Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem

Abdelwahab, Amira; Sekiya, Hiroo; Matsuba, Ikuo; Horiuchi, Yasuo; Kuroiwa, Shingo

doi:10.1145/1806338.1806406

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…In what follows, some of the previous research literatures related to the techniques used in userbased collaborative filtering is presented with employing different data sets. The related works are shown in Table ( 1).…”

Section: Related Workmentioning

confidence: 99%

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A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

al.¹

2019

Baghdad Sci.J

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Recommender Systems are tools to understand the huge amount of data available in the internet world. Collaborative filtering (CF) is one of the most knowledge discovery methods used positively in recommendation system. Memory collaborative filtering emphasizes on using facts about present users to predict new things for the target user. Similarity measures are the core operations in collaborative filtering and the prediction accuracy is mostly dependent on similarity calculations. In this study, a combination of weighted parameters and traditional similarity measures are conducted to calculate relationship among users over Movie Lens data set rating matrix. The advantages and disadvantages of each measure are spotted. From the study, a new measure is proposed from the combination of measures to cope with the global meaning of data set ratings. After conducting the experimental results, it is shown that the proposed measure achieves major objectives that maximize the accuracy Predictions.

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Section: Related Workmentioning

confidence: 99%

“…Similarity measure computation depends mostly on user's explicit ratings (users scan items and rate them on a rating scale values). Although explicit rating captures user favorites to items perfectly, its main drawback is sparsity problem due to the vast amount of information in the world (1).…”

Section: Introductionmentioning

confidence: 99%

A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

al.¹

2019

Baghdad Sci.J

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“…To the best of our knowledge, few works focus on the convergency of iteration and the optimized predicting order. Models proposed in [1,19], which just repeat basic cluster-based or neighbor-based model simply ignore the optimization and costs more time to carry on the iteration. In this paper, we propose an optimized iterative collaborative filtering model to extend the idea of hybrid and iteration, which could raise both the accuracy and performance significantly.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the data set we used in this paper is from WS-DREAM [18,22]. 19 Two features of the QoS value collected in the data set are response-time and throughput, both of which contain a user-item matrix(339* 5825). To evaluate the prediction accuracy and performance of different models, 10 user-item matrices(150*200) without missing value are selected from the original data set to serve as the real world QoS data.…”

Section: A Experimental Setupmentioning

confidence: 99%

Web Service Recommendation using Optimized Iterative Collaborative Filtering

Wang¹,

Guo²,

Zhou³

et al. 2014

Proceedings of the 3rd International Conference on Computer Science and Service System

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Abstract-With the explosive growth of web services on the World Wide Web, service recommendation is becoming extremely important to both the service providers and the active users. In this paper, we propose a web service recommendation model which utilizes the prediction of Quality-of-Services (QoS) based on collaborative filtering with optimized iteration. The benefit of employing iteration in collaborative filtering is that the prediction accuracy of QoS values can be raised significantly via recursive refinement. Since such iteration scheme will hinder training performance, a novel optimization strategy is introduced based on the predicting tree. Finally, the optimized model is implemented and deployed to conduct the experiments on a real-world data set, which contains 1.5 million web services invocation information. The experimental results show that our model has achieved better prediction accuracy than other models with similar performance.

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“…These *Corresponding Author. www.ijacsa.thesai.org both techniques are themselves interrelated to each other in the context of mean deduction, but dissimilar when it comes to the measure of coefficients [4].…”

Section: Introductionmentioning

confidence: 99%

A Proposed Method to Solve Cold Start Problem using Fuzzy user-based Clustering

Duja¹,

Niu²,

Ahmed³

et al. 2020

IJACSA

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With the elevation of the online accessibility to almost everything, many logics, systems and algorithms have to be revised to match the pace of the trends among the socialized networks. One such system; recommendation system has become very important as far as the socialized networks are concerned. In such paced and vibrant environment of the online accessibility and availability to heavy and large amount of data uploaded to the internet such as, movies, books, research articles and much more. The method of recommendation where provides the socialized networks between the operators, at the same instance, it provides references for the users to asses other users that effects their socialized relation directly or indirectly. Collaborative filtering is the technique used for recommending the same taste of picks to that of the user, and it is accomplished by the user's mutual collaboration, this technique is mostly used by the social networking sites. Nowadays this technique is not only popular but common for recommending the data to the user; meanwhile it also motivates the researchers to find the more effective system and algorithm so that the user's satisfaction can be achieved by recommending them the data according to their search history. This paper suggests the CF (Collaborative Filtering) model that is based on the user's truthful information applied by the FCM (Fuzzy C-means) clustering. This study proposes that the fuzzy truthful information of the user is to be combined with rating of the content by other users to produce a recommender system formula with a coupled coefficient with new parameters. To achieve the results the Data set of Movie Lens is included in the study which shows significant improvement in the recommendation subjected to the condition of cold start.

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Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem

Cited by 10 publications

References 12 publications

A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

A Study on the Accuracy of Prediction in Recommendation System Based on Similarity Measures

Web Service Recommendation using Optimized Iterative Collaborative Filtering

A Proposed Method to Solve Cold Start Problem using Fuzzy user-based Clustering

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