A fuzzy content matching-based e-Commerce recommendation approach

© 2015 IEEE. E-Commerce products often come with rich and tree-structured content information describing the attributes. To well utilize the content information, this study proposed a fuzzy content matching-based recommendation approach to assist e-Commerce customers to choose their truly interested items. In this paper, users' ratings and preferences are represented using fuzzy numbers to remain uncertainties. Tree-structured content information is transformed to a set of descriptors, and users' preferences on these descriptors are derived from fuzzy ratings by using fuzzy number operations. A kind of preference dependence relations is established between descriptors to explore the relations of different content features, and as a base to sketch the complete profile of users. While the extended preference profile of a user is established, given a new item, the fuzzy match degree of the user preference and the item content information is carried out, and then a fuzzy Topsis ranking method is proposed to able to rank all candidate items according to the fuzzy match degrees, and the highest ranked items are recommended to the target user. We conduct empirical experiments on Yelp and MovieLens datasets. The results indicate that the proposed approach improve recommendation performance in terms of both coverage and accuracy

An integrated fuzzy approach to solving multi-criteria decision making problems / Nor Hanimah Kamis

Multi-criteria decision making (MCDM). Method is a technique where alternatives or options are assessed based on a set of criteria. Criteria weights determination and ranking of alternatives are two important aspects in solving MCDM problems. The evaluation of criteria importance by decision makers and performance rating towards alternatives often involve subjective preferences, which are normally vague and imprecise. This study proposed an improvised algorithm in criteria weights determination based on consistent fuzzy preference relations (CFPR). CFPR requires only (n-l) pair-wise comparisons from a given n criteria as compared to other some existing pairwise-based comparison approaches. We improvised Herrera-Viedma's et. al (2004) algorithm by introducing fuzzy numbers to represent input values for the entries of decision matrix. However, application of fuzzy numbers in representing importance of criteria requires tedious calculation. Therefore, centroid-index formula (Chen & Chen, 2000, 2003) was utilized in order to transform fuzzy numbers into crisp values, which indirectly gives lesser computation. The generalized Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method by Wang and Lee (2007) with some modification on criteria weights determination procedure using our proposed algorithm was employed in ranking the alternatives. An example problem on new staff selection in the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM) Malaysia is given to demonstrate the computational parts of this proposed model. This model can be used as an alternative tool in solving MCDM problems

Algorithms to Detect and Rectify Multiplicative and Ordinal Inconsistencies of Fuzzy Preference Relations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Consistency, multiplicative and ordinal, of fuzzy preference relations (FPRs) is investigated. The geometric consistency index (GCI) approximated thresholds are extended to measure the degree of consistency for an FPR. For inconsistent FPRs, two algorithms are devised (1) to find the multiplicative inconsistent elements, and (2) to detect the ordinal inconsistent elements. An integrated algorithm is proposed to improve simultaneously the ordinal and multiplicative consistencies. Some examples, comparative analysis, and simulation experiments are provided to demonstrate the effectiveness of the proposed methods

A kernel-based framework for learning graded relations from data

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are often expressed in a graded manner in real-world applications. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and graded relations are considered, and it unifies existing approaches because different types of graded relations can be modeled, including symmetric and reciprocal relations. This framework establishes important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated through various experiments on synthetic and real-world data.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Dominance Measuring Method Performance under Incomplete Information about Weights.

In multi-attribute utility theory, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. A very widespread approach is to gather incomplete information. A recent approach for dealing with such situations is to use information about each alternative?s intensity of dominance, known as dominance measuring methods. Different dominancemeasuring methods have been proposed, and simulation studies have been carried out to compare these methods with each other and with other approaches but only when ordinal information about weights is available. In this paper, we useMonte Carlo simulation techniques to analyse the performance of and adapt such methods to deal with weight intervals, weights fitting independent normal probability distributions orweights represented by fuzzy numbers.Moreover, dominance measuringmethod performance is also compared with a widely used methodology dealing with incomplete information on weights, the stochastic multicriteria acceptability analysis (SMAA). SMAA is based on exploring the weight space to describe the evaluations that would make each alternative the preferred one

Methods For Fuzzy Demand Assessment For IT Specialties

The rapid development of information technologies and their penetration into various spheres of human activity cause a sharply increased demand for IT specialists, in many countries of the world far exceeding the supply on them. High rates of technological transformation contribute to the diversification of the IT segment of the labor market, on the one hand, stimulate the disappearance of some and the emergence of new IT specialties, on the other. This creates a discrepancy between the structure of IT-related education and the labor market demand for IT specialists of the required profile and determines the relevance of developing methods for assessing the demand for IT specialties.This article is devoted to the study and solution of the problem of identifying the demand for IT specialties in the absence of accurate and complete information about the situation in the IT market segment. For the assessment of IT specialties and their ranking by the degree of demand in the labor market, the tasks of making individual and group decisions in the context of fuzzy initial information are formulated and solved. The methodological basis of the tasks posed is multi-criteria decision support methods based on fuzzy relations of expert preferences.The proposed approach as a mathematical tool for minimizing the structural imbalance of supply and demand for IT specialties is one of the components of the system of intellectual management of the labor market of IT specialists. The latter is designed to support the adoption of scientifically based management decisions to eliminate the mismatch of supply and demand in the IT segment of the labor market in professional, quantitative and qualitative sections