Genetic Algorithm Approaches for Improving Prediction Accuracy of Multi-criteria Recommender Systems

Abstract

We often make decisions on the things we like, dislike, or even don't care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. Various techniques and approaches have been applied to design and implement such systems to generate credible recommendations to users. A multi-criteria recommendation technique is an extended approach for modeling user's preferences based on several characteristics of the items. This research presents genetic algorithm-based approaches for predicting user preferences in multi-criteria recommendation problems. Three genetic algorithms' methods, namely standard genetic algorithm, adaptive genetic algorithm, and multi-heuristic genetic algorithms are used to conduct the experiments using a multi-criteria dataset for movies recommendation. The empirical results of the comparative analysis of their performance are presented in this study.

Hypothetical tensor-based multi-criteria recommender system for new users with partial preferences

“…The model-based approach builds a model to predict unknown ratings and is based on the assumption that an item rating doesn't independent with other ratings and there exist relations between multi-criteria ratings. In this regard, various techniques have been used such as probabilistic modeling [25], support vector regression, multi-linear singular value decomposition [9], and genetic algorithm [10], deep neural network [22].…”

Soft Sensor Modeling Method by Maximizing Output-Related Variable Characteristics Based on a Stacked Autoencoder and Maximal Information Coefficients

“…The numbers of the three nodes in the hidden layer of AEs in SAE Figure 9 Flow chart of the atmospheric column. (5) 0.075694 x (9) 0.053006 x (13) 0.072433 x (2) 0.069409 x (6) 0.103963 x (10) 0.054964 x (14) 0.054684 x (3) 0 x (7) 0.050637 x (11) 0.084609 x (15) 0.064075 x (4) 0 x (8) 0.034854 x (12) 0.085078 x (16) 0.124650 are 9, 6, and 4. Figure 12 gives the absolute error trend along with the test sample number in the naphtha dry point dataset by our proposed method.…”

“…A number of data-driven modeling methods are available, and these methods are mainly divided into two categories. One is based on multivariate statistical algorithms, including principal component regression (PCR) [5] and partial least-squares regression (PLS) [6], and the other is based on statistical machine learning algorithms, such as support vector regression (SVR) [7], genetic algorithm (GA) [8], and artificial neural network (ANN) [9]. Although these algorithms can be applied to various fields, some problems, such as those related to robustness and accuracy, still exist in the soft sensor modeling process.…”

Enhanced Slope One Algorithm using Hierarchical Clustering and Trust Based Collaborative Filtering for Ecommerce Applications

“…Multi-constrained suggestion strategy was the prolonged method to develop the prototype with respect to clients' demands dependent with many attributes belonging to products. Work by Hassan et al, (2018) [15] established the strategies dependent with genetic algorithm to forecast the client's demands with respect to many-constrained suggestion challenges. Strategies such as benchmark genetic algorithm, tunable genetic algorithm, along with multiheuristic genetic algorithms were utilized in performing the investigation utilizing many constrained information to suggest the movies.…”