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Model adaptation with bayesian hierarchical modeling for context-aware recommendation

Model adaptation with bayesian hierarchical modeling for con...

3rd Workshop on Context-Aware Recommender Systems 2011, CARS 2011 - In Conjunction with the 5th ACM Conference on Recommender Systems, RecSys 2011

作者： Asoh, Hideki Motomura, Yoichi Ono, Chihiro AIST AIST Tsukuba Central 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan AIST AIST Tokyo Waterfront 2-3-26 Aomi Koutouku Tokyo 135-0064 Japan KDDI R and D Laboratories Inc. 2-1-15 Ohara Fujimino Saitama 365-8502 Japan

Model adaptation is a process of modifying a model trained with a large amount of training data from the source domain to adapt a specific similar target domain by using a small amount of adaptation data regarding the target domain. Bayesian hierarchical modeling is well known as a general tool for model adaptation and multitask learning, and widely used in various areas such as marketing, ecol ogy, medicine, education, and so on in order to model the heterogeneity in the phenomena. In this work, we propose to apply the Bayesian hierarchical modeling to the problem of preference modeling, where a model trained with a large amount of supposed context data is adapted to the real context by using additional small amount of real context data. The effectiveness of the proposed method is evaluated by experiments using context aware food preference *** and Subject Descriptors H.4 [Information Systems Applications]: Miscellaneous General Terms Experimentation, Human factors, Measurement.

关键词： Context awareness Model adaptation preference modeling

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PTIME: Personalized Assistance for Calendaring

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2011年第4期2卷 40-40页

作者： Berry, Pauline M. Gervasio, Melinda Peintner, Bart Yorke-Smith, Neil SRI Int Ctr Artificial Intelligence Menlo Pk CA 94025 USA Amer Univ Beirut Soilman S Olayan Sch Business Beirut 11072020 Lebanon

In a world of electronic calendars, the prospect of intelligent, personalized time management assistance seems a plausible and desirable application of AI. PTIME (Personalized Time Management) is a learning cognitive assistant agent that helps users handle email meeting requests, reserve venues, and schedule events. PTIME is designed to unobtrusively learn scheduling preferences, adapting to its user over time. The agent allows its user to flexibly express requirements for new meetings, as they would to an assistant. It interfaces with commercial enterprise calendaring platforms, and it operates seamlessly with users who do not have PTIME. This article overviews the system design and describes the models and technical advances required to satisfy the competing needs of preference modeling and elicitation, constraint reasoning, and machine learning. We further report on a multifaceted evaluation of the perceived usefulness of the system.

关键词： Design Experimentation Human Factors Personal assistant agents preference modeling machine learning calendaring

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A Survey on Representation, Composition and Application of preferences in Database Systems

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ACM TRANSACTIONS ON DATABASE SYSTEMS 2011年第3期36卷 19-19页

作者： Stefanidis, Kostas Koutrika, Georgia Pitoura, Evaggelia Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Hong Kong Peoples R China Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece

preferences have been traditionally studied in philosophy, psychology, and economics and applied to decision making problems. Recently, they have attracted the attention of researchers in other fields, such as databases where they capture soft criteria for queries. Databases bring a whole fresh perspective to the study of preferences, both computational and representational. From a representational perspective, the central question is how we can effectively represent preferences and incorporate them in database querying. From a computational perspective, we can look at how we can efficiently process preferences in the context of database queries. Several approaches have been proposed but a systematic study of these works is missing. The purpose of this survey is to provide a framework for placing existing works in perspective and highlight critical open challenges to serve as a springboard for researchers in database systems. We organize our study around three axes: preference representation, preference composition, and preference query processing.

关键词： Algorithms Design Languages preference modeling preference queries

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A ratio ranking method of triangular intuitionistic fuzzy numbers and its application to MADM problems

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2010年第6期60卷 1557-1570页

作者： Li, Deng-Feng Fuzhou Univ Sch Management Fuzhou 350108 Fujian Peoples R China

The concept of an intuitionistic fuzzy number (IFN) is of importance for quantifying an ill-known quantity, and the ranking of IFNs is a very difficult problem. The aim of this paper is to introduce the concept of a triangular IFN (TIFN) as a special case of the IFN and develop a new methodology for ranking TIFNs. Firstly the concepts of TIFNs and cut sets as well as arithmetical operations are introduced. Then the values and ambiguities of the membership function and the non-membership function for a TIFN are defined. A new ranking method is developed on the basis of the concept of a ratio of the value index to the ambiguity index and applied to multiattribute decision making problems in which the ratings of alternatives on attributes are expressed with TIFNs. The validity and applicability of the proposed method, as well as analysis of the comparison with other methods, are illustrated with a real example. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Fuzzy set Intuitionistic fuzzy number Ranking Uncertainty preference modeling

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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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An Interactive Algorithm to Deal with Inconsistencies in the Representation of Cardinal Information

An Interactive Algorithm to Deal with Inconsistencies in the...

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13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems

作者： Mayag, Brice Grabisch, Michel Labreuche, Christophe Univ Paris 01 106-112 Blvd Hop F-75013 Paris France T.R.T France F-91767 Palaiseau France

We present a new interactive algorithm allowing to solve the inconsistencies problem, when the preferences of a decision maker cannot be representable by a numerical function. This algorithm is based on technics of li... 详细信息

ISBN: (纸本)9783642140549

关键词： Decision making preference modeling Cardinal information Inconsistencies

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preference-ORIENTED FORM DESIGN: APPLICATION TO CARS' HEADLIGHTS

PREFERENCE-ORIENTED FORM DESIGN: APPLICATION TO CARS' HEADLI...

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17th International Conference on Engineering Design

作者： Petiot, Jean-Francois Dagher, Antoine Ecole Cent Nantes IRCCyN CNRS UMR 6597 F-44321 Nantes 3 France

ISBN: (纸本)9781904670063

The integration of customer preferences is nowadays a challenge in new product development. In this paper, we describe a method which integrates the customer preferences for the design of geometrical forms. We illustrate the approach by the design of a car's headlight. From a product space, the method is based on the definition of a perceptual space, built by multidimensional scaling, and which lead to the definition of interpretable perceptual dimensions. Objective measures of the form, computed from the design variables of the design model, are selected to interpret the perceptual dimensions. These measures are representative of the overall form and of the curvature variations. At this level, the Fourier coefficients of a closed curve are used to represent the information on the curvature variations. Next, from the preferences of a customer, the target values of the selected measures corresponding to a preference optimum are calculated. We show in the paper the interest of this approach for the design of forms. The method is illustrated by the design of a car's headlight, modeled by Bezier curves and integrated in a front-end.

关键词： customer centered design perceptual space multidimensional scaling preference modeling form design Fourier coefficients

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Context-Aware preference Model Based on a Study of Difference between Real and Supposed Situation Data

Context-Aware Preference Model Based on a Study of Differenc...

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17th International Conference on User modeling, Adaptation and Personalization

作者： Ono, Chihiro Takishima, Yasuhiro Motomura, Yoichi Asoh, Hideki KDDI R&D Labs Inc 2-1-15 Ohara Fujimino Saitama 3568502 Japan AIST Digital Human RC Tokyo 1350064 Japan AIST Intelligent Syst RI Ibaraki 3058568 Japan

ISBN: (纸本)9783642022463

We propose a novel approach for constructing statistical preference models for context-aware recommender systems. To do so, one of the most important but difficult problems is acquiring sufficient training data in various contexts/situations. Particularly, some situations require a heavy workload to set them up or to collect subjects under those situations. To avoid this, often a large amount of data in a supposed situation is collected, i.e., a situation where the subject pretends/imagines that he/she is in a specific situation. Although there may be difference between the preference in the real situation and the supposed situation, this has not been considered in existing researches. Here, to study the difference, we collected a certain amount of corresponding data. We asked subjects the same question about preference both in the real and the supposed situation. Then we proposed a new model construction method using a difference model constructed from the correspondence data and showed the effectiveness through the experiments.

关键词： preference modeling context-awareness recommender systems statistical modeling

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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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On efficient WOWA optimization for decision support under risk

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2009年第6期50卷 915-928页

作者： Ogryczak, Wlodzimierz Sliwinski, Tomasz Warsaw Univ Technol Fac Elect & IT Inst Control & Computat Engn PL-00665 Warsaw Poland

The problem of averaging outcomes under several scenarios to form overall objective functions is of considerable importance in decision support under uncertainty. The so-called Weighted OWA (WOWA) aggregation offers a well-suited approach to this problem. The WOWA aggregation, similar to the classical ordered weighted averaging (OWA). uses the preferential weights assigned to the ordered values (i.e. to the worst value, the second worst and so on) rather than to the specific criteria. This allows one to model various preferences with respect to the risk. Simultaneously, importance weighting of scenarios can be introduced. In this paper, we analyze solution procedures for optimization problems with the WOWA objective functions related to decisions under risk. Linear programming formulations are introduced for optimization of the WOWA objective with monotonic preferential weights thus representing risk averse preferences. Their computational efficiency is demonstrated. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Decision under risk preference modeling Aggregation operators OWA WOWA Linear programming

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