A Comparison Study of Different Privacy Preserving Techniques in Collaborative Filtering Based Recommender System

2019 ◽  
pp. 193-203
Author(s):  
Sitikantha Mallik ◽  
Abhaya Kumar Sahoo
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Privacy Preserving ◽  
Comparison Study

scholarly journals An Evaluation Framework of Trust Aware Recommender System

2018 ◽  
Vol 7 (4.33) ◽  
pp. 5
Author(s):  
S. Masrom ◽  
N. Khairuddin ◽  
A. Abdul Rahman ◽  
A. Azizan ◽  
A. S.A. Rahman
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
Evaluation Framework ◽  
Research Progress ◽  
Comparison Study ◽  
Common Group ◽  
Common Problems ◽  
Content Based Filtering

To date, there exists a variety of prediction approaches have been used in recommender systems. Among the widely known approaches are Content Based Filtering (CBF) and Collaborative Filtering (CF). Based on literatures, CF with users rating element has been widely used but the approach faced two common problems namely cold start and sparsity. As an alternative, Trust Aware Recommender Systems (TARS) for the CF based users rating has been introduced.  The research progress on TARS improvement is found to be rapidly progressing but lacking in the algorithm evaluation has been started to appear. Many researchers that introduced their new TARS approach provides different evaluation of users’ views for the TARS performances. As a result, the performances of different TARS from different publications are not comparable and difficult to be analyzed. Therefore, this paper is written with objective to provide common group of the users’ views based on trusted users in TARS. Then, this paper demonstrates a comparison study between different TARS techniques with the identified common groups by means of the accuracy error, rating and users coverage. The results therefore provide a relative comparison between different TARS. 

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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