Development of Novel Model for Trustable Users Recommendation in OSN | A Computational Model for Identifying Trustable Users in Online Social Network

M. Ishwariya (u. a.)

ISBN 10: 3659644072 / ISBN 13: 9783659644078

Published by LAP LAMBERT Academic Publishing, 2022

About this Item

Development of Novel Model for Trustable Users Recommendation in OSN | A Computational Model for Identifying Trustable Users in Online Social Network | M. Ishwariya (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9783659644078 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 121437109

Bibliographic Details

• Title: Development of Novel Model for Trustable Users Recommendation in OSN | A Computational Model for Identifying Trustable Users in Online Social Network
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 3659644072
• ISBN 13: 9783659644078
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 113 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The vastness of social media ensures that there is no control on the scope of information. Such situations pose a major challenge of trust and security. When the trustworthiness is considered, Social Network (SN) category proves a major risk in social media. The proposed study concentrates on “Social Networks”, the challenges faced and suggest a method to evaluate the trust of the user on the social network. Trusted similar user is recommended to alters of the ego user who is receiving friend request from another alter or to raise the friend request towards the trusted similar user. The recommended trust is computed using SDE based reputation model and topic modeling with LDA. Hybrid recommendation technique is implemented using CANOPY clustering which evolves various groups of trusted similar users. Each group is evaluated for its quality and the users within the group are recommended for friend request. Effective performance of the recommendation system is evaluated.