International Journal of Information Technology & Decision Making

With the growth of online shopping, the buyers are faced with information and cognitive overload, entailing worse buyers' decisions. Various decision aids, more and more implemented as web services, aim at reducing this overload. Often they implement compensatory strategies that enable desirable and undesirable values of a product attribute to compensate each other. However, increasing the number of options beyond a handful can lead to poor choices, decreasing satisfaction (i.e., paradox of choice). In such a situation, that involves uncertainty, people relies more on heuristics than rationality to arrive at decisions and purchases. Heuristics, or noncompensatory strategies, do not consider a buyer's preference for multiple attributes, such as the satisficing heuristic that compares each attribute value with a predetermined cut-off level, rejecting alternatives that do not meet it. This paper presents a study combining an E-Commerce literature survey, an E-Commerce websites' analysis, and a survey of online buyers opinions. It is pointing to a gap that exists between sellers' services and buyers' expectations. Empirical evidence suggests that it can be bridged turning to noncompensatory strategies implemented as web services.

This study conducts a mechanism enhancing the time series data treatment of the time-dependent evolutionary fuzzy support vector machine inference model (EFSIM_T). The enhanced model, EFSIM_ET, was developed particularly to treat construction management problems that contain time series data. EFSIM_ET is an artificial intelligent hybrid system in which fuzzy logic (FL) deal with vagueness and approximate reasoning; support vector machine (SVM) acts as supervise learning tool; and fast messy genetic algorithm (fmGA) works to optimize FL and SVMs parameters simultaneously. Moreover, to capture the time series data characteristics, the inference model develops fmGA-based searching mechanism to seek suitable weight values to weight the training data points. This random-based searching mechanism has capacity to address the complex and dynamic nature of time series data; thus, it could improve the model's performance significantly. Nowadays, construction managementis facing complex and difficult problems due to the increasing uncertainties during project implementation. Therefore, the second objective of this study is proposed for the application of EFSIM_ET to treat two typical problems in construction: forecasting cash flow and estimate at completion. Through performance's comparison with previous works, the effectiveness and reliability of EFSIM_ET are proven. Hence, this model may be used as an intelligent decision support tool to assist the decision-making process to solve the construction management's difficulties.

In this paper, a method based on prospect theory is proposed to solve the multiple attribute decision making (MADM) problem with three formats of attribute aspirations. According to the idea of prospect theory, reference points concerning attributes are first determined. Then, for the three formats of attribute aspirations, the calculation formulae of alternatives' gains/losses concerning attributes are given. By calculating each alternative's gain/loss, a gain–loss matrix is constructed. Further, using the value function from prospect theory and the simple additive weighting method, the overall prospect value of each alternative is calculated. Based on the obtained overall prospect values, a ranking of alternatives can be determined. Finally, a case study is given to illustrate the use of the proposed method.

This paper proposes fuzzy multi-criteria decision-making approach integrated with fuzzy real option value theory. The applicability of the proposed method was shown on a real-world supermarket location selection problem. Based on the interviews with the experts, the evaluation criteria for retail location selection were identified. Then the network for fuzzy analytic network process (ANP) method was constructed. The fuzzy real option value for each alternative was calculated and used in the proposed approach as the representative of the financial dimension. Finally, the preference ranking of alternatives and the relative importance of the criteria were obtained. The significant contribution of the proposed approach is that it integrates the financial dimension (FROV) of the location problem methodologically with the multi-criteria characteristic (FANP) of the problem. Another importance of this study is the first usage of real options valuation in the area of location selection science.

In Case-Based Reasoning (CBR), case retrieval is generally guided by similarity. However, the most similar case may not be the easiest one to reuse (hard to adapt). As recommended by Smyth and Keane, it might be more efficient to use an adaptability criterion to guide the retrieval process (adaptation-guided retrieval or AGR). In the same trend but with the goal of optimizing case reuse, our approach is to consider what is similar to copy and what is different to adapt during the retrieval stage. We introduce a more general framework for retrieval, namely the reuse-guided retrieval (RGR). The goal of this paper is twofold: first, it proposes a case retrieval approach that relies on reuse cost; then, it illustrates its use by integrating adaptation cost into the case retrieval net (CRN) memory model, a similarity-based case retrieval system. The described retrieval framework optimizes case reuse early in the inference cycle, without incurring the full cost of an adaptation step. Our results on two case studies reveal that the proposed approach yields better recall quality than CRN's similarity only-based retrieval while having similar computational complexity.

The induced ordered weighted averaging distance (IOWAD) approach is very suitable in situations in which the available information is represented with exact numerical values. In this paper, we develop some extended IOWAD operators: the linguistic induced ordered weighted averaging distance (LIOWAD) operator, the uncertain induced ordered weighted averaging distance (UIOWAD) operator and the fuzzy induced ordered weighted averaging distance (FIOWAD) operator. Their main objective is to assess uncertain situations in which the available information is given in the form of linguistic variables, interval numbers and fuzzy numbers. Some special cases of these three new extensions are studied. Finally, we develop an application of the new operators in a group decision-making problem under an uncertain environment and illustrate it with a numerical example.

Hidden Markov model (HMM) has become increasingly popular in the last several years. Real-world problems such as prediction of web navigation are uncertain in nature; in this case, HMM is less appropriate i.e., we cannot assign certain probability values while in fuzzy set theory everything has elasticity. In addition to that, a theory of possibility on fuzzy sets has been developed to handle uncertainity. Thus, we propose a fuzzy hidden Markov chain (FHMC) on possibility space and solve three basic problems of classical HMM in our proposed model to overcome the ambiguous situation. Client's browsing behavior is an interesting aspect in web access. Analysis of this issue can be of great benefit in discovering user's behavior in this way we have applied our proposed model to our institution's website (www.ssn.edu.in) to identify how well a given model matches a given observation sequence, next to find the corresponding state sequence which is the best to explain the given observation sequence and then to attempt to optimize the model parameters so as to describe best how a given observation sequence comes about. The solution of these problems help us to know the authenticity of the website.

This work extends the AHP-Bayesian Prioritization procedure proposed by the authors for a local context (a single criterion) to a global context (a hierarchy) and presents a taxonomy of criteria that contribute to the ranking of the alternatives. In this global context, the paper defines and characterizes: (i) the influence of a criterion in the final prioritization of the alternatives by means of cross-validation techniques; (ii) its degree of discordance with the rest of the criteria of the hierarchy utilizing new discrepancy measures; (iii) its relevance in the final decision. Along with the taxonomy and its corresponding methodology, the paper introduces new decisional tools for the establishment of the classification and demonstrates their use from a cognitive perspective (knowledge extraction) whilst paying special attention to the resolution of the P.α and P.γ problems. The proposed methodology is illustrated by a case study on an e-participation process developed for the Zaragoza City Council (Spain) and implemented by the Zaragoza Multicriteria Decision Making Group.

The electronic mail (email) is nowadays an essential communication service being widely used by most Internet users. One of the main problems affecting this service is the proliferation of unsolicited messages (usually denoted by spam) which, despite the efforts made by the research community, still remains as an inherent problem affecting this Internet service. In this perspective, this work proposes and explores the concept of a novel symbiotic feature selection approach allowing the exchange of relevant features among distinct collaborating users, in order to improve the behavior of anti-spam filters. For such purpose, several Evolutionary Algorithms (EA) are explored as optimization engines able to enhance feature selection strategies within the anti-spam area. The proposed mechanisms are tested using a realistic incremental retraining evaluation procedure and resorting to a novel corpus based on the well-known Enron datasets mixed with recent spam data. The obtained results show that the proposed symbiotic approach is competitive also having the advantage of preserving end-users privacy.