Over- and underestimation in different product domains

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper investigates the effects of over and underestimation on the perceived Effectiveness (helpfulness) of recommender systems. We consider four different product along two dimensions, degree of objectivity and investment. Overestimation was considered more severely than underestimation with regard to perceived Effectiveness. Overestimation was also considered more severely in high investment domains compared to low investment domains. In addition, we surveyed the effect of different gaps between initial (initial impression) and final ratings (true estimate). We found that for gaps which remained in the negative half of the scale were considered less Effective than gaps which crossed over from good to bad (or from
bad to good), and gaps which remained in the positive half of the scale.
Original languageEnglish
Title of host publicationWorkshop on Recommender Systems
Subtitle of host publicationECAI 2008, 18th European Conference on Artificial Intelligence, Proceedings
EditorsMalik Ghallab, Constantine D. Spyropoulos, Nikos Fakotakis, Nikos Avouris
Place of PublicationGreece
PublisherIOS Press
Number of pages6
ISBN (Print)1586038915 , 978-1586038915
Publication statusPublished - 31 Jul 2008
Event18th European Conference on Artificial Intelligence (ECAI 2008) - Patras, Greece
Duration: 21 Jul 200825 Jul 2008

Conference

Conference18th European Conference on Artificial Intelligence (ECAI 2008)
CountryGreece
CityPatras
Period21/07/0825/07/08

Fingerprint

Recommender systems

Keywords

  • Recommender systems
  • explanations
  • user studies

Cite this

Tintarev, N., & Masthoff, J. (2008). Over- and underestimation in different product domains. In M. Ghallab, C. D. Spyropoulos, N. Fakotakis, & N. Avouris (Eds.), Workshop on Recommender Systems: ECAI 2008, 18th European Conference on Artificial Intelligence, Proceedings Greece: IOS Press.

Over- and underestimation in different product domains. / Tintarev, Nava; Masthoff, Judith.

Workshop on Recommender Systems: ECAI 2008, 18th European Conference on Artificial Intelligence, Proceedings. ed. / Malik Ghallab; Constantine D. Spyropoulos; Nikos Fakotakis; Nikos Avouris. Greece : IOS Press, 2008.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Tintarev, N & Masthoff, J 2008, Over- and underestimation in different product domains. in M Ghallab, CD Spyropoulos, N Fakotakis & N Avouris (eds), Workshop on Recommender Systems: ECAI 2008, 18th European Conference on Artificial Intelligence, Proceedings. IOS Press, Greece, 18th European Conference on Artificial Intelligence (ECAI 2008) , Patras, Greece, 21/07/08.
Tintarev N, Masthoff J. Over- and underestimation in different product domains. In Ghallab M, Spyropoulos CD, Fakotakis N, Avouris N, editors, Workshop on Recommender Systems: ECAI 2008, 18th European Conference on Artificial Intelligence, Proceedings. Greece: IOS Press. 2008
Tintarev, Nava ; Masthoff, Judith. / Over- and underestimation in different product domains. Workshop on Recommender Systems: ECAI 2008, 18th European Conference on Artificial Intelligence, Proceedings. editor / Malik Ghallab ; Constantine D. Spyropoulos ; Nikos Fakotakis ; Nikos Avouris. Greece : IOS Press, 2008.
@inproceedings{66804cbcf1ea4589939d04c7f30dbb00,
title = "Over- and underestimation in different product domains",
abstract = "This paper investigates the effects of over and underestimation on the perceived Effectiveness (helpfulness) of recommender systems. We consider four different product along two dimensions, degree of objectivity and investment. Overestimation was considered more severely than underestimation with regard to perceived Effectiveness. Overestimation was also considered more severely in high investment domains compared to low investment domains. In addition, we surveyed the effect of different gaps between initial (initial impression) and final ratings (true estimate). We found that for gaps which remained in the negative half of the scale were considered less Effective than gaps which crossed over from good to bad (or from bad to good), and gaps which remained in the positive half of the scale.",
keywords = "Recommender systems, explanations, user studies",
author = "Nava Tintarev and Judith Masthoff",
note = "Workshop on Recommender Systems, 18th European Conference on Artificial Intelligence",
year = "2008",
month = "7",
day = "31",
language = "English",
isbn = "1586038915",
editor = "Malik Ghallab and Spyropoulos, {Constantine D.} and Nikos Fakotakis and Nikos Avouris",
booktitle = "Workshop on Recommender Systems",
publisher = "IOS Press",

}

TY - GEN

T1 - Over- and underestimation in different product domains

AU - Tintarev, Nava

AU - Masthoff, Judith

N1 - Workshop on Recommender Systems, 18th European Conference on Artificial Intelligence

PY - 2008/7/31

Y1 - 2008/7/31

N2 - This paper investigates the effects of over and underestimation on the perceived Effectiveness (helpfulness) of recommender systems. We consider four different product along two dimensions, degree of objectivity and investment. Overestimation was considered more severely than underestimation with regard to perceived Effectiveness. Overestimation was also considered more severely in high investment domains compared to low investment domains. In addition, we surveyed the effect of different gaps between initial (initial impression) and final ratings (true estimate). We found that for gaps which remained in the negative half of the scale were considered less Effective than gaps which crossed over from good to bad (or from bad to good), and gaps which remained in the positive half of the scale.

AB - This paper investigates the effects of over and underestimation on the perceived Effectiveness (helpfulness) of recommender systems. We consider four different product along two dimensions, degree of objectivity and investment. Overestimation was considered more severely than underestimation with regard to perceived Effectiveness. Overestimation was also considered more severely in high investment domains compared to low investment domains. In addition, we surveyed the effect of different gaps between initial (initial impression) and final ratings (true estimate). We found that for gaps which remained in the negative half of the scale were considered less Effective than gaps which crossed over from good to bad (or from bad to good), and gaps which remained in the positive half of the scale.

KW - Recommender systems

KW - explanations

KW - user studies

M3 - Conference contribution

SN - 1586038915

SN - 978-1586038915

BT - Workshop on Recommender Systems

A2 - Ghallab, Malik

A2 - Spyropoulos, Constantine D.

A2 - Fakotakis, Nikos

A2 - Avouris, Nikos

PB - IOS Press

CY - Greece

ER -