Constructing a Hybrid Recommender System

Ignat’ev, V. Yu.; Lemtyuzhnikova, D. V.; Rul, D.; Ryabov, I. L.

doi:10.1134/s1064230718060060

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…To know the error of the prediction, we generalize the calculation for existing vote values (Table 2) and apply the mean absolute error measure MAE (see section 4.4). The MAE measure makes it possible to differentiate between the rating value given by the users and the prediction value generated by the formula (6).…”

Section: Item Link Predictionmentioning

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: Item Link Predictionmentioning

confidence: 99%

“…However, finding the nearest neighbours is the critical phase of the collaborative approach. In addition, providing high-quality recommendations to users with a minimum of common feedback is a major challenge for recommendation systems [5,6]. Currently, several in-depth studies are focused on modelling using bipartite networks [7,8].…”

Section: Introductionmentioning

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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“…Broadly speaking, the system makes recommendations based on context [4], content [5], social environment [6], demographic data [7] and collaborative information, the latter being the most widely used approach [8]. Hybrid methods [9], which combine Collaborative Filtering (CF) and other filtering sources, are commonly used in commercial RS designs.…”

Section: Introductionmentioning

confidence: 99%

Dirichlet Matrix Factorization: A Reliable Classification-Based Recommender System

et al. 2022

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Traditionally, recommender systems have been approached as regression models aiming to predict the score that a user would give to a particular item. In this work, we propose a recommender system that tackles the problem as a classification task instead of as a regression. The new model, Dirichlet Matrix Factorization (DirMF), provides not only a prediction but also its reliability, hence achieving a better balance between the quality and quantity of the predictions (i.e., reducing the prediction error by limiting the model’s coverage). The experimental results conducted show that the proposed model outperforms other models due to its ability to discard unreliable predictions. Compared to our previous model, which uses the same classification approach, DirMF shows a similar efficiency, outperforming the former on some of the datasets included in the experimental setup.

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