A New Approach for Matrix Completion Using Arrow Relation of FCA in Recommender Systems
07 Jun 2019-pp 839-846
TL;DR: This paper explores the problem of recommendation by presenting a FCA-based methodology which uses the mathematical tool in reducing the dimensionality of the user-item rating thereby determining the rating value of the unknown user with its corresponding item.
Abstract: In this digital world, the Internet is the major source of rich amount of data where there are large numbers of choices available to the internet users. It requires the attention to scrutinize, itemize, and effectively project the essential information in order to violate the dispute of information overload. The information overload is an obstacle for many of the internet users. Recommender systems aim to provide solution to such obstacles by refining and seeking the large volume of exponentially growing information to provide the internet users with the personalized content and services. Here finding or predicting the user-item rating with the sparse data available in the rating matrix of the input data becomes a challenging task. This paper explores the problem of recommendation by presenting a FCA-based methodology which uses the mathematical tool in reducing the dimensionality of the user-item rating thereby determining the rating value of the unknown user with its corresponding item. The proposed method is simple and faster explained with an illustration to complete the user-item rating matrix from which the rating can be predicted by applying any of the recommendation algorithms.
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TL;DR: A new approach based on the mathematical model, Formal Concept Analysis (FCA) is used to improve the rating prediction of the unknown users which can certainly overcome the issues of the existing approaches like data sparsity, high dimensionality of data, performance of the recommendation generated for top n recommendation.
Abstract: The recommender systems are used to mainly suggest recommendations to the online users by utilizing the user preferences recorded during the item purchase. No matter how, the performance of the recommendation quality seems to be inevitable and far satisfactory. In this paper, a new approach based on the mathematical model, Formal Concept Analysis (FCA) is used to improve the rating prediction of the unknown users which can certainly overcome the issues of the existing approaches like data sparsity, high dimensionality of data, performance of the recommendation generated for top n recommendation. The FCA method is applied using Boolean Matrix Factorization (ie. optimal formal concepts) in predicting the rating of the unknown users in the available user-item interaction matrix which proves to be more efficientin tackling the problem of computational complexity managing the high dimensionality of data. The proposed method is applied using item-based collaborative filtering technique and the experiment is conducted on the Movielens dataset which shows the satisfactory results. The experiments results are evaluated using the related error metrics and performance metrics. The experimental results are also compared with existing item-based Collaborative Filtering techniques which demonstrate that the performance of recommendation quality gradually improved with state-of-the-art existing techniques.
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Cites methods or result from "A New Approach for Matrix Completio..."
...The main idea in choosing the BMF technique for the recommender systems is to solve the challenge of addressing the binary-rated data which is so called as implicit feedback [24]....
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...To the fact that the experimental results shown in figure 7, illustrates that the error metrics measured using Root Mean Square Error (RMSE) [24] and Mean Absolute Eror (MAE) [25] is satisfactory which can enhance the quality of recommendation as expected....
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References
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TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Abstract: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels
9,583 citations
Book•
04 Dec 1998
TL;DR: This is the first textbook on formal concept analysis that gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially in data analysis and knowledge processing.
Abstract: From the Publisher:
This is the first textbook on formal concept analysis. It gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially in data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. Theory and graphical representation are thus closely coupled together. The mathematical foundations are treated thoroughly and illuminated by means of numerous examples.
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TL;DR: Restructuring lattice theory is an attempt to reinvigorate connections with the authors' general culture by interpreting the theory as concretely as possible, and in this way to promote better communication between lattice theorists and potential users of lattices theory.
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TL;DR: The different characteristics and potentials of different prediction techniques in recommendation systems are explored in order to serve as a compass for research and practice in the field of recommendation systems.
861 citations
TL;DR: The theory of rough sets and formal concept analysis are compared in a common framework based on formal contexts to produce different types of rules summarizing knowledge embedded in data.
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318 citations