Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations

Esmeli, Ramazan; Bader-El-Den, Mohamed; Abdullahi, Hassana

doi:10.5220/0010107001790186

Cited by 2 publications

(2 citation statements)

References 15 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is a natural extension of conventional classification, where, instead of a single label, a preference over the class labels is requested as a prediction. Particular examples of applications for this problem are crowd opinion analysis, ranking a set of genes from their expression level, a set of relevant topics for a given document or a set of available machine learning algorithms for a given dataset and prediction task (Chatterjee et al, 2018; Esmeli et al, 2020; Werbin‐Ofir et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Efficient ensembles of distance‐based label ranking trees

Rodrigo,

Alfaro,

Aledo

et al. 2023

Expert Systems

View full text Add to dashboard Cite

Ensemble of label ranking trees (LRTs) are currently the state‐of‐the‐art approaches to the label ranking problem. Recently, bagging, boosting, and random forest methods have been proposed, all based on the LRT algorithm, which adapts regression/classification trees to the label classification problem. The LRT algorithm uses theoretically grounded Mallows probability distribution to select the best split when growing the tree, and an EM‐type process to complete the rankings on the training data when they are incomplete. These two steps have proven to be accurate, but require a large computational effort. This article proposes two alternative methods that replace the use of the Mallows distribution with distance‐based criteria to select the best split at each inner node of the tree. Moreover, these distance‐based criteria allow dealing with incomplete rankings natively, so avoiding the completion process. We have carried out an extensive experimental evaluation, which shows that (1) the integration of the two proposed modifications to the LRT algorithm into ensemble methods (bagging and random forest) are an order of magnitude faster than using the original Mallows‐based LRT algorithm; (2) ensembles using the proposed LRT methods are significantly more accurate in the presence of incomplete rankings, while they are at least as accurate in the complete case; and (3) the two modified LRT algorithms are also an order of magnitude faster than the Mallows‐based LRT, while they are at least as accurate as the Mallows‐based LRT on both complete and incomplete rankings.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient ensembles of distance‐based label ranking trees

Rodrigo,

Alfaro,

Aledo

et al. 2023

Expert Systems

View full text Add to dashboard Cite

show abstract

“…Particular examples can be ranking a set of genes from their expression level, ranking the set of relevant topics for a given document, ranking a set of available machine learning algorithms for a given dataset and prediction task, etc. [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem

Rodrigo

Alfaro

Aledo

et al. 2021

Entropy

View full text Add to dashboard Cite

The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms (e.g., k-nearest neighbors) and model-based algorithms (e.g., decision trees) have performed remarkably well in tackling the LR problem. Probabilistic Graphical Models (PGMs, e.g., Bayesian networks) have not been considered to deal with this problem because of the difficulty of modeling permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing a hidden variable, we can design a hybrid Bayesian network in which several types of distributions can be combined: multinomial for discrete variables, Gaussian for numerical variables, and Mallows for permutations. We consider two kinds of probabilistic models: one based on a Naive Bayes graphical structure (where only univariate probability distributions are estimated for each state of the hidden variable) and another where we allow interactions among the predictive attributes (using a multivariate Gaussian distribution for the parameter estimation). The experimental evaluation shows that our proposals are competitive with the start-of-the-art algorithms in both accuracy and in CPU time requirements.

show abstract

Session Similarity based Approach for Alleviating Cold-start Session Problem in e-Commerce for Top-N Recommendations

Cited by 2 publications

References 15 publications

Efficient ensembles of distance‐based label ranking trees

Efficient ensembles of distance‐based label ranking trees

Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem

Contact Info

Product

Resources

About