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preference modeling for Personalized Retrieval Based on Browsing History Analysis

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第Sup1期8卷 S81-S87页

作者： Zhang, Xiaoyu Inst Sci & Tech Informat China Ctr Sci & Technol Studies Beijing 100038 Peoples R China

Personalized retrieval aims at meeting the personalized information need of users, in which preference modeling is of great importance. The user's preference can be revealed via user specification and relevance feedback, both of which require extra effort from the user and are inevitably labor intensive. In this paper, we propose a novel preference modeling algorithm based on browsing history analysis for personalized retrieval. Based on the browsing log, we explore the user's interest in both fields and field values and accumulatively update the preference model. Given a query, the relevant retrieval results can spontaneously be ranked according to their corresponding preference score without any extra user interference. Advanced settings are subsequently discussed to further improve the algorithm for practical use. Experimental results demonstrate the advantages of the proposed algorithm over previous work. (c) 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： personalized information retrieval preference modeling browsing history analysis user experience relevance feedback

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preference modeling experiments with surrogate weighting procedures for the PROMETHEE method

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第2期264卷 453-461页

作者： de Almeida Filho, Adiel T. Clemente, Tharcylla R. N. Morais, Danielle Costa de Almeida, Adiel Teixeira Univ Fed Pernambuco CDSID Av Acad Helio Ramos S-NCidade Univ BR-50740530 Recife PE Brazil

One of the main tasks in a multi-criteria decision-making process is to define weights for the evaluation criteria. However, in many situations, the decision-maker (DM) may not be confident about defining specific values for these weights and may prefer to use partial information to represent the values of such weights with surrogate weights. Although for the additive model, the use of surrogate weighting procedures has been already explored in the literature, there is a gap with regard to experimenting with such kind of preference modeling in outranking based methods, such as PROMETHEE, for which there already are applications with surrogate weights in the literature. Thus, this paper presents an experimental study on, preference modeling based on simulation so as to increase understanding and acceptance of a recommendation obtained when using surrogate weights within the PROMETHEE method. The main approaches to surrogate weights in the literature (EW, RS, RR and ROC) have been evaluated for choice and ranking problematics throughout statistical procedures, including Kendall's tau coefficient. The surrogate weighting procedure that most faithfully represents a DM's value system according to this analysis is the ROC procedure. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Multi criteria decision analysis preference modeling PROMETHEE method Partial information Surrogate weights

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preference modeling AS A REASONING PROCESS - A NEW WAY TO FACE UNCERTAINTY IN MULTIPLE CRITERIA DECISION SUPPORT SYSTEMS

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 1991年第3期55卷 309-318页

作者： TSOUKIAS, A POLITECN TORINO DIPARTIMENTO AUTOMAT INFORMAT CORSO DUCA ABRUZZI 24 I-10129 TURIN ITALY

How is it possible to improve both multicriteria methodology and building of flexible and reliable Decision Support System? In this paper an attempt is made, through the use of Artificial Intelligence techniques, towards a declarative representation of decision and activities and principally towards a new way to face problems of uncertainty in preference modeling. Particularly the use of nonmonotonic inference procedures is explored and a new formalization of the preference relations is proposed based on a reason maintenance approach. A possible generalization is also discussed.

关键词： MULTICRITERIA METHODOLOGY preference modeling NONMONOTONIC LOGIC TRUTH MAINTENANCE SYSTEM

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preference modeling Tools for the Decision Support Method ELECTRE-III-H 22

Preference Modeling Tools for the Decision Support Method EL...

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22nd International Conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Valls, Aida Moreno, Antonio Pascual-Fontanilles, Jordi Univ Rovira & Virgili Dept Engn Informat & Matemat Avda Paisos Catalans 26 Tarragona 43007 Catalonia Spain

ISBN: (纸本)9781643680156;9781643680149

ELECTRE is a multi-criteria decision aiding methodology widely extended in many fields of application, such as environmental risk assessment, systems evaluation or analysis of financial management actions. The ELECTRE-III-H method generates a partial pre-order on a set of alternatives at different levels of a hierarchy of evaluation criteria. This method models the decision maker's preferences using three parameters on each of the intermediate criteria of the hierarchy. These parameters must take values depending on the positions of the alternatives in the partial pre-orders. For a non expert user, the definition of those values is not trivial. It requires a deep understanding of the underlying method in order to assign the appropriate values. In this work, some assisting tools have been designed and implemented to help the user to find the most suitable values for these parameters. First, some initial sets of parameters are automatically calculated by the system for some predefined common preference models. Second, the user has several tools to explore the space of possible values starting from these initial ones. The system also generates several graphics that display the results obtained using different parameters, so that the user can easily identify which ones represent his preferences.

关键词： Decision support systems Analysis tools preference modeling

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Probabilistic fuzzy regression approach for preference modeling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 64卷 286-294页

作者： Jiang, Huimin Kwong, C. K. Park, Woo-Yong Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China

Two types of uncertainty, namely, randomness and fuzziness, exist in preference modeling. Fuzziness is mainly caused by human subjective judgment and incomplete knowledge, and randomness often originates from the variability of influences on the inputs and outputs of a preference model. Various techniques have been utilized to develop preference models. However, only few previous studies have addressed both fuzziness and randomness in preference modeling. Among these limited studies, none have considered the randomness caused by particular independent variables. To fill this research gap, this study proposes probabilistic fuzzy regression (PFR), a new approach for preference modeling. PFR considers both the fuzziness of data sets and the randomness caused by independent variables. In the proposed approach, probability density functions (PDFs) are adopted to model randomness. The parameter settings of the PDFs are determined using a chaos optimization algorithm. The probabilistic terms of the PFR models are generated according to the expected value functions of the random variables. Fuzzy regression analysis is employed to determine the fuzzy coefficients for all the terms of the PFR models. An industrial case study of a tea maker design is used to illustrate the applicability of PFR and evaluate its effectiveness. modeling results obtained from PFR are compared with those obtained from statistical regression, fuzzy regression, and fuzzy least-squares regression. Results of the training and validation tests show that PFR outperforms the other approaches in terms of training and validation errors. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Probabilistic fuzzy regression preference modeling Chaos optimization algorithm

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Adaptive storytelling based on personality and preference modeling

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ENTERTAINMENT COMPUTING 2020年 34卷 100342-100342页

作者： de Lima, Edirlei Soares Feijo, Bruno Furtado, Antonio L. Univ Europeia Sch Design Technol & Commun Lisbon Portugal Pontifical Catholic Univ Rio de Janeiro Dept Informat Rio De Janeiro Brazil

In almost all forms of storytelling, the background and the current state of mind of the audience members predispose them to experience a given story from a uniquely personal perspective. However, traditional story writers usually construct their narratives based on the average preferences of their audience, which does not guarantee satisfying narrative experiences for its members. When a narrative aims at providing pleasurable entertainment, having some information about the preferences of the current user for the narratives content is vital to create satisfying experiences. This paper explores personality modeling and proposes a novel approach to generate individualized interactive narratives based on the preferences of users, which we model in terms of the Big Five factors. This paper presents and evaluates the proposed method in a web-based interactive storytelling system that explores the Little Red Riding Hood folktale. The results show that the proposed method is capable of correctly recognizing the preferences of users for story events (average accuracy of 91.9%) and positively improve user satisfaction and experience.

关键词： Interactive storytelling Personality modeling preference modeling

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A review on fuzzy preference modeling methods for group decision-making

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期40卷 10645-10660页

作者： Wang, Guan Wu, Lingjiu Liu, Yusheng Ye, Xiaoping Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Xian Satellite Control Ctr Xian Peoples R China Lishui Univ Sch Engn Lishui Peoples R China

With the rise of group decision-making and the increasingly complex decision-making environment, preference modeling for decision makers has become more and more important, and many preference modeling methods have emerged. Based on the fuzzy theory, researchers have proposed a large number of preference models to express the subjective uncertainty of decision makers. These methods based on fuzzy theory are collectively referred to as fuzzy preference modeling methods. The fuzzy sets preference model is the first practice of fuzzy theory used in the field of preference modeling, and it is still widely used by researchers until now. Subsequently, based on fuzzy theory, the researchers also proposed linguistic term sets and cloud model. These methods have different representation domains, and are applicable to different decision-making environment. In this paper we give a review of classical fuzzy preference modeling methods and its latest extensions and variants. After the presentation of comparative analyses on the existing methods, we figure out some current challenges and possible future development directions in the field of fuzzy preference modeling.

关键词： preference modeling fuzzy preference modeling fuzzy sets group decision-making decision-making modeling

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Hierarchical temporal-spatial preference modeling for user consumption location prediction in Geo-Social Networks

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INFORMATION PROCESSING & MANAGEMENT 2021年第6期58卷 102715-102715页

作者： Xu, Shuai Pi, Dechang Cao, Jiuxin Fu, Xiaoming Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Univ Gottingen Inst Comp Sci Gottingen Germany

Predicting where people will consume in the future is of great significance for promoting local business. Although the prevalence of Geo-Social Networks (GSNs) has provided sufficient and desirable geo-tagged data for user mobility modeling, most studies attempt to directly fit user's preference toward locations through exploring the complex interaction between ⟨user, location⟩ pairs, which is usually hard to incorporate temporal-spatial context and side information. Moreover, the availability of multi-modal data associated with both user and location in GSNs has not yet been comprehensively leveraged. In view of the above-mentioned situations, in this article, we propose a two-stage framework composed of a Temporal Base Model (TBM) and a Location Prediction Model (LPM) to accomplish the task of user consumption location prediction at a given time in the future. In the first stage, based on user sentimental textual reviews, we leverage the hierarchical attention mechanism to capture time-sensitive user latent preference. In the second stage, we fuse the multifaceted context to derive the user's consumption probability toward different locations at the given time. We conduct extensive experiments over three real-world GSN datasets to verify the performance of the proposed approach. The experimental results encouragingly demonstrate the effectiveness of the two-stage framework, which outperforms multiple baselines in terms of different evaluation metrics such as accuracy, average percentile rank (APR) and coverage ratio.

关键词： Location prediction preference modeling Hierarchical attention Feature fusion Geo-social networks

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THE USE OF FUZZY OUTRANKING RELATIONS IN preference modeling

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FUZZY SETS AND SYSTEMS 1992年第1期49卷 33-53页

作者： PERNY, P ROY, B LAMSADE Université Paris IX Dauphine Paris France

This paper deals with preference modelling in the context of Decision Aid. In this framework, conflicting systems of logic, uncertain knowledge, ambiguous positions are always present. In order to tackle this problem, a multiple criteria methodology is proposed, mainly based on fuzzy outranking relations introduced both at one-dimensional and multi-dimensional levels. Some properties of outranking relations are investigated. Such relations are then combined using fuzzy logical connectives to generate relational systems of fuzzy preferences that are shown to be very useful to reflect the vagueness of information in the various preference situations that may be considered in the modelling process.

关键词： preference modeling FUZZY BINARY RELATION FUZZY LOGIC FUZZY PARTITION T-NORM NEGATION

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Risk preference modeling with conditional average: an application to portfolio optimization

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ANNALS OF OPERATIONS RESEARCH 2009年第1期165卷 67-95页

作者： Krzemienowski, Adam Warsaw Univ Technol Inst Control & Computat Engn PL-00665 Warsaw Poland

The paper introduces a new risk measure called Conditional Average (CAVG), which was designed to cover typical attitudes towards risk for any type of distribution. It can be viewed as a generalization of Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), two commonly used risk measures. The preference structure induced by CAVG has the interpretation in Yaari's dual theory of choice under risk and relates to Tversky and Kahneman's cumulative prospect theory. The measure is based on the new stochastic ordering called dual prospect stochastic dominance, which can be considered as a dual stochastic ordering to recently developed prospect stochastic dominance. In general, CAVG translates into a nonconvex quadratic programming problem, but in the case of a finite probability space it can also be expressed as a mixed-integer program. The paper also presents the results of computational studies designed to assess the preference modeling capabilities of the measure. The experimental analysis was performed on the asset allocation problem built on historical values of S&P 500 sub-industry indexes.

关键词： preference modeling Stochastic dominance Quantile risk measures Portfolio optimization Experimental analysis

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