Evaluating product search and recommender systems for E-commerce environments
Related publications (91)
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Consumer decision support systems (CDSSs) help online users make purchasing decisions in e-commerce Web sites. To more effectively compare the usefulness of the various functionalities and interface features of such systems, we have developed a simulation ...
This paper describes the development and application of a new unique tool to support designers to optimise the sustainability of urban neighbourhoods (SUNtool). In this the paper introduces (i) the software architecture, (ii) the integrated solver and rela ...
We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that ...
Association for Computing Machinery, New York, NY 10036-5701, United States2007
Based on our recent work on the development of a trust model for recommender agents and a qualitative survey, we explore the potential of building users' trust with explanation interfaces. We present the major results from the survey, which provided a road ...
We consider example-critiquing systems that help people search for their most preferred item in a large catalog. We compare 6 existing approaches in terms of user or system-centric, implicit or explicit use of preferences, assumptions used and their behavi ...
We consider example-critiquing systems that help people search for their most preferred item in a large electronic catalog. We analyze how such systems can help users in the framework of four existing example-critiquing approaches (RABBIT, FindMe, Incremen ...
People frequently use the world-wide web to find their most preferred item among a large range of options. We call this task preference-based search. The most common tool for preference-based search on the WWW today obtains users' preferences by asking the ...
One crucial task for e-commerce systems is to help buyers find products that not only satisfy their preferences but also reduce their search effort. Usually the amount of available products is far beyond the upper limit that any individual could process by ...
Association for Computing Machinery, New York, NY 10036-5701, United States2005
A recommender system's ability to establish trust with users and convince them of its recommendations, such as which camera or PC to purchase, is a crucial design factor especially for e-commerce environments. This observation led us to build a trust model ...
Conversational recommender systems are designed to help users to more efficiently navigate complex product spaces by alternatively making recommendations and inviting users' feedback. Compound critiquing techniques provide an efficient way for users to fee ...
Association for Computing Machinery, New York, NY 10036-5701, United States2007