 1990, younger age groups had higher PRs, and after 1990, older age groups had higher PRs. Male pwMS had higher burden compared with females. Overall, renal, respiratory, and seizures had the highest PRs. Before 2001, 50% of pwMS received a first/additional CMD diagnosis 20 years prior to people without MS, which reduced to 4 years after 2001. PwMS had four times higher rates of first/additional diagnoses in earlier time periods, which reduced to less than two times higher in recent time periods compared to people without MS. Conclusion: Swedish pwMS have increased CMD burden compared with the general population, but this has reduced over time.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

scholarly journals Visualizing Belief in Meritocracy, 1930–2010

2018 ◽  
Vol 4 ◽  
pp. 237802311881180 ◽  
Author(s):  
Jonathan J. B. Mijs
Keyword(s):  
Survey Data ◽  
Representative Sample ◽  
Western World ◽  
Birth Cohorts ◽  
Popular Belief ◽  
Time Period ◽  
Level Data ◽  
Two Measures ◽  
Time Periods ◽  
Over Time

In this figure I describe the long trend in popular belief in meritocracy across the Western world between 1930 and 2010. Studying trends in attitudes is limited by the paucity of survey data that can be compared across countries and over time. Here, I show how to complement survey waves with cohort-level data. Repeated surveys draw on a representative sample of the population to describe the typical beliefs held by citizens in a given country and period. Leveraging the fact that citizens surveyed in a given year were born in different time-periods allows for a comparison of beliefs across birth cohorts. The latter overlaps with the former, but considerably extends the time period covered by the data. Taken together, the two measures give a “triangulated” longitudinal record of popular belief in meritocracy. I find that in most countries, popular belief in meritocracy is (much) stronger for more recent periods and cohorts.

scholarly journals The Effects of Power Levels/Time Periods for Sputtering Cobalt onto Carbon Nanotubes/Graphene Composites and Cobalt Annealed on the Characteristics of Anode Materials for Lithium-Ion Batteries

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chuen-Chang Lin ◽  
You-Lun Shen ◽  
An-Na Wu
Keyword(s):  
Carbon Nanotubes ◽  
Cobalt Oxide ◽  
Anode Materials ◽  
Specific Capacity ◽  
Rf Sputtering ◽  
Lithium Ion ◽  
Chemical Vapor ◽  
Electrochemical Stability ◽  
Time Period ◽  
Time Periods

Carbon nanotubes/graphene composites are directly grown on nickel foil without additional catalysts by chemical vapor deposition (CVD). Next, the cobalt is deposited on carbon nanotubes/graphene composites by radio frequency (RF) sputtering with different power levels and time periods. Then, the cobalt is transformed into cobalt oxide by annealing. A longer time period of sputtering leads to higher specific capacity. Furthermore, the electrochemical stability of cobalt oxide/carbon nanotubes/graphene composites is higher than that of cobalt oxide.

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