Transforming collaborative filtering into supervised learning

Braida, Filipe; Mello, Carlos E.; Pasinato, Marden B.; Zimbrão, Geraldo

doi:10.1016/j.eswa.2015.01.023

Cited by 27 publications

(15 citation statements)

References 9 publications

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“…This approach compares a target user's profile with the historical profiles of other users to find the top K users who have similar tastes or interests [26]. Other common forms of solving the CF problem include MF (with many variants [22][23][24]27]) and ML [11,12,28].…”

Section: Background 21 Recommending Systemsmentioning

confidence: 99%

“…There are three main sets of techniques for building personalized RSs: ontologybased [4,5], filtering by matrix factorization (MF) [6][7][8][9][10], and machine learning (ML) [11,12]. The common premise for all of them is trying to predict new items that match the users' preferences, revealed through their past purchases or by means of explicit ratings.…”

Section: Introductionmentioning

confidence: 99%

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Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems

Ahmed

Fernández‐Veiga

Gawich

2022

Sensors

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Machine learning (ML) and especially deep learning (DL) with neural networks have demonstrated an amazing success in all sorts of AI problems, from computer vision to game playing, from natural language processing to speech and image recognition. In many ways, the approach of ML toward solving a class of problems is fundamentally different than the one followed in classical engineering, or with ontologies. While the latter rely on detailed domain knowledge and almost exhaustive search by means of static inference rules, ML adopts the view of collecting large datasets and processes this massive information through a generic learning algorithm that builds up tentative solutions. Combining the capabilities of ontology-based recommendation and ML-based techniques in a hybrid system is thus a natural and promising method to enhance semantic knowledge with statistical models. This merge could alleviate the burden of creating large, narrowly focused ontologies for complicated domains, by using probabilistic or generative models to enhance the predictions without attempting to provide a semantic support for them. In this paper, we present a novel hybrid recommendation system that blends a single architecture of classical knowledge-driven recommendations arising from a tailored ontology with recommendations generated by a data-driven approach, specifically with classifiers and a neural collaborative filtering. We show that bringing together these knowledge-driven and data-driven worlds provides some measurable improvement, enabling the transfer of semantic information to ML and, in the opposite direction, statistical knowledge to the ontology. Moreover, the novel proposed system enables the extraction of the reasoning recommendation results after updating the standard ontology with the new products and user behaviors, thus capturing the dynamic behavior of the environment of our interest.

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Section: Background 21 Recommending Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems

Ahmed

Fernández‐Veiga

Gawich

2022

Sensors

View full text Add to dashboard Cite

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“…This method is based on a scoring matrix, extracts the user feature vector and the item feature vector from the two perspectives of the user and the item, and then concatenates the user and item feature vectors. The concatenated vector represents the user-item pair feature representation (Feature Representation), all users-the film became the input feature space of the supervised learning algorithm for Zhang [15], and finally used the existing score value of the score matrix as the sample label, and input it into the regression prediction model for training and prediction. The combination of hybrid supervised learning and CF recommendation algorithm changes the form of the traditional CF recommendation algorithm using the scoring matrix through data conversion, and uses Zhang Cheng's input feature space for regression prediction, which can solve the data sparsity problem of the scoring matrix to a certain extent.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

Duan¹,

Wang²,

Han³

2021

KSII TIIS

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A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the coldstart problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

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“…In Polatidis and Georgiadis [29], a multi-level recommendation method was proposed to assist users in making decisions. Moreover, some techniques in other domains, such as cluster [30–32], support vector machine (SVM) [33,34] and neural networks [35,36], have been introduced to work with CF to enhance the comprehensive performance of recommendation systems.…”

Section: Basic Concepts and Related Workmentioning

confidence: 99%

A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse data

Deng

Wang

Guo

et al. 2018

Journal of Information Science

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In the neighbourhood-based collaborative filtering (CF) algorithms, a user similarity measure is used to find other users similar to an active user. Most of the existing user similarity measures rely on the co-rated items. However, there are not enough co-rated items in sparse dataset, which usually leads to poor prediction. In this article, a new similarity scheme is proposed, which breaks free of the constraint of the co-rated items. Moreover, an item similarity measure based on the Kullback–Leibler (KL) divergence is presented, which identifies the relation between items based on the probability density distribution of ratings. Since the item similarity based on KL divergence makes full use of all ratings, it owns better flexibility for sparse datasets. The CF algorithm using our proposed similarity scheme is implemented and compared with some classic CF algorithms. The compared results show that the CF using our similarity has better predictive performance. Therefore, our similarity scheme is a good solution for the sparsity problem and has great potential to be applied to recommendation systems.

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Transforming collaborative filtering into supervised learning

Cited by 27 publications

References 9 publications

Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems

Neural Collaborative Filtering with Ontologies for Integrated Recommendation Systems

A Hybrid Recommendation System based on Fuzzy C-Means Clustering and Supervised Learning

A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse data

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