A buyers integration support system in group buying

Matsuo, Tokuro; Ito, Takayuki; Shintani, Toramatsu

doi:10.1109/icect.2004.1319724

Cited by 19 publications

(11 citation statements)

References 10 publications

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“…This scheme, buyers form a group based on category of items. Matsuo et al [6] addressed decision support systems for buyers in group buying. They integrate buyers with multi-attribute preferences (utility) into a coalition.…”

Section: Related Workmentioning

confidence: 99%

“…Many existing buyer coalition schemes already exist in the literature (e.g., [2], [3], [4], [5], [6], [7], [8], [9], [10]). Some buyer coalition research (i.e., [7], [8], [9] and [10]) form a buyer coalition with bundles of items.…”

Section: Related Workmentioning

confidence: 99%

“…The main steps of Kmean algorithm are modified from Jain and Dubes [6] using cosine similarity function. We apply this K-mean clustering algorithm [6] to form the buyer coalition steps, based on their reservation prices and their locations. These steps are as follows.…”

Section: Reservation Price ($) Buyersmentioning

confidence: 99%

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A Location-Price-Based Buyer Coalition

Narabin

Boonjing

2012

2012 IEEE 12th International Conference on Computer and Information Technology

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A buyer coalition is a group of buyers who join together to negotiate with sellers to purchase items for a larger discount. In this article, a novel buyer coalition scheme, called the "GroupSimilarBuyer Scheme," is introduced. The new idea focuses on buyer coalition formation based on similarity of buyers. The mechanism of approach begins to prepare data for driving the next step. Then, a k-mean clustering algorithm is used to form the coalition of buyers which are very similar in both reservation price and location of buyer. In addition, the utility of each buyer coalition is calculated. Based on the simulation result, the GroupSimilarBuyer showed that the average standard deviation of the coalition was nearly the optimum result.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Location-Price-Based Buyer Coalition

Narabin

Boonjing

2012

2012 IEEE 12th International Conference on Computer and Information Technology

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“…He and Ioerger (2004) have considered the problem of group buying in conjunction with bundle search where a consumer needs to buy different goods as a bundle. Matsuo et al (2004) used the multi-attribute utility theory, in particular the Analytic Hierarchy Process method, to form buying groups. Sarne and Kraus (2003) have proposed a model for non-transferable payoff group buying.…”

Section: Specific Research Work For Group Buyingmentioning

confidence: 99%

Performance of software agents in non-transferable payoff group buying

Asselin

Chaib-draa

2006

Journal of Experimental & Theoretical Artificial Intelligen

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Software agents can be useful in forming buyers' groups since humans have considerable difficulties in finding Pareto-optimal deals (no buyer can be better without another being worse) in negotiation situations. What are the computational and economical performances of software agents for a group buying problem? We have developed a negotiation protocol for software agents which we have evaluated to see if the problem is difficult on average and why. This protocol probably finds a Pareto-optimal solution and, furthermore, minimizes the worst distance to ideal among all software agents given strict preference ordering. This evaluation demonstrated that the performance of software agents in this group buying problem is limited by memory requirements (and not execution time complexity). We have also investigated whether software agents following the developed protocol have a different buying behaviour from that which the customer they represented would have had in the same situation. Results show that software agents have a greater difference of behaviour (and better behaviour since they can always simulate the obvious customer behaviour of buying alone their preferred product) when they have similar preferences over the space of available products. We also discuss the type of behaviour changes and their frequencies based on the situation.

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“…Their approach is based on a genetic algorithm. Buyers with multi-attribute preferences are integrated into a coalition in the system proposed by Matsuo et al [9]. Boongasame et al [10] present an algorithm for forming a buyer coalition with bundles of items, which refers to selling more than one goods together in a package at a price which is lower than the sum of the independent prices [11].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Combinational Coalition Formation Based On Genetic Algorithm

Li¹,

Jiang²

2010

Proceedings of the 2010 International Conference on E-Business Intelligence

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In electronic commerce, the purchasing power of an individual buyer is limited. Sellers' discount policies are often based on the cost of a single transaction. A profitable purchasing strategy for buyers is to form buyer coalition in order to lower their cost. The key problem is how to form combinational coalitions among buyers in order to minimize the total cost of all buyers. In this paper, we formally analyze the problem and solve it by using the genetic algorithm. Our method can optimally allocate buyers in an efficient time. The experimental results show that the algorithm is feasible and effective.

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A buyers integration support system in group buying

Cited by 19 publications

References 10 publications

A Location-Price-Based Buyer Coalition

A Location-Price-Based Buyer Coalition

Performance of software agents in non-transferable payoff group buying

An Optimal Combinational Coalition Formation Based On Genetic Algorithm

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