A Hybrid Multi Feature Semantic Similarity Based Online Social Recommendation System Using CNN

2021 ◽  
Author(s):  
K. Saraswathi ◽  
V. Mohanraj ◽  
Y. Suresh ◽  
J. Senthil Kumar
Keyword(s):  
Semantic Similarity ◽  
Recommendation System ◽  
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.

scholarly journals Picture semantic similarity search based on bipartite network of picture-tag type

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259028
Author(s):  
Mingxi Zhang ◽  
Liuqian Yang ◽  
Yipeng Dong ◽  
Jinhua Wang ◽  
Qinghan Zhang
Keyword(s):  
Image Retrieval ◽  
Image Classification ◽  
Semantic Similarity ◽  
Similarity Search ◽  
Recommendation System ◽  
Search Method ◽  
Visual Features ◽  
Bipartite Network ◽  
Effectiveness And Efficiency ◽  
Image Recommendation

Searching similar pictures for a given picture is an important task in numerous applications, including image recommendation system, image classification and image retrieval. Previous studies mainly focused on the similarities of content, which measures similarities based on visual features, such as color and shape, and few of them pay enough attention to semantics. In this paper, we propose a link-based semantic similarity search method, namely PictureSim, for effectively searching similar pictures by building a picture-tag network. The picture-tag network is built by “description” relationships between pictures and tags, in which tags and pictures are treated as nodes, and relationships between pictures and tags are regarded as edges. Then we design a TF-IDF-based model to removes the noisy links, so the traverses of these links can be reduced. We observe that “similar pictures contain similar tags, and similar tags describe similar pictures”, which is consistent with the intuition of the SimRank. Consequently, we utilize the SimRank algorithm to compute the similarity scores between pictures. Compared with content-based methods, PictureSim could effectively search similar pictures semantically. Extensive experiments on real datasets to demonstrate the effectiveness and efficiency of the PictureSim.

Intelligent navigation of linked data with a graphical interface based on semantic similarity

2019 ◽  
Author(s):  
Pedro Fonseca-Ortiz ◽  
Hector G. Ceballos
Keyword(s):  
User Interface ◽  
Semantic Web ◽  
Semantic Similarity ◽  
Linked Data ◽  
Recommendation System ◽  
Web Applications ◽  
Graphical Interface ◽  
User Interest ◽  
Specialized Knowledge ◽  
Semantic Web Technology

"Semantic Web Technology proposes the use of linked data and ontologies as a mean for providing meaning to information. Even though several tools for the analysis and visualization of linked data exist, these tools require a lot of specialized knowledge to fulfill a purpose. Additionally, this complexity hardens its use for nonexperienced users therefore limiting semantic web applications. This paper describes a tool that combines the use of a recommendation system and an intuitive dynamic user interface for navigating linked data. The tool guides the user to find resources of interest by highlighting those related to his search intention. This is, the platform learns on the fly the user interest and makes recommendations based on the connections between resources."

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 Design and Implementation of Adaptive Recommendation System

2018 ◽  
pp. 101-117
Author(s):  
MagedEla zony ◽  
Ahmed Khalifa ◽  
Sayed Nouh ◽  
Mohamed Hussein
Keyword(s):  
Semantic Web ◽  
Semantic Similarity ◽  
Recommendation System ◽  
Learning Objects ◽  
Just In Time ◽  
Web Based ◽  
Design And Implementation ◽  
Knowledge Based ◽  
E Learning ◽  
Learner Profile

E-learning offers advantages for E-learners by making access to learning objects at any time or place, very fast, just-in-time and relevance. However, with the rapid increase of learning objects and it is syntactically structured it will be time-consuming to find contents they really need to study.In this paper, we design and implementation of knowledge-based industrial reusable, interactive web-based training and use semantic web based e-learning to deliver learning contents to the learner in flexible, interactive, and adaptive way. The semantic and recommendation and personalized search of Learning objects is based on the comparison of the learner profile and learning objects to determine a more suitable relationship between learning objects and learner profiles. Therefore, it will advise the e-learner with most suitable learning objects using the semantic similarity.

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