scholarly journals THE RESEARCH TO THE ROBUSTNESS OF RECOMMENDATION SYSTEMS WITH COLLABORATIVE FILTERING TO INFORMATION ATTACKS

2019 ◽  
pp. 95-104
Author(s):  
Yelyzaveta Meleshko ◽  
Vitaliy Khokh ◽  
Oleksandr Ulichev
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
User Profile ◽  
User Profiles ◽  
Injection Attacks ◽  
User Reputation ◽  
General Method

In this article research to the robustness of recommendation systems with collaborative filtering to information attacks, which are aimed at raising or lowering the ratings of target objects in a system. The vulnerabilities of collaborative filtering methods to information attacks, as well as the main types of attacks on recommendation systems - profile-injection attacks are explored. Ways to evaluate the robustness of recommendation systems to profile-injection attacks using metrics such as rating deviation from mean agreement and hit ratio are researched. The general method of testing the robustness of recommendation systems is described. The classification of collaborative filtration methods and comparisons of their robustness to information attacks are presented. Collaborative filtering model-based methods have been found to be more robust than memorybased methods, and item-based methods more resistant to attack than user-based methods. Methods of identifying information attacks on recommendation systems based on the classification of user-profiles are explored. Metrics for identify both individual bot profiles in a system and a group of bots are researched. Ways to evaluate the quality of user profile classifiers, including calculating metrics such as precision, recall, negative predictive value, and specificity are described. The method of increasing the robustness of recommendation systems by entering the user reputation parameter as well as methods for obtaining the numerical value of the user reputation parameter is considered. The results of these researches will in the future be directed to the development of a program model of a recommendation system for testing the robustness of various algorithms for collaborative filtering to known information attacks.

scholarly journals Hybrid Filtering Recommendation System in an Educational Context

2022 ◽  
Vol 17 (1) ◽  
pp. 0-0
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Educational Context ◽  
Hybrid Filtering ◽  
Content Based Filtering ◽  
Research Studies

In education, the needs of learners are different in the majority of the time, as each has specificities in terms of preferences, performance and goals. Recommendation systems have proven to be an effective way to ensure this learning personalization. Already used and tested in other areas such as e-commerce, their adaptation to the educational context has led to several research studies that have tried to find the best approaches with the best expected results. This article suggests that a hybridization of recommendation systems filtering methods can improve the quality of recommendations. An experiment was conducted to test an approach that combines content-based filtering and collaborative filtering. The results proved to be convincing.

An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms

2010 ◽  
Vol 159 ◽  
pp. 671-675 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Rating Data ◽  
Measure Methods ◽  
Collaborative Filtering Algorithm

Personalized recommendation systems combine the data mining technology with users browse profile and provide recommendation set to user forecasted by their interests. Collaborative filtering algorithm is one of the most successful methods for building personalized recommendation system, and is extensively used in many fields to date. With the development of E-commerce, the magnitudes of users and items grow rapidly, resulting in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To alleviate the problem, an enhanced Pearson correlation similarity measure method is introduced in the personalized collaborative filtering recommendation algorithm. The approach considers the common correlation rating of users. The recommendation using the enhanced similarity measure can improve the neighbors influence in the course of recommendation and enhance the accuracy and the quality of recommendation systems effectively.

scholarly journals Collaborative Filtering: Data Sparsity Challenges

2018 ◽  
Vol 6 (6) ◽  
Author(s):  
Er.Meenakshi . ◽  
Dr.Satpal .
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Effective Means ◽  
Recommendation Systems ◽  
Decision Making Process ◽  
Huge Amount ◽  
Data Sparsity ◽  
Filtering Technique ◽  
The Cost

Today internet is a place where the huge amount of data is stored, there is need to sift, which create a problem for the internet user, so recommend system solve the problem. A recommendation system is a system that helps a user found the products and content by forecast the user’s rating of each item and showing them the items that they would rate highly. Recommendation systems are everywhere. With online shopping, customer has nearly infinite choices. No one has enough time to try every product for sale. Recommendation systems play an important role to solve the users search the products and content they care about. Recommendation system is a process of filtering the information that deal with information overloaded problems. Recommendation system is important for both user and service provider. It reduces the cost of transaction and selecting item in an online scenario it also improve the quality of decision making process. It is now an effective means for selling their product. So over emphasized of user is not good for recommendation system. To solve the problems of recommendation system like data sparsity we use one of best technique that is collaborative filtering technique.

A Recommendation System Framework Based on Web Mining

2012 ◽  
Vol 151 ◽  
pp. 576-582 ◽  
Author(s):  
Zhen Jian Yang ◽  
Ke Wen Xia
Keyword(s):  
Collaborative Filtering ◽  
Web Mining ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Web Content ◽  
Research Papers ◽  
Rating Data ◽  
Web Structure ◽  
Better Than

Presently recommendation systems have gradually become an important part in E-Commerce, more and more research papers about recommendation systems in E-Commerce appeared in many kinds of conferences and journals. With expanding of E-Commerce it also faces series of challenges. Traditional collaborative filtering recommendation technique is hard to provide recommendation service for unregistered users. To overcome this problem, we suggested a framework of recommendation system based on web mining. It is made up of two parts, offline and online. This method first clustered web usage data, web content data and web structure data respectively, then provided high-quality recommendation services based on mining results. Compared with traditional collaborative filtering techniques, recommendation systems based on web mining are convenient for users because user need not to provide user-rating data explicitly. In end of this paper, accuracy of recommendation system based on web mining was tested and compared with traditional collaborative filtering recommendation system. Testing results showed that, quality of recommendation system based on web mining is better than quality of traditional collaborative filtering recommendation system.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

A New Collaborative Filtering Approach Based on Game Theory for Recommendation Systems

2021 ◽  
Author(s):  
Selma Benkessirat ◽  
Narhimene Boustia ◽  
Rezoug Nachida
Keyword(s):  
Game Theory ◽  
Collaborative Filtering ◽  
Cooperative Game ◽  
Shapley Value ◽  
Cooperative Game Theory ◽  
Recommendation Systems ◽  
Internet Users ◽  
User Communities ◽  
The One

Recommendation systems can help internet users to find interesting things that match more with their profile. With the development of the digital age, recommendation systems have become indispensable in our lives. On the one hand, most of recommendation systems of the actual generation are based on Collaborative Filtering (CF) and their effectiveness is proved in several real applications. The main objective of this paper is to improve the recommendations provided by collaborative filtering using clustering. Nevertheless, taking into account the intrinsic relationship between users can enhance the recommendations performances. On the other hand, cooperative game theory techniques such as Shapley Value, take into consideration the intrinsic relationship among users when creating communities. With that in mind, we have used SV for the creation of user communities. Indeed, our proposed algorithm preforms into two steps, the first one consists to generate communities user based on Shapley Value, all taking into account the intrinsic properties between users. It applies in the second step a classical collaborative filtering process on each community to provide the Top-N recommendation. Experimental results show that the proposed approach significantly enhances the recommendation compared to the classical collaborative filtering and k-means based collaborative filtering. The cooperative game theory contributes to the improvement of the clustering based CF process because the quality of the users communities obtained is better.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

2021 ◽  
Vol 14 (1) ◽  
pp. 387-399
Author(s):  
Noor Ifada ◽  
◽  
Richi Nayak ◽  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Approach ◽  
Tensor Model ◽  
Learning Approaches ◽  
Data Sparsity ◽  
Tensor Models ◽  
Sparsity Problem ◽  
Weighted Rank

The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.

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