A personalized and integrative comparison-shopping engine and its applications

Yuan, Soe‐Tsyr

doi:10.1016/s0167-9236(02)00077-5

Cited by 45 publications

(25 citation statements)

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“…CSAs were expected to reduce the search cost associated with obtaining price information by finding the best price or nearest point of distribution [Maes 1994;Montgomery et al 2004;Pathak 2010;]. As such, the majority of CSA research is mostly concerned with analyzing the influence of CSAs on retailers' and consumers' behavior [Clay et al 2002;Johnson et al 2004;Karat et al 2004;Smith 2002;Yuan 2003] and the cost of obtaining information [Bakos 1997;Waldeck 2008].…”

Section: Related Workmentioning

confidence: 99%

Competitive Shopbots-Mediated Markets

Sarne

2013

ACM Trans. Econ. Comput.

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This article considers markets mediated by autonomous self-interested comparison-shopping agents. As in today's markets, the agents do not charge buyers for their services but rather benefit from payments obtained from sellers upon the execution of a transaction. The agents aim at maximizing their expected benefit, taking into consideration the cost incurred by the search and competition dynamics that arise in the multi-agent setting. This article provides a comprehensive analysis of such models, based on search theory principles. The analysis results in a characterization of the buyers' and agents' search strategies in equilibrium. The main result of this article is that the use of self-interested comparison-shopping agents can result in a beneficial equilibrium, where both buyers and sellers benefit, in comparison to the case where buyers control the comparison-shopping agent, and the comparison-shopping agents necessarily do not lose. This, despite the fact that the service is offered for free to buyers and its cost is essentially covered by sellers. The analysis generalizes to any setting where buyers can use self-interested agents capable of effectively performing the search (e.g., evaluating opportunities) on their behalf.

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Section: Related Workmentioning

confidence: 99%

Competitive Shopbots-Mediated Markets

Sarne

2013

ACM Trans. Econ. Comput.

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“…The natural language processing may be used to automatically extract terms to add to the ontology using grammar analysis [3,15,18], linguistic patterns [19,20] with predefined templates [22,26]. However the quality of automatically extracted knowledge from natural language sources is not accurate enough yet, so the draft should be manually refined by knowledge engineers [20,25].…”

Section: Knowledge Acquisitionmentioning

confidence: 99%

Architecture and performance of the rule based comparison shopping

Lim

Kang

Lee

et al. 2008

Proceedings of the 10th International Conference on Electronic Commerce

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Comparison shopping is one of the important ingredients in electronic commerce, because it saves time and efforts of searching for product information and costs. However, most shopping malls still rely on the simple data such as price and other descriptive specifications, and cannot support the services of comparing the exact cost which requires rule based computation such as delivery cost. The comparison of delivery costs requires tailored computation because each bookstore has different free shipping rules, delivery options, and shipping rates. The purpose of this paper is to propose a rule based comparison shopping framework using XRML(eXtensible Rule Markup Language) approach. We propose the architecture, named ConsiderD, of maintaining rules in the comparison portal site to be consistent with its original pages. We experiment the benefit of using the rule based exact computation in comparison of book buying with the result by estimated average price such as BestWebBuy.com does. The result shows that providing the benefit of rule based comparison is significant and the hidden information such as delivery cost can be effectively processed with XRML framework.

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“…They could be knowledgeable, open-ended and able to have a dialogue with the user. Research shows that shopping agents are a possibility, but have thus far achieved mixed results [55,82,83]. As long as the agents are insufficiently informed and trusted, they are reduced to information gatherers; the user still has significant information filtering, weighing and integration to perform.…”

Section: Introductionmentioning

confidence: 99%