Entropy-Based vs. Similarity-Influenced: Attribute Selection Methods for Dialogs Tested on Different Electronic Commerce Domains

Schmitt, Sascha; Dopichaj, Philipp; Domínguez-Marín, Patricia

doi:10.1007/3-540-46119-1_28

Cited by 9 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Or, by counting the number of interactions required to obtain the final recommended product list. In interactive decision guides (Schmitt, Dopichaj et al 2002) the number of interactions is the number of features specified by the user. In critique based systems , Viappiani, Faltings et al 2006) initial query and the number of critiques applied to direct the search becomes the total number of interactions.…”

Section: Australasian Journal Of Information Systemsmentioning

confidence: 99%

A New User Model based Interactive Product Retrieval Process for improved eBuying

Alahakoon

2015

AJIS

View full text Add to dashboard Cite

When searching for items online there are three common problems that e-buyers may encounter; null retrieval, retrieving unmanageable number of items, and retrieving unsatisfactory items. In the past information retrieval systems or recommender systems were used as solutions. With information retrieval systems, too rigorous filtering based on the user query to reduce unmanageable number of items result in either null retrieval or filtering out the items users prefer. Recommender systems on the other hand do not provide sufficient opportunity for users to communicate their needs. As a solution, this paper introduces a novel method combining a user model with an interactive product retrieval process. The new layered user model has the potential of being applied across multiple product and service domains and is able to adapt to changing user preferences. The new product retrieval algorithm is integrated with the user model and is able to successfully address null retrieval, retrieving unmanageable number of items, and retrieving unsatisfactory items. The process is demonstrated using a bench mark dataset and a case study. Finally the Product retrieval process is evaluated using a set of guidelines to illustrate its suitability to current eBuying environments.

show abstract

Section: Australasian Journal Of Information Systemsmentioning

confidence: 99%

A New User Model based Interactive Product Retrieval Process for improved eBuying

Alahakoon

2015

AJIS

View full text Add to dashboard Cite

show abstract

“…They evaluate different question-selection criteria, including an entropy-based method inspired by work on inducing decision trees (Quinlan, 1986). Schmitt and his colleagues propose an alternative approach, called simVar, based on the variance in the similarity values (Kohlmaier et al, 2001;Schmitt et al, 2002;Schmitt, 2002). One advantage of simVar is that the knowledge it uses to choose the next question is the same knowledge that is used to make the next retrieval (i.e.…”

Section: Case-based and Collaborative Recommendersmentioning

confidence: 99%

Case-based recommender systems

Bridge

Göker

McGinty

et al. 2005

The Knowledge Engineering Review

218

115

View full text Add to dashboard Cite

We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.

show abstract

“…CRS can be distinguished by the way of system in building the user model; navigation by asking and navigation by proposing [1]. In navigation by asking (NBA), the system provides a series of questions about user needs [3][4][5], while in navigation by proposing (NBP), the system suggests certain products to users and obtaining user needs in the form of feedback on the recommended products [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements

2016

View full text Add to dashboard Cite

Conversational recommender system (CRS) helps customers get products fitted their needs by repeated interaction mechanisms. When customers want to buy products having many and high tech features (e.g., cars, smartphones, notebook, etc.), most users are not familiar with product technical features. The more natural way to elicit customers' needs is by asking what they really want to use with the product they want (we call as product functional requirements). In this paper, we analyze four factors, e.g., perceived usefulness, perceived ease of use, trust and perceived enjoyment associated to user's intention to adopt the interaction model (in CRS) based on product functional requirements. Result of experiment using technology acceptance model (TAM) indicates that, for users who aren't familiar with technical features, perceives usefulness is a main factor influencing users' adoption. Meanwhile, perceived enjoyment plays a role on user's intention to adopt this interaction model, for users who are familiar with technical features of product.

show abstract

Entropy-Based vs. Similarity-Influenced: Attribute Selection Methods for Dialogs Tested on Different Electronic Commerce Domains

Cited by 9 publications

References 6 publications

A New User Model based Interactive Product Retrieval Process for improved eBuying

A New User Model based Interactive Product Retrieval Process for improved eBuying

Case-based recommender systems

Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements

Contact Info

Product

Resources

About