Recommender System with Formal Concept Analysis

Zhang, Wei; Du, Ya Jun; Song, Wen

doi:10.1049/cp.2015.0243

Cited by 2 publications

(5 citation statements)

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“…TFCC algorithm was compared with the work APG (Zhang et al , 2015) which used the statistical properties of Gibbs Sampling for solving the matrix completion problem. Though the APG was able to reconstruct the original matrix, it was suitable when experimented with the extremely large and sparse data matrix.…”

Section: Resultsmentioning

confidence: 99%

“…Zhang et al (2015) have proposed an accelerated proximal gradient algorithm (APG), which aims at solving large-scale randomly-generated matrix with ε -optimal solution. This APG algorithm, with a fast method called PROPACK, was used to compute the matrix decomposition which aims at solving the matrix completion problems with low-rank solution matrix.…”

Section: Related Workmentioning

confidence: 99%

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Three-way formal concept clustering technique for matrix completion in recommender system

Selvi

Priya

2020

IJPCC

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Purpose In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information. Design/methodology/approach To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix. Findings The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score. Social implications The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products. Originality/value This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Three-way formal concept clustering technique for matrix completion in recommender system

Selvi

Priya

2020

IJPCC

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“…The work proposed by Nobahari [45] enhances recommendations accuracy and obtains users, directors, and producers' satisfaction by combining synchronously user-item ratings based on trust, sequential interest, and user implicit interest. Also, the research in [46] illustrates how user's profile characteristics and social relationships effectively improve the recommendations' performance when constructing a user interest network. The technique proposed in [47] applies various interactive factors and tourists' relationships, such as their desires and interests, trust, reputation, affinities, and social community, to calculate the similarity and provide appropriate recommendations.…”

Section: A Social-based Recommender Systemmentioning

confidence: 99%

“…[61] Semantic similaritybased [66], [68], [102] Similarity-based method [37], [41], [46], [47], [50], [51], [63], [69], [74], [78], [79], [101], [105], [116] Tensor factorization [31], [36], [94], [95], [110] Many researchers categorize approaches in recommender systems into at least three main approaches [8], [13], [14], [15] as presented in Section II. Following this tendency, in this research, we consider four main approaches for recommender systems, namely 2 : collaborative filtering, content-based filtering, graph-based filtering (GB), and hybrid-based filtering (HB).…”

Section: Regression-basedmentioning

confidence: 99%

“…The recommendation similarity for each user is calculated using graph entropy of all time intervals resulted in graphs. Randomly connected graph techniques apply to build a concept context graph that assigns a proper priority for each of the visited candidate users are proposed in [46] to determine the recommended candidate users' ranking. Using a randomwalk technique, a relevance propagation technique that catches the overall user preference on music is presented in [57] to recommend appropriate music for users based on their emotional status.…”

Section: ) Graph-based Approachmentioning

confidence: 99%

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Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Suhaim

Berri

2021

IEEE Access

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Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.

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Recommender System with Formal Concept Analysis

Cited by 2 publications

References 0 publications

Three-way formal concept clustering technique for matrix completion in recommender system

Three-way formal concept clustering technique for matrix completion in recommender system

Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

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