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An empirical study of using rule induction and rough sets to software cost estimation

FUNDAMENTA INFORMATICAE 2006年第1期71卷 63-82页

作者： Stefanowski, Jerzy Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

This paper concerns problems of applying the approach based on rough sets and rule induction to a software engineering data analysis. More precisely, we focus our interest on a software cost estimation problem, which includes predicting the effort required to develop a software system basing on values of cost factors. The case study of analysing the COCOMO data set, containing descriptions of representative historical projects, allows us to discuss how this approach could be used to: identify the most discriminatory cost factors, extract meaningful rule representation of classification knowledge from data, construct accurate rule based classifiers.

关键词： software engineering rough sets machine learning preference modeling software cost prediction COCOMO model

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RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework With LLM Agents

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第5期11卷 6759-6770页

作者： Shu, Yubo Zhang, Haonan Gu, Hansu Zhang, Peng Lu, Tun Li, Dongsheng Gu, Ning Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Data Sci Fudan Inst Aging MOELab Natl Dev & Intelligent Governance Shanghai 200433 Peoples R China Fudan Univ Shanghai Inst Intelligent Elect & Syst Shanghai 200433 Peoples R China Microsoft Res Asia Shanghai 200241 Peoples R China

The rapid evolution of the web has led to an exponential growth in content. Recommender systems play a crucial role in human-computer interaction (HCI) by tailoring content based on individual preferences. Despite their importance, challenges persist in balancing recommendation accuracy with user satisfaction, addressing biases while preserving user privacy, and solving cold-start problems in cross-domain situations. This research argues that addressing these issues is not solely the recommender systems' responsibility, and a human-centered approach is vital. We introduce the recommender system, assistant, and human (RAH) framework, an innovative solution with large language model (LLM)-based agents such as perceive, learn, act, critic, and reflect, emphasizing the alignment with user personalities. The framework utilizes the learn-act-critic loop and a reflection mechanism for improving user alignment. Using the real-world data, our experiments demonstrate the RAH framework's efficacy in various recommendation domains, from reducing human burden to mitigating biases and enhancing user control. Notably, our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.

关键词： Human-centered recommendation mitigate bias preference modeling reduce user burden user control

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Answer set programming and combinatorial multicriteria decision making

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2006年第4期14卷 393-420页

作者： Grabos, Rafal Univ Leipzig Dept Comp Sci D-04109 Leipzig Germany

The majority of approaches to multicriteria optimization are based on quantitative representations of preferences of a decision maker, in which numerical procedures of multicriteria analysis are used for aggregation purposes. However, very often qualitative data cannot be known in terms of absolute values so that a qualitative approach is needed. Moreover, the multicriteria methods are directly applicable when alternatives are individuals-then they may be explicitly listed and ordered by an agent. However, sometimes the set of alternatives has combinatorial structure and it must be selected from the set of Cartesian products of value domains of attributes satisfying certain constraints. Then, the space of possible alternatives has a size exponential in the number of variables and ranking all alternatives explicitly is a complex and tedious task. In this paper we propose logic programming with ordered disjunction as a qualitative approach to combinatorial multicriteria decision making, allowing a concise representation of the preference structures, and a human-like form of expressions, being close to natural language, hence providing a good readability and simplicity. A combinatorial multicriteria decision making problem is encoded as a logic program, in which preferences of the decision maker are represented qualitatively. The optimal decision corresponds exactly to the preferred answer set of the program, obtained via the well-known methods of multicriteria analysis.

关键词： multicriteria decision making answer set programming preference modeling aggregation procedures

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A Kansei evaluation approach based on the technique of computing with words

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ADVANCED ENGINEERING INFORMATICS 2016年第1期30卷 1-15页

作者： Chou, Jyh-Rong I Shou Univ Dept Creat Prod Design 1Sec 1Syuecheng Rd Kaohsiung 84001 Taiwan

Kansei evaluation plays a vital role in the implementation of Kansei engineering;however, it is difficult to quantitatively evaluate customer preferences of a product's Kansei attributes as such preferences involve human perceptual interpretation with certain subjectivity, uncertainty, and imprecision. An effective Kansei evaluation requires justifying the classification of Kansei attributes extracted from a set of collected Kansei words, establishing priorities for customer preferences of product alternatives with respect to each attribute, and synthesizing the priorities for the evaluated alternatives. Moreover, psychometric Kansei evaluation systems essentially require dealing with Kansei words. This paper presents a Kansei evaluation approach based on the technique of computing with words (CWW). The aims of this study were (1) to classify collected Kansei words into a set of Kansei attributes by using cluster analysis based on fuzzy relations;(2) to model Kansei preferences based on semantic labels for the priority analysis;and (3) to synthesize priority information and rank the order of decision alternatives by means of the linguistic aggregation operation. An empirical study is presented to demonstrate the implementation process and applicability of the proposed Kansei evaluation approach. The theoretical and practical implications of the proposed approach are also discussed. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Kansei evaluation preference modeling Kansei clustering Linguistic aggregation Computing with words

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A QUALITATIVE APPROACH TO FACE UNCERTAINTY IN DECISION-MODELS

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DECISION SUPPORT SYSTEMS 1994年第4-5期12卷 287-296页

作者： TSOUKIAS, A UNIV PARIS 09 LAMSADEPL MARECHAL LATTRE TASSIGNYF-75775 PARIS 16FRANCE

Inaccurate determination, uncertainty, imprecision and ambiguity are often present in complex decision situations where decision aid is requested. Instead of reducing complexity via quantitative models of preferences, as traditional preference modeling does, it may be necessary to represent these situations explicitly. There exist operational methods that face these problems, the principal reference being the partial comparability theory. The lack of an axiomatization however limits the operational potentialities of this theory. In the paper an axiomatic foundation of the partial comparability theory is outlined based on a sound and complete four valued logic (the truth values ''true'', ''false'', ''unknown'', ''contradictory'' are accepted). This logic is extended to the first order predicate calculus. Four basic preference relations are thus defined, namely: strict preference, weak preference, indifference and incomparability. The operational perspectives are discussed in the paper as some problems in multicriteria methods can be solved in a much easier and natural way. Moreover non monotonic reasoning devices could be built enhancing the potentialities of the theory.

关键词： preference modeling 4 VALUED LOGIC PARTIAL COMPARABILITY OUTRANKING RELATIONS

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Personalizing Queries Based on Networks of Composite preferences

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ACM TRANSACTIONS ON DATABASE SYSTEMS 2010年第2期35卷 13:1-13:50页

作者： Koutrika, Georgia Ioannidis, Yannis Stanford Univ Dept Comp Sci Stanford CA 94305 USA Univ Athens GR-10679 Athens Greece

People's preferences are expressed at varying levels of granularity and detail as a result of partial or imperfect knowledge. One may have some preference for a general class of entities, for example, liking comedies, and another one for a fine-grained, specific class, such as disliking recent thrillers with Al Pacino. In this article, we are interested in capturing such complex, multi-granular preferences for personalizing database queries and in studying their impact on query results. We organize the collection of one's preferences in a preference network ( a directed acyclic graph), where each node refers to a subclass of the entities that its parent refers to, and whenever they both apply, more specific preferences override more generic ones. We study query personalization based on networks of preferences and provide efficient algorithms for identifying relevant preferences, modifying queries accordingly, and processing personalized queries. Finally, we present results of both synthetic and real-user experiments, which: ( a) demonstrate the efficiency of our algorithms, (b) provide insight as to the appropriateness of the proposed preference model, and ( c) show the benefits of query personalization based on composite preferences compared to simpler preference representations.

关键词： Theory Algorithms Experimentation preference modeling preference networks personalization

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P-FCM: a proximity - based fuzzy clustering

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FUZZY SETS AND SYSTEMS 2004年第1期148卷 21-41页

作者： Pedrycz, W Loia, V Senatore, S Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2G7 Canada Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland Univ Salerno Dept Math & Informat I-84081 Baronissi SA Italy

In this study, we introduce and study a proximity-based fuzzy clustering. As the name stipulates, in this mode of clustering, a structure "discovery" in the data is realized in an unsupervised manner and becomes augmented by a certain auxiliary supervision mechanism. The supervision mechanism introduced in this algorithm is realized via a number of proximity "hints" (constraints) that specify an extent to which some pairs of patterns are regarded similar or different. They are provided externally to the clustering algorithm and help in the navigation of the search through the set of patterns and this gives rise to a two-phase optimization process. Its first phase is the standard FCM while the second step is concerned with the gradient-driven minimization of the differences between the provided proximity values and those computed on a basis of the partition matrix computed at the first phase of the algorithm. The proximity type of auxiliary information is discussed in the context of Web mining where clusters of Web pages are built in presence of some proximity information provided by a user who assesses (assigns) these degrees on a basis of some personal preferences. Numeric studies involve experiments with several synthetic data and Web data (pages). (C) 2004 Elsevier B.V. All rights reserved.

关键词： fuzzy clustering proximity measure web mining fuzzy C-means (FCM) supervision hints preference modeling proximity hints (constraints)

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Bayesian framework for managing preferences in decision-making

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2006年第5期91卷 556-569页

作者： Maes, MA Faber, MH Univ Calgary Dept Civil Engn Calgary AB T2N 1N4 Canada ETH Honggerberg Swiss Fed Inst Technol Dept Struct Engn CH-8093 Zurich Switzerland

A rational decision-making process does not exclude the possibility of decision makers expressing different preferences and disagreeing regarding the effects of consequences and optimal course of actions. This point of view is explored in depth in this paper. A framework is developed that includes several decision makers (instead of just one) and allows for the variability of preferences among these decision makers. The information provided by the varying opinions of decision makers can be used to optimize our own decision-making. To achieve this, likelihood functions are developed for stated preferences among both discrete and continuous alternatives, and stated preference rankings of alternatives. Two applications are pursued: the optimization of the lifecycle utility of a structural system subject to consequences of failure proportional to the intensity of hazards exceeding a variable threshold, and to follow-up consequences. Also, the problem of tight decisions or close calls is investigated in order to explore the efficiency of a Bayesian approach using stated preferences and stated rankings. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： preference modeling utility consequences Bayesian decision-making expert solicitation

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Fuzzy relations for the analysis of traders' preferences in an information market game

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2009年第3期195卷 905-913页

作者： Van de Walle, Bartel Turoff, Murray Tilburg Univ Dept Informat Syst & Management Tilburg Netherlands New Jersey Inst Technol Dept Informat Syst Newark NJ 07102 USA

n this paper we focus on preference and decision data gathered during a computer-supported information market game in which 35 students participated during seven consecutive trading sessions. The participants' individual preferences on the market shares are collected to calculate a collective preference ranking using the Borda social choice method. Comparing this preference ranking to the shares' actual market ranking resulting from the participants' trading, we find a statistically significant difference between both rankings. As the preferences established by market behavior cannot be adequately explained through a social choice rule, we propose an alternative explanation based on the herd behavior phenomenon where traders imitate the most successful trader in the market. Using a decision analysis technique based on fuzzy relations, we study the participants' rankings of the best share in the market during 7 weeks and compare the most successful trader to the other traders. The results from our analysis show that a substantial number of traders is indeed following the market leader. (C) 2007 Elsevier B.V. All rights reserved.

关键词： Fuzzy sets Information market Decision making preference modeling

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A multiple criteria approach integrating social ties to support purchase decision

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 147卷 106655-106655页

作者： Liang, Qian Liao, Xiuwu Shang, Jennifer Xi An Jiao Tong Univ Sch Management Key Lab Minist Educ Proc Control & Efficiency Engn Xian 710049 Shaanxi Peoples R China Univ Pittsburgh Joseph M Katz Grad Sch Business Pittsburgh PA 15260 USA

To leverage information sharing in online social networks, online retailers (e-tailers) have launched social networking functions on their platforms. The objective of e-tailers' social networks is to empower consumers to connect and share product information. However, the e-tailers have not used these social networks to provide product recommendations to customers. Our goal is to aid e-retailers in personalizing recommendations for customers using their friends' (referrers') preferences. Our approach involves: (a) mapping the referrers' online behaviors into a set of pairwise comparisons, and use additive value functions to model their preferences, (b) defining the degree of contextual trust of a customer towards a referrer to differentiate the roles of referrers, (c) proposing a clustering algorithm to capture referrers' heterogeneous preferences, and (d) aggregating preference information for referrers within the same subgroup to obtain diversified recommendations. On a broader note, this study illustrates how online information (i.e., preference expressions of referrers, social ties between a consumer and referrers) from an e-tailer's social network can be mined and incorporated into a decision-aiding approach to generate tailor-made recommendations for customers. Finally, we illustrate the proposed approach to with a numerical case study.

关键词： E-tailers' social networks Social ties preference modeling Multiple criteria analysis

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