scholarly journals AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 427 ◽  
Author(s):  
Zahir ◽  
Yuan ◽  
Moniz
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Computational Cost ◽  
Recommendation Systems ◽  
Similarity Measures ◽  
Extraction Methods ◽  
Common Interest ◽  
Trust Relationship ◽  
Public Data ◽  
Implicit Trust

Recommendation systems alleviate the problem of information overload by helping users find information relevant to their preference. Memory-based recommender systems use correlation-based similarity to measure the common interest among users. The trust between users is often used to address the issues associated with correlation-based similarity measures. However, in most applications, the trust relationships between users are not available. A popular method to extract the implicit trust relationship between users employs prediction accuracy. This method has several problems such as high computational cost and data sparsity. In this paper, addressing the problems associated with prediction accuracy-based trust extraction methods, we proposed a novel trust-based method called AgreeRelTrust. Unlike accuracy-based methods, this method does not require the calculation of initial prediction and the trust relationship is more meaningful. The collective agreements between any two users and their relative activities are fused to obtain the trust relationship. To evaluate the usefulness of our method, we applied it to three public data sets and compared the prediction accuracy with well-known collaborative filtering methods. The experimental results show our method has large improvements over the other methods.

scholarly journals Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering

2019 ◽  
Vol 9 (20) ◽  
pp. 4378 ◽  
Author(s):  
Yuan ◽  
Zahir ◽  
Yang
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Prediction Accuracy ◽  
State Of The Art ◽  
Side Information ◽  
Initial Trust ◽  
The Social ◽  
Implicit Trust ◽  
Value Decomposition ◽  
Better Than

Recommendation systems often use side information to both alleviate problems, such as the cold start problem and data sparsity, and increase prediction accuracy. One such piece of side information, which has been widely investigated in addressing such challenges, is trust. However, the difficulty in obtaining explicit relationship data has led researchers to infer trust values from other means such as the user-to-item relationship. This paper proposes a model to improve prediction accuracy by applying the trust relationship between the user and item ratings. Two approaches to implement trust into prediction are proposed: one involves the use of estimated trust, and the other involves the initial trust. The efficiency of the proposed method is verified by comparing the obtained results with four well-known methods, including the state-of-the-art deep learning-based method of neural graph collaborative filtering (NGCF). The experimental results demonstrate that the proposed method performs significantly better than the NGCF, and the three other matrix factorization methods, namely, the singular value decomposition (SVD), SVD++, and the social matrix factorization (SocialMF).

scholarly journals Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1566 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Debapriya Hazra ◽  
Sejoon Park ◽  
Yung Cheol Byun
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Collaborative Filtering ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Gradient Boosting ◽  
Mean Square ◽  
Extreme Gradient Boosting

With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.

Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review

2017 ◽  
Vol 44 (3) ◽  
pp. 331-344 ◽  
Author(s):  
Youdong Yun ◽  
Danial Hooshyar ◽  
Jaechoon Jo ◽  
Heuiseok Lim
Keyword(s):  
Collaborative Filtering ◽  
Quantitative Data ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Opinion Mining ◽  
Recommendation Systems ◽  
Product Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Product Rating ◽  
Positive Recommendation

The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users’ actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: ‘Why is collaborative filtering algorithm not effective?’ and ‘Do quantitative data such as product rating or purchase history reflect users’ actual tastes?’

scholarly journals EBCR: Empirical Bayes concordance ratio method to improve similarity measurement in memory-based collaborative filtering

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255929
Author(s):  
Yu Du ◽  
Nicolas Sutton-Charani ◽  
Sylvie Ranwez ◽  
Vincent Ranwez
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Empirical Bayes ◽  
Similarity Measures ◽  
Ratio Method ◽  
Bayes Inference ◽  
Benchmark Datasets ◽  
Large Product ◽  
Accuracy Performance ◽  
Selection Of

Recommender systems aim to provide users with a selection of items, based on predicting their preferences for items they have not yet rated, thus helping them filter out irrelevant ones from a large product catalogue. Collaborative filtering is a widely used mechanism to predict a particular user’s interest in a given item, based on feedback from neighbour users with similar tastes. The way the user’s neighbourhood is identified has a significant impact on prediction accuracy. Most methods estimate user proximity from ratings they assigned to co-rated items, regardless of their number. This paper introduces a similarity adjustment taking into account the number of co-ratings. The proposed method is based on a concordance ratio representing the probability that two users share the same taste for a new item. The probabilities are further adjusted by using the Empirical Bayes inference method before being used to weight similarities. The proposed approach improves existing similarity measures without increasing time complexity and the adjustment can be combined with all existing similarity measures. Experiments conducted on benchmark datasets confirmed that the proposed method systematically improved the recommender system’s prediction accuracy performance for all considered similarity measures.

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 Collaborative Filtering Recommendation Algorithm Incorporated with Life Cycle

2013 ◽  
Vol 765-767 ◽  
pp. 630-633 ◽  
Author(s):  
Chong Lin Zheng ◽  
Kuang Rong Hao ◽  
Yong Sheng Ding
Keyword(s):  
Life Cycle ◽  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Experimental Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Time Information ◽  
Successful Technology

Collaborative filtering recommendation algorithm is the most successful technology for recommendation systems. However, traditional collaborative filtering recommendation algorithm does not consider the change of time information. For this problem,this paper improve the algorithm with two new methods:Predict score incorporated with time information in order to reflect the user interest change; Recommend according to scores by adding the weight information determined by the item life cycle. Experimental results show that the proposed algorithm outperforms the traditional item in accuracy.

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 Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

2020 ◽  
Vol 10 (04) ◽  
pp. 37-56
Author(s):  
Tzu-Yu Yeh ◽  
Rasha Kashef
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Systems

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.

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