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.

Pre-Filling Collaborative Filtering Algorithm Based on Matrix Factorization

2013 ◽  
Vol 411-414 ◽  
pp. 2223-2228
Author(s):  
Dong Liang Su ◽  
Zhi Ming Cui ◽  
Jian Wu ◽  
Peng Peng Zhao
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Matrix Factorization ◽  
Nearest Neighbor ◽  
User Profile ◽  
Personalized Recommendation ◽  
System Quality ◽  
Recommendation Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Personalized Recommender System

Nowadays personalized recommendation algorithm of e-commerce can hardly meet the needs of users as an ever-increasing number of users and items in personalized recommender system has brought about sparsity of user-item rating matrix and the emergence of more and more new users has threatened recommender system quality. This paper puts forward a pre-filled collaborative filtering recommendation algorithm based on matrix factorization, pre-filling user-item matrixes by matrix factorization and building nearest-neighbor models according to new user profile information, thus mitigating the influence of matrix sparsity and new users and improving the accuracy of recommender system. The experimental results suggest that this algorithm is more precise and effective than the traditional one under the condition of extremely sparse user-item rating matrix.

scholarly journals Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xiushan Zhang
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Absolute Error ◽  
User Model ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms ◽  
Similarity Calculation ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Based on the understanding and comparison of various main recommendation algorithms, this paper focuses on the collaborative filtering algorithm and proposes a collaborative filtering recommendation algorithm with improved user model. Firstly, the algorithm considers the score difference caused by different user scoring habits when expressing preferences and adopts the decoupling normalization method to normalize the user scoring data; secondly, considering the forgetting shift of user interest with time, the forgetting function is used to simulate the forgetting law of score, and the weight of time forgetting is introduced into user score to improve the accuracy of recommendation; finally, the similarity calculation is improved when calculating the nearest neighbor set. Based on the Pearson similarity calculation, the effective weight factor is introduced to obtain a more accurate and reliable nearest neighbor set. The algorithm establishes an offline user model, which makes the algorithm have better recommendation efficiency. Two groups of experiments were designed based on the mean absolute error (MAE). One group of experiments tested the parameters in the algorithm, and the other group of experiments compared the proposed algorithm with other algorithms. The experimental results show that the proposed method has better performance in recommendation accuracy and recommendation efficiency.

Collaborative Filtering Algorithm Based on User Clustering

2013 ◽  
Vol 411-414 ◽  
pp. 1044-1048
Author(s):  
Zhao Deng ◽  
Jin Wang
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
User Clustering ◽  
Similarity Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Similarity Computation ◽  
Improved Algorithm

To overcome the uncertainty of the users neighborhoods in the recommendation algorithm of nearest neighbor, an improved collaborative filtering algorithm based on user clustering is proposed. This improved algorithm filters the users by their features, and the improved cosine similarity algorithm is used for the item similarity computation. Experiments on the MovieLens dataset showed that, compared with Lis collaborative filtering algorithm, the recommendation quality of the improved algorithm is more accurate and the category coverage is larger.

A Collaborative Filtering Recommendation Algorithm Combined with User and Item

2014 ◽  
Vol 513-517 ◽  
pp. 1878-1881
Author(s):  
Feng Ming Liu ◽  
Hai Xia Li ◽  
Peng Dong
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
User Evaluation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didnt evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.

scholarly journals Personalized News Recommendation and Simulation Based on Improved Collaborative Filtering Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kunni Han
Keyword(s):  
Collaborative Filtering ◽  
Nearest Neighbor ◽  
Data Preprocessing ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Data Set ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Accuracy

Faced with massive amounts of online news, it is often difficult for the public to quickly locate the news they are interested in. The personalized recommendation technology can dig out the user’s interest points according to the user’s behavior habits, thereby recommending the news that may be of interest to the user. In this paper, improvements are made to the data preprocessing stage and the nearest neighbor collection stage of the collaborative filtering algorithm. In the data preprocessing stage, the user-item rating matrix is filled to alleviate its sparsity. The label factor and time factor are introduced to make the constructed user preference model have a better expression effect. In the stage of finding the nearest neighbor set, the collaborative filtering algorithm is combined with the dichotomous K-means algorithm, the user cluster matching the target user is selected as the search range of the nearest neighbor set, and the similarity measurement formula is improved. In order to verify the effectiveness of the algorithm proposed in this paper, this paper selects a simulated data set to test the performance of the proposed algorithm in terms of the average absolute error of recommendation, recommendation accuracy, and recall rate and compares it with the user-based collaborative filtering recommendation algorithm. In the simulation data set, the algorithm in this paper is superior to the traditional algorithm in most users. The algorithm in this paper decomposes the sparse matrix to reduce the impact of data sparsity on the traditional recommendation algorithm, thereby improving the recommendation accuracy and recall rate of the recommendation algorithm and reducing the recommendation error.

scholarly journals Combined Recommendation Algorithm Based on Improved Similarity and Forgetting Curve

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 130 ◽  
Author(s):  
Taoying Li ◽  
Linlin Jin ◽  
Zebin Wu ◽  
Yan Chen
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Sparse Data ◽  
Average Precision ◽  
Recommendation Algorithm ◽  
Wide Range

The recommendation algorithm in e-commerce systems is faced with the problem of high sparsity of users’ score data and interest’s shift, which greatly affects the performance of recommendation. Hence, a combined recommendation algorithm based on improved similarity and forgetting curve is proposed. Firstly, the Pearson similarity is improved by a wide range of weighted factors to enhance the quality of Pearson similarity for high sparse data. Secondly, the Ebbinghaus forgetting curve is introduced to track a user’s interest shift. User score is weighted according to the residual memory of forgetting function. Users’ interest changing with time is tracked by scoring, which increases both accuracy of recommendation algorithm and users’ satisfaction. The two algorithms are then combined together. Finally, the MovieLens dataset is employed to evaluate different algorithms and results show that the proposed algorithm decreases mean absolute error (MAE) by 12.2%, average coverage 1.41%, and increases average precision by 10.52%, respectively.

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.

Subjective social class, perceived quality of social relationships, and happiness: A case of mediation and moderation across gender

2020 ◽  
Vol 35 (3) ◽  
pp. 312-335
Author(s):  
Jae-Woo Kim ◽  
Chaeyoon Lim ◽  
Christina Falci
Keyword(s):  
Social Class ◽  
Social Status ◽  
Relationship Quality ◽  
Social Relationships ◽  
Social Relationship ◽  
Perceived Quality ◽  
Mediating Effect ◽  
Men And Women ◽  
Subjective Social Class

This study investigates the link between social relationship and subjective well-being in the context of social stratification. The authors examine how perceived quality of social relationships and subjective social class are linked to self-reported happiness among men and women in South Korea. The study finds that one’s perception of relative social standing is positively associated with happiness independently of objective indicators of socioeconomic status, while social relationship quality strongly predicts the happiness among both men and women. However, the mediation pathway and moderating effects vary by gender. For men, the nexus between subjective social class and happiness is partially mediated by the quality of interpersonal relationships. No similar mediating effect is found among women. The study also finds gender difference in whether the link between social relationship quality and happiness varies by subjective social class. The happiness return to positive social relationships increases as men’s subjective social status becomes higher, which is consistent with the resource multiplication hypothesis. No similar moderation effect is found among women. Combined, these results reveal potentially different pathways to happiness across gender in Korea, where social status competition, collectivistic culture, and patriarchal gender relations are salient in daily life.

scholarly journals Enhanced Hybrid Recommender System using Social Friend Network

2014 ◽  
Vol 10 (4) ◽  
pp. 2023-2031
Author(s):  
Shalmali A. Patil ◽  
Reena Pagare
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Recommender System ◽  
Social Relationship ◽  
Information Overload ◽  
Great Success ◽  
Traditional System ◽  
Use Of Data ◽  
The Social ◽  
Hybrid Recommender

Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize users likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.

scholarly journals Quality of life and its association with current substance use, medication non-adherence and clinical factors of people with schizophrenia in Southwest Ethiopia: a hospital-based cross-sectional study

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Defaru Desalegn ◽  
Shimelis Girma ◽  
Tilahun Abdeta
Keyword(s):  
Quality Of Life ◽  
Substance Use ◽  
Linear Regression ◽  
Social Relationships ◽  
Social Relationship ◽  
Linear Regression Analysis ◽  
Physical Illness ◽  
Negative Association ◽  
Physical Domain

Abstract Background Schizophrenia was ranked as one of the top ten illnesses contributing to the global burden of disease. But little is known about the quality of life among people with schizophrenia, in particular in low-income countries. This study was aimed to examine the association of quality of life with current substance use, medication non-adherence and clinical factors of people with schizophrenia at Jimma University Medical Center, psychiatry clinic, Southwest Ethiopia. Methods Institution based cross-sectional study design was employed. Study participants were recruited using a systematic random sampling method and a sample fraction of two was used after the first person was identified by the lottery method. we used the World Health Organization Quality of Life Scale-Brief version (WHOQoL-BREF) and 4-item Morisky Medication Adherence Scale (MMAS-4) to assess the quality of life and medication non-adherence respectively. Data about current substance use was assessed by yes/no questions. Descriptive statistics, such as frequency, mean and standard deviations were computed to describe the characteristics of the study population. Data entry was done using EpiData version 3.1 then exported to SPSS statistics version 25 for analysis and analyzed using multiple linear regression. The assumption for linear regression analysis including the presence of a linear relationship between the outcome and predictor variable, the test of normality, collinearity statistics, auto-correlation and homoscedasticity were checked. Un-standardized Beta (β) coefficients with 95% confidence interval (CI) and P-value < 0.05 were computed to assess the level of association and statistical significance in the final multiple linear regression analysis. Result In this study 31.65% of participants were medication non-adherent and total mean scores of quality of life showed a lower level of satisfaction in social relationship domain (10.14 ± 3.12). Our study showed 152(43.3%), 248(70.7%) and 97(27.6%) of respondents had used tobacco, Khat and alcohol atleast once during the past 3 months respectively. Final adjusted multiple regression model showed medication non-adherence has significant negative association with physical domain (beta = − 4.42, p < 0.001), psychological (beta = − 4.49, p < 0.001), social relationships (beta = − 2.29, p < 0.001) and environmental domains (beta = − 4.95, p < 0.001). Treatment duration has significant negative association with psychological domain (beta = − 0.17, p < 0.04), social relationship (beta = − 0.14, p < 0.005), environmental domain (beta = − 0.24, p < 0.02) and overall quality of life (beta = − 0.67, p < 0.02). Having comorbid physical illness has significant negative association with physical domain (beta = − 2.74, p < 0.001), psychological (beta = − 2.13, p < 0.004), social relationships (beta = − 1.25, p < 0.007), environmental domain (beta = − 3.39, p < 0.001) and overall quality of life (beta = − 9.9, p < 0.001). Current tobacco use has significant negative association with physical domain (beta = − 1.16, p < 0.004), psychological (beta = − 1.23, p < 0.001), social relationships (beta = − 0.88, p < 0.001), environmental domains (beta = − 1.98, p < 0.001) and overall quality of life (beta = − 5.73, p < 0.001). Also, current chewing khat has significant negative association with physical domain (beta = − 1.15, p < 0.003), psychological (beta = − 1.58, p < 0.001), environmental domains (beta = − 2.63, p < 0.001) and overall quality of life (beta = − 6.22, p < 0.001). Conclusion The social relationship domain of quality of life has the lowest mean score. Medication non-adherence, treatment duration, having a comorbid physical illness, current tobacco use and current chewing khat were found to have a statistically significant association with the overall quality of life. Therefore, treatments aimed to improve social deficits, medication non-adherence, comorbid physical illness and decrease substance abuse is imperative.

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