Privacy Preserving Collaborative Filtering Approach for Recommendation System

— Among algorithms in recommendation system, Collaborative Filtering (CF) is a popular one. However, the CF methods can’t guarantee the safety of the user rating data which cause private preserving issue. In general, there are four kinds of methods to solve private preserving: Perturbation, randomization, swapping and encryption. In this paper, we mimic algorithms which attack the privacy-preserving methods with randomized perturbation techniques. After leaking part of rating history of a customer, we can infer this customer’s other rating history. At the end, we propose an algorithm to enhance the system so as to avoid being attacked.

Download Full-text

Clustering collaborative filtering approach for Diftari E-Learning platform' recommendation system

Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications - SITA'18 ◽

10.1145/3289402.3289535 ◽

2018 ◽

Author(s):

Jamal Mawane ◽

Abdelwahab Naji ◽

Mohammed Ramdani

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Learning Platform ◽

E Learning ◽

Filtering Approach

Download Full-text

Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

Kinetik Game Technology Information System Computer Network Computing Electronics and Control ◽

10.22219/kinetik.v5i3.1062 ◽

2020 ◽

pp. 179-186

Author(s):

Zainur Romadhon ◽

Eko Sediyono ◽

Catur Edi Widodo

Keyword(s):

Collaborative Filtering ◽

Fast Food ◽

Recommendation System ◽

Black Pepper ◽

The Internet ◽

Daily Lives ◽

A Value ◽

Similarity Calculation ◽

Item Data ◽

Filtering Approach

The Recommendation System plays an increasingly important role in our daily lives. With the increasing amount of information on the internet, the recommendation system can also solve problems caused by increasing information quickly. Collaborative filtering is one method in the recommendation system that makes recommendations by analyzing correlations between users. Collaborative filtering accumulates customer item ratings, identifies customers with common ratings, and offers recommendations based on inter-customer comparisons. This study aims to build a system that can provide recommendations to users who want to order or choose fast food menus. This recommendation system provides recommendations based on item data calculations with customer review data using a collaborative filtering approach. The results of applying cosine similarity calculation to determine fast food menu recommendations obtained for the item-based recommendation is Pizza Frankfurter BBQ Large with a value of 1.0, item-based with genre recommendation is Calblend Float with value 1.0 and user-based recommendation is Pizza Black Pepper Beef / Chicken Large with mean score 2.5.

Download Full-text

Movie Recommendation System using Machine Learning

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35741 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4778-4781

Author(s):

Ghanashyam Vibhandik

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Cosine Similarity ◽

Daily Lives ◽

Movie Content ◽

Movie Recommendation ◽

The Many ◽

The Individual ◽

Content Based Filtering ◽

Filtering Approach

Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.

Download Full-text