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Collaborative Filtering Algorithm Based on the preference list in the Digital Library 1

3rd International Conference on Information Computing and Applications (ICICA 2012)

作者： Gao Feng Liu Zhiyou Liu Huixin Hebei United Univ Affiliated Hosp Tangshan Hebei Peoples R China Hebei United Univ Lib Tangshan Hebei Peoples R China

ISBN: (数字)9783642340383

ISBN: (纸本)9783642340376

Traditional digital library services are built on the explicit needs of the user. The information needs of the user with specific digital resources associated with key words. This is a passive information retrieval service, only to meet the basic needs of users, not through the user's interest in reading and reading goals to provide targeted services. Through the user log information, we can mine user preferences of different digital resources, to establish a list of user preferences. By the user preference list of association and similarity computation, this paper presents a collaborative filtering algorithm based on user preference list to help readers discover more useful knowledge and information in the mass of digital resources in the digital library system.

关键词： preference list Collaborative filtering Recommendation algorithms Digital libraries

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Optimal electric vehicle charging with dynamic pricing, customer preferences and power peak reduction

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INFOR 2025年

作者： Anjos, Miguel F. Brotcorne, Luce Guillot, Gael Univ Edinburgh Sch Math Edinburgh Scotland Univ Edinburgh Maxwell Inst Math Sci Edinburgh Scotland INRIA Lille Nord Europe Lille France

We consider a provider of electric vehicle charging stations that operates a network of charging stations and use time varying pricing to maximize profit and reduce the impact on the electric grid. We propose a bilevel model with a single leader and multiple disjoint followers. The customers (followers) make decisions independently from each other. The provider (leader) sets the price of charging for each station at each time slot, and ensures there is enough energy to charge. The charging choice of each customer is represented by a combination of a preference list of (station, time) pairs and a reserve price. The proposed model thus accounts for the heterogeneity of customers with respect to price sensitivity and charging preferences. We define a single-level reformulation based on a reformulation approach from the literature on product line optimization, and we report computational results that highlight the efficiency of the new reformulation and the potential impact of our approach for reducing peaks on the electricity grid.

关键词： Electric vehicle charging dynamic pricing bilevel optimization preference list reserve price

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Fair and Stable Allocation in On-Demand Delivery Services for Meals and Groceries 16

Fair and Stable Allocation in On-Demand Delivery Services fo...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shen, Hui Gurumurthy, Krishna Murthy Huang, Yantao Ismael, Abdelrahman Sahin, Olcay Auld, Joshua Argonne National Laboratory 9700 S Cass Ave Lemont 60439 United States

Most existing studies in the shared mobility literature address the request-vehicle assignment problem with a globally optimal goal, with only some consideration to the parties involved. This study deviates from the norm and employs a decentralized approach called stable and fair matching algorithm (SFMA) for the two-sided matching problem between requests and vehicles for on-demand delivery (ODD) of meals and groceries. The SFMA matching pairs are stable and fair such that no pair of requests and drivers prefer to change the match. With meal preparation and grocery packaging time considered in simulation, a case study in the metropolitan region of Austin, Texas is conducted with POLARIS, a large-scale agent-based mesoscopic traffic simulator, to illustrate the matching performance of SFMA. The delivery services are provided by operators closely resembling transportation network companies (TNCs) in the simulation. Results are compared to the existing default heuristic strategy (DHS) to demonstrate the SFMA benefits in terms of the average wait time, matching rate, vehicle usage rate, empty vehicle miles travelled (eVMT), and the average profit of vehicles. Several scenarios are investigated to assess the impacts of fleet size on performance of SFMA. Compared to DHS, SFMA improves the matching rate and profits earned per vehicle due to the preference consideration of TNC drivers while the resultant average wait times and eVMT increases slightly. © 2025 Elsevier B.V.. All rights reserved.

关键词： Agent-based simulation fair matching On-demand delivery preference list Stable TNC market

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Fair and Stable Allocation in On-Demand Delivery Services for Meals and Groceries

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Procedia Computer Science 2025年 257卷 714-721页

作者： Hui Shen Krishna Murthy Gurumurthy Yantao Huang Abdelrahman Ismael Olcay Sahin Joshua Auld Argonne National Laboratory 9700 S Cass Ave Lemont 60439 USA

关键词： Stable fair matching On-demand delivery TNC market Agent-based simulation preference list

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Platform-based stable truck matching problem with trailer-swapping mode

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2024年 188卷

作者： Peng, Wenxiang Chen, Xiangsheng Xue, Zhaojie Liao, Yubin You, Jintao Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Shenzhen Univ Key Lab Coastal Urban Resilient Infrastructures Minist Educ Shenzhen 518060 Peoples R China State Key Lab Subtrop Bldg & Urban Sci Shenzhen 518060 Peoples R China Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China

This study investigates the platform-based stable truck-matching problem with trailer-swapping mode (STMP-TSM). The TSM is a novel collaborative transportation approach in which trucks participate in trailer swapping, significantly decreasing the rate of empty trucks and reducing transportation costs. In the STMP-TSM, a platform delivers a trailer-swapping scheme that satisfies all participating trucks. Correspondingly, an integer linear programming model is developed to maximize the total truck utility of the STMP-TSM. A specific preference list based on a chaindata structure is meticulously constructed to obtain a stable matching scheme. The preference list enables more generalized truck matching. In addition, a series of acceleration strategies is proposed to expedite the generation of a preference list while effectively reducing its length. An iterative preference-list-trim heuristic algorithm is designed, which strategically trims chains in the preference list to solve the STMP-TSM efficiently. As a benchmark, an integer linear programming model is developed based on the preference list to solve the truck-matching problem using the TSM. Finally, a series of numerical experiments are conducted to evaluate the performance of the proposed algorithm, assess the practicality of the TSM, and analyze the influences of the key parameters.

关键词： Collaborative transportation Platform Trailer-swapping mode Iterative preference-list-trim heuristic algo rithm Integer programming preference list

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Parameterized algorithms for stable matching with ties and incomplete lists

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THEORETICAL COMPUTER SCIENCE 2018年 723卷 1-10页

作者： Adil, Deeksha Gupta, Sushmita Roy, Sanjukta Saurabh, Saket Zehavi, Meirav Indian Inst Sci Educ & Res Pune Maharashtra India Univ Bergen Bergen Norway HBNI Inst Math Sci Madras Tamil Nadu India Ben Gurion Univ Negev Beer Sheva Israel

We study the parameterized complexity of NP-hard optimization versions of STABLE MATCHING and STABLE ROOMMATES in the presence of ties and incomplete lists. These problems model many real-life situations where solutions have to satisfy certain predefined criterion of suitability and compatibility. Specifically, our objective is to maximize/minimize the size of the stable matching. Our main theorems state that STABLE MATCHING and STABLE ROOMMATES admit small kernels. Consequently, we also conclude that STABLE MATCHING is fixed-parameter tractable (FPT) with respect to solution size, and that STABLE ROOMMATES is FPT with respect to a structural parameter. Finally, we analyze the special case where the input graph is planar. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Stable matching Parameterized complexity preference list

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A Nonparametric Approach to Modeling Choice with Limited Data

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MANAGEMENT SCIENCE 2013年第2期59卷 305-322页

作者： Farias, Vivek F. Jagabathula, Srikanth Shah, Devavrat MIT Alfred P Sloan Sch Management Cambridge MA 02142 USA MIT Ctr Operat Res Cambridge MA 02142 USA NYU Stern Sch Business New York NY 10012 USA MIT Cambridge MA 02139 USA

Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models fire the formidable stumbling block of simply identifying the "right" model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions;one must contend with the risks of mis-specification and overfitting/underfitting. Thus motivated, we visit the following problem: For a "generic" model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models;this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward "automating" the crucial task of choice model selection.

关键词： nonparametric choice choice models revenue prediction utility preference preference list marketing mix

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A Stable Task Assignment Scheme in Crowdsourcing 22

A Stable Task Assignment Scheme in Crowdsourcing

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22nd IEEE International Conference on Computational Science and Engineering (IEEE CSE) / 17th IEEE International Conference on Embedded and Ubiquitous Computing (IEEE EUC)

作者： Chen, Xiao Texas State Univ Dept Comp Sci San Marcos TX 78666 USA

ISBN: (纸本)9781728116648

Crowdsourcing has become a popular business development strategy that outsources self-contained small tasks to a crowd of people to solve problems that an individual or an organization cannot easily do. There are two different task assignment types in crowdsourcing platforms: the worker selected task mode and the server assigned task mode. Right now, the crowdsourcing websites only use one of them and the satisfaction of the workers and the requesters is not fully addressed. Furthermore, it is not easy for the requesters to identify qualified workers quickly. In this paper, we propose a crowdsourcing model that considers the preferences of both the requesters and workers to improve their satisfaction and thereafter benefits the crowdsourcing platform. We first put forward a ranking formula for the requesters to identify qualified workers timely based on the Bayesian inference by considering two factors: the prices the workers charge and their online reviews, and then propose a stable task assignment algorithm STA that stably matches the workers and the tasks through the stable marriage approach. Simulation results show that our proposed task assignment approach greatly improves the satisfaction of the requesters and the workers compared with the existing Hungarian method and the STA variations that only consider one factor.

关键词： Baysian inference crowdsourcing preference list stable matching task assignment

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Energy-Efficient Resource Allocation for Cognitive Radio Networks

Energy-Efficient Resource Allocation for Cognitive Radio Net...

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IEEE Global Communications Conference (GLOBECOM)

作者： Lu, Lu He, Dawei Yu, Xingxing Li, Geoffrey Ye Georgia Inst Technol Sch ECE Atlanta GA 30332 USA Georgia Inst Technol Sch Math Atlanta GA 30332 USA

ISBN: (纸本)9781479913534

In this paper, we investigate resource allocation for underlay cognitive radio (CR) networks, where the CR users coexist but do not cause unacceptable interference with licensed users. We focus on the energy efficiency (EE) performance of the system, where both throughput and energy consumption will be considered. We first formulate a sum-EE maximization problem and solve it by the Hungarian algorithm, called sum-EE-based scheme. Then, we take into account the preferences of CR users and licensed users and motivate a resource allocation scheme based on stable matching, called preference-based scheme. Numerical results demonstrate that the preference-based scheme has up to 5 0 % performance gain on interference over the sum-EE-based scheme, while the former scheme has around 5 % performance loss on EE performance compared to the latter one.

关键词： Cognitive radio (CR) network Energy Efficiency Channel Assignment Power Control preference list

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Energy-Efficient Resource Allocation for Cognitive Radio Networks

Energy-Efficient Resource Allocation for Cognitive Radio Net...

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IEEE Global Communications Conference

作者： Lu Lu Dawei He Xingxing Yu Geoffrey Ye Li School of ECE Georgia Institute of Technology USA School of Math Georgia Institute of Technology USA

ISBN: (纸本)9781479913510

In this paper, we investigate resource allocation for underlay cognitive radio (CR) networks, where the CR users coexist but do not cause unacceptable interference with licensed users. We focus on the energy efficiency (EE) performance of the system, where both throughput and energy consumption will be considered. We first formulate a sum-EE maximization problem and solve it by the Hungarian algorithm, called sum-EE-based scheme. Then, we take into account the preferences of CR users and licensed users and motivate a resource allocation scheme based on stable matching, called preference-based scheme. Numerical results demonstrate that the preference-based scheme has up to 50% performance gain on interference over the sum-EE-based scheme, while the former scheme has around 5% performance loss on EE performance compared to the latter one.

关键词： Cognitive radio (CR) network Energy Efficiency Channel Assignment Power Control preference list

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