Discovering User Behavior Patterns in Personalized Interface Agents

Liu, Jiming; Wong, Kelvin C. K.; Hui, Ka Keung

doi:10.1007/3-540-44491-2_58

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Thus, rather than presenting the same options and making the same decisions for each and every user, the agent-based approach offers much greater tailoring to the individual user. Such personalization is often represented by an interface agent that acts as a personal assistant for the user in some task environment (see [29], [85], [87], [91] for more details of work in this area). However, for this to be effective, the agents need to know the following information about their user: their objectives, their preferences for particular goods, and the range of activities.…”

Section: Conclusion and Future Challengesmentioning

confidence: 99%

On agent-mediated electronic commerce

Jennings

Leung

2003

IEEE Trans. Knowl. Data Eng.

303

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Abstract-This paper surveys and analyzes the state of the art of agent-mediated electronic commerce (e-commerce), concentrating particularly on the business-to-consumer (B2C) and business-to-business (B2B) aspects. From the consumer buying behavior perspective, agents are being used in the following activities: need identification, product brokering, buyer coalition formation, merchant brokering, and negotiation. The roles of agents in B2B e-commerce are discussed through the business-to-business transaction model that identifies agents as being employed in partnership formation, brokering, and negotiation. Having identified the roles for agents in B2C and B2B e-commerce, some of the key underpinning technologies of this vision are highlighted. Finally, we conclude by discussing the future directions and potential impediments to the wide-scale adoption of agent-mediated e-commerce.

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Section: Conclusion and Future Challengesmentioning

confidence: 99%

On agent-mediated electronic commerce

Jennings

Leung

2003

IEEE Trans. Knowl. Data Eng.

303

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“…Many applications domains need for the analysis of sequences of events, like the design of personalized interface agents [5]. The extraction of frequent sequential patterns in huge databases of sequences has been heavily studied since the design of apriori-like algorithms [1,6].…”

Section: Introductionmentioning

confidence: 99%

Mining Frequent Sequential Patterns under a Similarity Constraint

Capelle

Masson

Boulicaut

2002

Intelligent Data Engineering and Automated Learning — IDEAL 2002

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Abstract. Many practical applications are related to frequent sequential pattern mining, ranging from Web Usage Mining to Bioinformatics. To ensure an appropriate extraction cost for useful mining tasks, a key issue is to push the user-defined constraints deep inside the mining algorithms. In this paper, we study the search for frequent sequential patterns that are also similar to an user-defined reference pattern. While the effective processing of the frequency constraints is well-understood, our contribution concerns the identification of a relaxation of the similarity constraint into a convertible anti-monotone constraint. Both constraints are then used to prune the search space during a levelwise search. Preliminary experimental validations have confirmed the algorithm efficiency.

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Framework for Personalized e-Mediator

Kim¹,

Kim²

2004

Lecture Notes in Computer Science

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Discovering User Behavior Patterns in Personalized Interface Agents

Cited by 3 publications

References 2 publications

On agent-mediated electronic commerce

On agent-mediated electronic commerce

Mining Frequent Sequential Patterns under a Similarity Constraint

Framework for Personalized e-Mediator

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