2006
DOI: 10.1007/11766254_32
|View full text |Cite
|
Sign up to set email alerts
|

The Lookahead Principle for Preference Elicitation: Experimental Results

Abstract: Abstract. Preference-based search is the problem of finding an item that matches best with a user's preferences. User studies show that example-based tools for preference-based search can achieve significantly higher accuracy when they are complemented with suggestions chosen to inform users about the available choices. We discuss the problem of eliciting preferences in example-based tools and present the lookahead principle for generating suggestions. We compare two different implementations of this principle… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
10
0

Year Published

2006
2006
2014
2014

Publication Types

Select...
5
1

Relationship

3
3

Authors

Journals

citations
Cited by 9 publications
(10 citation statements)
references
References 16 publications
0
10
0
Order By: Relevance
“…User studies show that suggestions are particularly effective when they present additional opportunities to the user according to the look-ahead principle [32].This paper proposes a strategy for producing suggestions that exploits prior knowledge of preference distributions and can adapt relative to users' reactions to the displayed examples.We evaluate the approach with simulations using data acquired by previous interactions with real users. In two different settings, we measured the effects of prior knowledge and adaptation strategies with satisfactory results.…”
mentioning
confidence: 99%
“…User studies show that suggestions are particularly effective when they present additional opportunities to the user according to the look-ahead principle [32].This paper proposes a strategy for producing suggestions that exploits prior knowledge of preference distributions and can adapt relative to users' reactions to the displayed examples.We evaluate the approach with simulations using data acquired by previous interactions with real users. In two different settings, we measured the effects of prior knowledge and adaptation strategies with satisfactory results.…”
mentioning
confidence: 99%
“…In most of the existing ranking algorithms, the ranking score of an item describes this item's relevance to the query [14], importance [13], match to a user's taste [3], [12], and so on. Similar to the weightbased multi-attribute approach, they do not address inter-item competition, which is a critical factor in personalized multiattribute ranking problem.…”
Section: Fundamental Ranking Methodologymentioning
confidence: 99%
“…The most widely used utility function is a linear combination of the transformed attribute values, as shown in equation (3).…”
Section: Limitations In Weight-based Ranking Approachmentioning
confidence: 99%
See 2 more Smart Citations