APPLICATION OF MULTI-CRITERIA ANALYSIS BASED ON THE INDIVIDUAL PSYCHOLOGICAL PROFILE FOR RECOMMENDER SYSTEMS

This paper presents a novel approach for user classification exploiting multicriteriaanalysis. This method is based on measuring the distance between anobservation and its respective Pareto front. The obtained results show that thecombination of the standard KNN classification and the distance from Paretofronts gives satisfactory classification accuracy – higher than the accuracy obtainedfor each of these methods applied separately. Conclusions from thisstudy may be applied in recommender systems where the proposed methodcan be implemented as the part of the collaborative filtering algorithm

Analysis of Questionnaire Results Using Metric Methods

The paper presents the application of the metric methods to the analysis of the questionnaires used in various fields. The generic methodology is presented, including particular modules, responsible for the subsequent operations. They include generation of category patterns based on the available data, application of envelopes, dataset complexity assessment and performing classification of questionnaire results. Metrics applied in the presented research are then introduced. The methodology is tested on three data sets from the psychological, sociological and educational domains. Results show the advantage of our approach compared to the standard classification and decision making methods. Also, it may be used for the results interpretation, finding relations in data, or evaluation the test discriminating power (regarding each question separately).Proposed methodology may be found beneficial in all areas where questionnaire data is used - from classical diagnosis to HCI and big-data applications

Application of multi-criteria analysis based on individual psychological profile for recommender systems

This paper presents a novel approach for user classification exploiting multi- criteria analysis. This method is based on measuring the distance between an observation and its respective Pareto front. The obtained results show that the combination of the standard KNN classification and the distance from Pareto fronts gives satisfactory classification accuracy – higher than the accuracy ob- tained for each of these methods applied separately. Conclusions from this study may be applied in recommender systems where the proposed method can be implemented as the part of the collaborative filtering algorithm

Application of multi-criteria analysis based on individual psychological profile for recommender systems

This paper presents a novel approach for user classification exploiting multi- criteria analysis. This method is based on measuring the distance between an observation and its respective Pareto front. The obtained results show that the combination of the standard KNN classification and the distance from Pareto fronts gives satisfactory classification accuracy – higher than the accuracy ob- tained for each of these methods applied separately. Conclusions from this study may be applied in recommender systems where the proposed method can be implemented as the part of the collaborative filtering algorithm

Application of multi-criteria analysis based on individual psychological profile for recommender systems

This paper presents a novel approach for user classification exploiting multi- criteria analysis. This method is based on measuring the distance between an observation and its respective Pareto front. The obtained results show that the combination of the standard KNN classification and the distance from Pareto fronts gives satisfactory classification accuracy – higher than the accuracy ob- tained for each of these methods applied separately. Conclusions from this study may be applied in recommender systems where the proposed method can be implemented as the part of the collaborative filtering algorithm