scholarly journals Latent based temporal optimization approach for improving the performance of collaborative filtering

2020 ◽  
Vol 6 ◽  
pp. e331
Author(s):  
Ismail Ahmed Al-Qasem Al-Hadi ◽  
Nurfadhlina Mohd Sharef ◽  
Md Nasir Sulaiman ◽  
Norwati Mustapha ◽  
Mehrbakhsh Nilashi
Keyword(s):  
Collaborative Filtering ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Optimization Approach ◽  
The Past ◽  
Temporal Representation ◽  
Feedback Learning ◽  
Temporal Factors ◽  
Sparsity Problem ◽  
Over Time

Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.

scholarly journals Enhancing Collaborative Filtering by User-User Covariance Matrix

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yingyuan Xiao ◽  
Jingjing Shi ◽  
Wenguang Zheng ◽  
Hongya Wang ◽  
Ching-Hsien Hsu
Keyword(s):  
Collaborative Filtering ◽  
Covariance Matrix ◽  
Personalized Recommendation ◽  
Similarity Matrix ◽  
User Similarity ◽  
Filtering Method ◽  
The Past ◽  
Changing Trends ◽  
Over Time

The collaborative filtering (CF) approach is one of the most successful personalized recommendation methods so far, which is employed by the majority of personalized recommender systems to predict users’ preferences or interests. The basic idea of CF is that if users had the same interests in the past they will also have similar tastes in the future. In general, the traditional CF may suffer the following problems: (1) The recommendation quality of CF based system is greatly affected by the sparsity of data. (2) The traditional CF is relatively difficult to adapt the situation that users’ preferences always change over time. (3) CF based approaches are used to recommend similar items to a user ignoring the user’s demand for variety. In this paper, to solve the above problems we build a new user-user covariance matrix to replace the traditional CF’s user-user similarity matrix. Compared with the user-user similarity matrix, the user-user covariance matrix introduces the user-user covariance to finely describe the changing trends of users’ interests. Furthermore, we propose an enhancing collaborative filtering method based on the user-user covariance matrix. The experimental results show that the proposed method can significantly improve the diversity of recommendation results and ensure the good recommendation precision.

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.

scholarly journals Qos Web Service Design Based on Collaborative Filtering

CONVERTER ◽  
2021 ◽  
pp. 107-115
Author(s):  
Yu-ping LI, Ke LI, Zhan-jie Guo
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Service Recommendation ◽  
Recommendation Algorithm ◽  
Similar Weight ◽  
Definition Of ◽  
Collaborative Prediction

In the process of web service recommendation, the prediction accuracy of Web Service missing Quality of Service (QoS) value will have an important impact on the rationality of service recommendation. Therefore, combined with spatiotemporal similarity perception, this paper proposes a new web service QoS collaborative filtering recommendation algorithm. This paper designs the framework of web service recommendation system from the perspective of QoS collaborative prediction, and gives the definition of related parameter set. Aiming at the problem that some services in the traditional Top-k algorithm are not similar to the target services, the spatial-temporal similarity perception combined with similar weight is used to predict the missing data to improve the prediction accuracy. In this paper, the calculation process of the algorithm is given through a simple example. The effectiveness of the algorithm is verified by the experimental results.

Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

2020 ◽  
Vol 19 (02) ◽  
pp. 385-412 ◽  
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Least Square ◽  
Data Sparsity ◽  
Filtering Method ◽  
Sparsity Problem ◽  
Hybrid Collaborative Filtering ◽  
User Ratings ◽  
Hybrid Recommendation

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

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?’

Using the beta distribution technique to detect attacked items from collaborative filtering

2021 ◽  
Vol 25 (1) ◽  
pp. 121-137
Author(s):  
Ping-Yu Hsu ◽  
Jui-Yi Chung ◽  
Yu-Chin Liu
Keyword(s):  
Collaborative Filtering ◽  
Beta Distribution ◽  
Detection Rate ◽  
Recommendation System ◽  
Target Item ◽  
Multiple Target ◽  
The Past ◽  
User Data

A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system will be injected with false data to create false ratings to push or nuke specific items. This will affect the user’s trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%.

scholarly journals Recommendation System Using Autoencoders

2020 ◽  
Vol 10 (16) ◽  
pp. 5510 ◽  
Author(s):  
Diana Ferreira ◽  
Sofia Silva ◽  
António Abelha ◽  
José Machado
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Mean Squared Error ◽  
Singular Value ◽  
High Dimensions ◽  
Data Sparsity ◽  
Filtering Method ◽  
Squared Error ◽  
Sparsity Problem ◽  
Value Decomposition

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.

scholarly journals Employing opposite ratings users in a new approach to collaborative filtering

2022 ◽  
Vol 25 (1) ◽  
pp. 450
Author(s):  
Abdellah El Fazziki ◽  
Yasser El Madani El Alami ◽  
Jalil Elhassouni ◽  
Ouafae El Aissaoui ◽  
Mohammed Benbrahim
Keyword(s):  
Collaborative Filtering ◽  
Basic Assumption ◽  
Recommendation System ◽  
Experimental Results ◽  
The Other ◽  
Unusual Behavior ◽  
New Approach ◽  
The Real ◽  
The Past ◽  
The One

Over the past few decades, various recommendation system paradigms have been developed for both research and industrial purposes to satisfy the needs and preferences of users when they deal with enormous data. The collaborative filtering (CF) is one of the most popular recommendation techniques, although it is still immature and suffers from some difficulties such asparsity, gray sheep and scalability impeding recommendation quality. Therefore, we propose a new CF approach to deal with the gray sheep problem in order to improve the predictions accuracy. To realize this goal, our solution aims to infer new users from real ones existing in datasets. This transformation allows for creating users with opposite preferences to the real ones. On the one hand, our approach permits to amplify the number of neighbors, especially in the case of users who have unusual behavior (gray sheep). On the other hand, it facilitates building a dense similar neighborhood. The basic assumption behind this is that if user X is not similar to user Y, then the imaginary user ¬X is similar to the user Y. The performance of our approach was evaluated using two datasets, MovieLens and FilmTrust. Experimental results have shown that our approach surpasses many traditional recommendation approaches.

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