Decomposition in Multidimensional Boolean-Optimization Problems with Sparse Matrices

Kovkov, D. V.; Lemtyuzhnikova, D. V.

doi:10.1134/s1064230718010033

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…The assumption is that users with similar ratings would have similar tastes in the future. These approaches are divided into two categories: memory-collaborative filtering (memory CF) and model-collaborative filtering (model CF) [16]. Memory CF uses the set of rating matrix scores to make predictions about the active user.…”

Section: Traditional Approachesmentioning

confidence: 99%

“…CARS can recommend a restaurant or hotel to a tourist based on previous choices of its similar neighbours by context. In addition, it is possible to use the context [20] in collaborative filtering in the absence of the ratings and overcome the problem of the sparse matrix [16]. Thus, several authors [21] use the ontologies and semantic web layers [22] to improve the performance and accuracy of the recommendation.…”

Section: Context-aware and Semantic Approachesmentioning

confidence: 99%

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Improving collaborative recommendation based on item weight link prediction

KHARROUBI,

DAHMANI,

NOUALI

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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There is a continuous information overload on the Web. The problem treated is how to have relevant items (documents, products, services, etc.) at time and without difficulty. Filtering system also called recommender systems are widely used to recommend items to users by similarity process such as Amazon, MovieLens, Cdnow, etc. In the literature, to predict a link in a bipartite network, most methods are based either on a binary history (like, dislike) or on the common neighbourhood of the active user. In this paper, we modelled the recommender system by a weighted bipartite network. The bipartite topology offers a bidirectional reasoning item side and user side, which preserve the information shared between the nodes. To make such a prediction, we seek to determine the shares of items shared between users. In the first step, we accumulate the shares of the users towards the items and in the second step, the shares of the items towards the users. The idea is to exploit the item-user connectivity to predict non-existent links based on existing links. Therefore, the information is propagated linearly and without loss between nodes in the network. We focused on the item through a dual projection that combines the sharing of quotas between users and items. Empirical tests on real data sets (Amazon-Books, MovieLens 1M, Yelp2018, Yahoo! Songs) showed satisfactory results.

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Section: Traditional Approachesmentioning

confidence: 99%

Section: Context-aware and Semantic Approachesmentioning

confidence: 99%

Improving collaborative recommendation based on item weight link prediction

KHARROUBI,

DAHMANI,

NOUALI

2022

Turkish Journal of Electrical Engineering and Computer Sciences

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“…The entropy of most of the blocks of the solution is 0, as they contain constant values. However, in addition to clustering, the task can be solved in many other ways, see [ 4 , 5 , 6 ]. A natural generalization of this decomposition task is when the matrix contains not binary but real values; then, the decomposition gives a nice visualization of the clusters, as at the solution the sample variance of the blocks of the decomposition is minimal.…”

Section: Introductionmentioning

confidence: 99%

Decompose Boolean Matrices with Correlation Clustering

Aszalós

2021

Entropy

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One of the tasks of data science is the decomposition of large matrices in order to understand their structures. A special case of this is when we decompose relations, i.e., logical matrices. In this paper, we present a method based on the similarity of rows and columns, which uses correlation clustering to cluster the rows and columns of the matrix, facilitating the visualization of the relation by rearranging the rows and columns. In this article, we compare our method with Gunther Schmidt’s problems and solutions. Our method produces the original solutions by selecting its parameters from a small set. However, with other parameters, it provides solutions with even lower entropy.

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