Social Balance Theory Based Hybrid Movie Recommendation System

Suggesting people exactly according to their likeness is most challenging in today’s generation. Present websites fail to provide the recommendation that is appropriate for people. There are several reasons such as there is either inadequate information about people or absence of feedback from the movies that they have watched. In this Situation considering those few/Sparse scores that are given that are collected from the people a socially balanced concept came into picture. Socially balanced theory Concept (hybrid) uses a integrated recommendation by combining both substance—oriented and community organized approach i.e., recommends based on both on viewers as well as movie. Socially balance theory helps to get better suggestion even when there is less information or inappropriate content by finding the opponent for the end users later discover the end users companion i.e., “opponents opponent is a companion” rule in social balance theory. So that suggestions can be based on both customer as well as goods based. For this, initially grouping the community is required to find the similarity between them. Finally the workability of integrated—recommendation is evaluated by considering film lens dataset – 10 M.

Logics of Allies and Enemies: A Formal Approach to the Dynamics of Social Balance Theory

We combine social balance theory with temporal logic to obtain a Logic of Allies and Enemies (LAE), which formally describes the likely changes to a social network due to social pressure. We demonstrate how the rich language of LAE can be used to describe various interesting concepts, and show that both model checking and validity checking are PSPACE-complete.

The “mutual ignoring” mechanism of cyberbalkanization: triangulating observational data analysis and agent-based modelling

Cyberbalkanization has been observed in some online activities, but its mechanism remains uncertain. Drawing on the Social Balance Theory, we theorized that the formation of “mutual ignoring” triads (three actors in a group with only a pair of interacts) in a network is a key procedure for cyberbalkanization. A cyberbalkanized network was constructed using data shared on Facebook Pages during the 2014 Hong Kong protests. Our findings indicate “mutual ignoring” was a prominent triad network configuration, whereas no such configuration was found in randomly generated networks. Our empirical-based observation was retested with simulation by agent-based model, confirming that “mutual ignoring” is essential for the emergence of cyberbalkanization. A strategy is then suggested to minimize cyberbalkanization.

Time-Aware IoE Service Recommendation on Sparse Data

With the advent of “Internet of Everything” (IoE) age, an excessive number of IoE services are emerging on the web, which places a heavy burden on the service selection decision of target users. In this situation, various recommendation techniques are introduced to alleviate the burden, for example, Collaborative Filtering- (CF-) based recommendation. Generally, CF-based recommendation approaches utilize similar friends or similar services to achieve the recommendation goal. However, due to the sparsity of user feedback, it is possible that a target user has no similar friends and similar services; in this situation, traditional CF-based approaches fail to produce a satisfying recommendation result. Besides, recommendation accuracy would be decreased if time factor is overlooked, as IoE service quality often varies with time. In view of these challenges, a time-aware service recommendation approach named Ser_Rectime is proposed in this paper. Concretely, we first calculate the time-aware user similarity; afterwards, indirect friends of the target user are inferred by Social Balance Theory (e.g., “enemy’s enemy is a friend” rule); finally, the services preferred by indirect friends of the target user are recommended to the target user. At last, through a set of experiments deployed on dataset WS-DREAM, we validate the feasibility of our proposal.