Content based filtering in online social network using inference algorithm

Abstract

The basic issue in the online social network is to provide the ability for the user to manage the messages post on their wall. Online social networks offer only minimal assistance to avoid unwanted content displayed in the user wall. To enhance the support, a system is designed to filter unwanted messages and allow user to have direct control on the messages posted in the wall. It is achieved using flexible rule based system that allows the user to specify filtering rule for their wall. And, Inference algorithms are used to infer new information from the filtering rules to increase the efficiency of the filtering process. Machine Learning based soft classifier is employed to facilitate the content based filtering.

Recommendation and Classification Systems: A Systematic Mapping Study

“…Reference Building accurate and practical recommender system algorithms using machine learning classifier and collaborative filtering [20] DGA botnet detection using collaborative filtering and density-based clustering [21] A multistage collaborative filtering method for fall detection [22] Analysis and performance of collaborative filtering and classification algorithms [1] Extracting a vocabulary of surprise by collaborative filtering mixture and analysis of feelings [4] Content based filtering in online social network using inference algorithm [23] Building switching hybrid recommender system using machine learning classifiers and collaborative filtering [8] Imputation-boosted collaborative filtering using machine learning classifiers [24] CRISP-an interruption management algorithm based on collaborative filtering [25] A credit scoring model based on collaborative filtering [26] Collaborative filtering recommender systems [2] An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering [6] Tweet modeling with LSTM recurrent neural networks for hashtag recommendation [27] A two-stage cross-domain recommendation for cold start problem in cyber-physical systems [28] ELM based imputation-boosted proactive recommender systems [29] Twitter-user recommender system using tweets: a content-based approach [30] A personalized time-bound activity recommendation system [31] Automated content based short text classification for filtering undesired posts on Facebook [32] Shilling attack detection in collaborative recommender systems using a meta learning strategy [33] Building a distributed generic recommender using scalable data mining library [34] Context-aware movie recommendation based on signal processing and machine learning [35] Recommender systems using linear classifiers [36] A survey of accuracy evaluation metrics of recommendation tasks [3] Incorporating user control into recommender systems based on naive Bayesian classification [37] Classification features for attack detection in collaborative recommender systems [38] Automatic tag recommendation algorithms for social recommender systems [39] Optimizing similar item recommendations in a semistructured marketplace to maximize conversion …”

Advance Identification of Cloning Attacks in Online Social Networks

“…Neural learning model is utilized for the proficient content characterization. [3] Fortunato et al A subset of clustering calculations is considered with different information. The mutual data portrays people as hubs and their companionships as connections.…”

Controlling and Mitigating Targeted Socio-Economic Attacks

“…Content-based filtering in online social networks has good results in the case of text or information. Content-based filtering can be applied concurrently at the same time when the text is getting uploaded [10] [11]. But this is not the case with videos or photographs.…”