A Folksonomy-Based Social Recommendation System for Scientific Workflow Reuse

2015 ◽  
Author(s):  
Aravind Mohan ◽  
Mahdi Ebrahimi ◽  
Shiyong Lu
Keyword(s):  
Recommendation System ◽  
Scientific Workflow ◽  
Social Recommendation

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

Using RSSI Simple Localization Method to Implement the Context-Aware and Social Recommendation System

2013 ◽  
Vol 479-480 ◽  
pp. 1213-1217
Author(s):  
Mu Yen Chen ◽  
Ming Ni Wu ◽  
Hsien En Lin
Keyword(s):  
Social Media ◽  
Social Relations ◽  
Context Awareness ◽  
Recommendation System ◽  
Product Information ◽  
Indoor Localization ◽  
Context Aware ◽  
Social Recommendation ◽  
Localization Method ◽  
The Social

This study integrates the concept of context-awareness with association algorithms and social media to establish the Context-aware and Social Recommendation System (CASRS). The Simple RSSI Indoor Localization Module (SRILM) locates the user position; integrating SRILM with Apriori Recommendation Module (ARM) provides effective recommended product information. The Social Media Recommendation Module (SMRM) connects to users social relations, so that the effectiveness for users to gain product information is greatly enhanced. This study develops the system based on actual context.

Collaborative filtering-based recommendation of online social voting

2018 ◽  
Vol 7 (3) ◽  
pp. 1504 ◽  
Author(s):  
Dr Mohammed Ismail ◽  
Dr K. Bhanu Prakash ◽  
Dr M. Nagabhushana Rao
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Nearest Neighbor ◽  
Social Trust ◽  
Social Recommendation ◽  
Peer Reviewers ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Cold Start Problem

Social voting is becoming the new reason behind social recommendation these days. It helps in providing accurate recommendations with the help of factors like social trust etc. Here we propose Matrix factorization (MF) and nearest neighbor-based recommender systems accommodating the factors of user activities and also compared them with the peer reviewers, to provide a accurate recommendation. Through experiments we realized that the affiliation factors are very much needed for improving the accuracy of the recommender systems. This information helps us to overcome the cold start problem of the recommendation system and also y the analysis this information was much useful to cold users than to heavy users. In our experiments simple neighborhood model outperform the computerized matrix factorization models in the hot voting and non hot voting recommendation. We also proposed a hybrid recommender system producing a top-k recommendation inculcating different single approaches.  

scholarly journals Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sheng Bin ◽  
Gengxin Sun
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Social Relationships ◽  
Matrix Factorization ◽  
Social Relationship ◽  
Recommendation System ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms

With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship.

scholarly journals A Comprehensive Social Matrix Factorization with Social Regularization for Recommendation Based on Implicit Similarity by Fusing Trust Relationships and Social Tags

2021 ◽  
Author(s):  
Rui Chen ◽  
Jianwei Zhang ◽  
Zhifeng Zhang ◽  
Yan-Shuo Chang ◽  
Jingli Gao ◽  
...  
Keyword(s):  
Social Relationships ◽  
Social Relationship ◽  
Recommendation System ◽  
Feature Space ◽  
Experimental Results ◽  
User Preference ◽  
Superior Performance ◽  
Social Recommendation ◽  
Social Tags ◽  
Trust Relationships

Abstract Social relationships play an important role in improving the quality of recommender systems (RSs). A large number of experimental results show that social relationship-based recommendation methods alleviate the problems of data sparseness and cold start in RSs to some extent. However, since the social relationships between users are extremely sparse and complex, and it is difficult to obtain accurately user preference model, thus the performance of the recommendation system is affected by the existing social recommendation methods. In order to accurately model social relationships and improve recommendation quality, we use explicit social relationships such as user-item ratings, trust relationships and implicit social relationships such as social tags to mine potential interest preferences of users and propose an improved social recommendation method integrating trust relationship and social tags. The method map user features and item features to the shared feature space by using the above social relationship, respectively, and obtains user similarity and item similarity through potential feature vectors of users and items, and continuously trains them to obtain accurate similarity relationship to improve the recommendation performance. Experimental results demonstrate that our proposed approach achieves superior performance to the other social recommendation approaches.

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