Clustering-based recommender system using principles of voting theory

Das, Joydeep; Mukherjee, Partha; Majumder, Subhashis; Gupta, Prosenjit

doi:10.1109/ic3i.2014.7019655

Cited by 43 publications

(19 citation statements)

References 9 publications

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“…where Φ ∈ R n×n ≥ 0 and Ψ ∈ R c×n ≥ 0 are the Lagrangian multiplier matrices and O(V, S) denotes the objective function in Eq. (5). Following the KKT conditions, the optimal solution of Eq.…”

Section: B Optimization Methodsmentioning

confidence: 99%

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Clustering-Aware Graph Construction: A Joint Learning Perspective

Jia

Liu

Hou

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering accuracy. To this end, we propose a joint learning framework, which is able to learn the graph and the clustering result simultaneously, such that the resulting graph is tailored to the clustering task. The proposed model is formulated as a well-defined nonnegative and off-diagonal constrained optimization problem, which is further efficiently solved with convergence theoretically guaranteed. The advantage of the proposed model is demonstrated by comparing with 19 state-of-the-art clustering methods on 10 datasets with 4 clustering metrics. ). 2 2. The proposed optimization method has the following theoretical guarantees: i), the constraints in the proposed model can be naturally satisfied 2 in the optimization process; ii), each optimization step can decrease the value of the objective function; and iii), the converged limit point is a stationary point that satisfies the Karush-Kuhn-Tucker (KKT) conditions.The rest of this paper is organized as follows. In section II, we discuss the related works. Section III presents the proposed model, the optimization method, its computational complexity analysis and its theoretical guarantees. Experimental comparisons and analyses are shown in section IV, and finally section V concludes this paper. II. RELATED WORK A. NotationThroughout this paper, matrices are denoted by boldface uppercase letters, e.g., A, and the element at the ith row and jth column of a matrix is denoted as A ij or a ij . Vectors are represented by boldface lowercase letters, e.g., a and scales are represented by italic lowercase letters, e.g., a. Moreover, T stands for the transpose of a matrix, A F = i j A 2 ij is the Frobenius norm of matrix A, A ∞ = max ij |A ij | returns the maximum absolute value of matrix A, diag(·) returns the diagonal elements of a matrix as a vector, ⊙ returns the Hadamard product of two matrices, i.e., the element-wise multiplication of two matrices, exp(·) returns the exponential value, ·, · calculates the inner product of two matrices, I k denotes an identity matrix of size k × k, and A ≥ 0 means each element of A is greater than or equal to 0, i,e., A ij ≥ 0, ∀i, j. X = {x 1 , x 2 , . . . , x n } ∈ R d×n denotes the input data, x i ∈ R d×1 is the ith sample, n, d, and c represent the number of samples, the dimension of features, and the number of classes, respectively, B. Graph-based ClusteringDifferent from traditional clustering methods (like K-means) that partition the raw features X straightforwardly, graph clustering [15], [13] transforms data clustering as a graph partition problem. Specifically, a typical graph clustering method is composed of the following steps:

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Section: B Optimization Methodsmentioning

confidence: 99%

“…where A = (1 + β)I + α X T X + , B = α X T X − , C = 2VV T + 2βW + 2α X T X + , and D = 2B = 2α X T X − . 5 non-increasing, to be precise.…”

Section: A Proof Of Theorem 1-1mentioning

confidence: 99%

Clustering-Aware Graph Construction: A Joint Learning Perspective

Jia

Liu

Hou

2020

IEEE Trans. on Signal and Inf. Process. over Networks

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“…Clustering-based methods [2,23,[27][28][29][30][31][32][33] have been proposed to address the scalability issue. Users or/and items are clustered into groups, and thus the numbers of users or/and items are reduced.…”

Section: Related Workmentioning

confidence: 99%

“…However, this method tends to recommend tail items as a whole to users. Das et al [30] use the DBSCAN clustering algorithm for clustering the users, and then implement voting algorithms to recommend items to the user depending on the cluster into which it belongs. The idea is to partition the users and apply the recommendation algorithm separately to each partition.…”

Section: Related Workmentioning

confidence: 99%

Leveraging User Comments for Recommendation in E-Commerce

et al. 2020

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Collaborative filtering recommender systems traditionally recommend products to users solely based on the given user-item rating matrix. Two main issues, data sparsity and scalability, have long been concerns. In our previous work, an approach was proposed to address the scalability issue by clustering the products using the content of the user-item rating matrix. However, it still suffers from these concerns. In this paper, we improve the approach by employing user comments to address the issues of data sparsity and scalability. Word2Vec is applied to produce item vectors, one item vector for each product, from the comments made by users on their previously bought goods. Through the user-item rating matrix, the user vectors of all the customers are produced. By clustering, products and users are partitioned into item groups and user groups, respectively. Based on these groups, recommendations to a user can be made. Experimental results show that both the inaccuracy caused by a sparse user-item rating matrix and the inefficiency due to an enormous amount of data can be much alleviated.

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“…To deal with the sparsity and scalability problems, that CF techniques suffer from, some works propose a RS based on data clustering. In this context, Das et al [6] use DBSCAN clustering algorithm for clustering users according to their preferences. To recommend items of interest for a new user, authors use different voting systems as algorithms to combine opinions from multiple users within his cluster.…”

Section: U |−1rmentioning

confidence: 99%

Like-tasted user groups to predict ratings in recommender systems

Jaffali

Jamoussi

Smaïli

et al. 2020

Soc. Netw. Anal. Min.

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Recommendation Systems have gained the intention of many researchers due to the growth of the business of personalizing, sorting and suggesting products to customers. Most of rating prediction in recommendation systems are based on customer preferences or on the historical behavior of similar customers. The similarity between customers is generally measured by the number of times customers liked or disliked the same item. Given the huge number and the variety of items, many customers cannot be considered as similar, as they did not evaluate the same items, even if they have similar tastes. This paper presents a new method of rating prediction in recommendation systems. The proposed method starts by identifying the taste directions or the interest centers based on the users' demographic information combined with their previous evaluations. Thus, it uses the Principal Component Analysis (PCA) to retrieve the major taste orientations. According to these orientations, user groups are created. Then, for each group, it generates a prediction model, that will be used to predict unknown rates of users within the corresponding group. In order to assess the accuracy of the proposed method, we compare its

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Clustering-based recommender system using principles of voting theory

Cited by 43 publications

References 9 publications

Clustering-Aware Graph Construction: A Joint Learning Perspective

Clustering-Aware Graph Construction: A Joint Learning Perspective

Leveraging User Comments for Recommendation in E-Commerce

Like-tasted user groups to predict ratings in recommender systems

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