Intelligent Agents for Multi-user Preference Elicitation

2022 ◽  
pp. 151-161
Author(s):  
Ons Nefla ◽  
Iméne Brigui ◽  
Meltem Ozturk ◽  
Paolo Viappiani
Keyword(s):  
Intelligent Agents ◽  
Preference Elicitation ◽  
User Preference

scholarly journals User-Involved Preference Elicitation for Product Search and Recommender Systems

AI Magazine ◽  
2008 ◽  
Vol 29 (4) ◽  
pp. 93 ◽  
Author(s):  
Pearl Pu ◽  
Li Chen
Keyword(s):  
Recommender Systems ◽  
State Of The Art ◽  
Preference Elicitation ◽  
Design Guidelines ◽  
User Preferences ◽  
The State ◽  
User Preference ◽  
Large Collection ◽  
Complete Model ◽  
Product Search

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state-of-the-art of the developed techniques and discuss concrete scenarios where they have been applied and tested.

scholarly journals A Genetic-Based Pairwise trip Planner Recommender System

2021 ◽  
Author(s):  
Nunung Nurul Qomariyah ◽  
Dimitar Kazakov
Keyword(s):  
Recommender System ◽  
Evaluation Study ◽  
Preference Elicitation ◽  
User Preference ◽  
Elicitation Method ◽  
Massive Growth ◽  
Internet Users ◽  
Pairwise Preference ◽  
Personalized Recommender System ◽  
Trip Planner

Abstract The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

On Design Preference Elicitation With Crowd Implicit Feedback

2012 ◽  
Author(s):  
Yi Ren ◽  
Panos Y. Papalambros
Keyword(s):  
Preference Elicitation ◽  
Black Box ◽  
User Preference ◽  
Implicit Feedback ◽  
Optimization Approach ◽  
Preference Model ◽  
Binary Choices ◽  
Design Preference ◽  
Query Algorithm ◽  
Query Algorithms

We define preference elicitation as an interaction, consisting of a sequence of computer queries and human implicit feedback (binary choices), from which the user’s most preferred design can be elicited. The difficulty of this problem is that, while a human-computer interaction must be short to be effective, query algorithms usually require lengthy interactions to perform well. We address this problem in two steps. A black-box optimization approach is introduced: The query algorithm retrieves and updates a user preference model during the interaction and creates the next query containing designs that are both likely to be preferred and different from existing ones. Next, a heuristic based on accumulated elicitations from previous users is employed to shorten the current elicitation by querying preferred designs from previous users (the “crowd”) who share similar preferences to the current one.

User Preference Elicitation, Rating Sparsity and Cold Start

2018 ◽  
pp. 253-294 ◽  
Author(s):  
Mehdi Elahi ◽  
Matthias Braunhofer ◽  
Tural Gurbanov ◽  
Francesco Ricci
Keyword(s):  
Cold Start ◽  
Preference Elicitation ◽  
User Preference

scholarly journals A genetic-based pairwise trip planner recommender system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nunung Nurul Qomariyah ◽  
Dimitar Kazakov
Keyword(s):  
Recommender System ◽  
Evaluation Study ◽  
Preference Elicitation ◽  
User Preference ◽  
Elicitation Method ◽  
Massive Growth ◽  
Internet Users ◽  
Pairwise Preference ◽  
Personalized Recommender System ◽  
Trip Planner

AbstractThe massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.

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