Article

Case amazon: ratings and reviews as part of recommendations

Authors:

Juha Leino,

Kari-Jouko RäihäAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 137 - 140

https://doi.org/10.1145/1297231.1297255

Published: 19 October 2007 Publication History

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Abstract

We studied user behavior in a recommender-rich environment, Amazon online store, to see what role the algorithm-based and user-generated recommendations play in finding items of interest. We used applied ethnography, on-location interviewing and observation, to get an accurate picture of user activity. We were especially interested in the role of customer ratings and reviews and what kind of strategies users had developed for such an environment. Our results underline the need to develop recommender systems as a whole. The way the recommendations are shown affects which items get picked, and for improving the interface, it is necessary to study the whole in addition to studying the parts in isolation.

References

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Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., Riedl, J., Is Seeing Believing? How Recommender Interfaces Affect Users' Opinions. In Proc. of CHI 2003, ACM Press, 2003, 585--592.

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Herlocker, J. L., Konstan, J. A., and Riedl, J., Explaining Collaborative Filtering Recommendations. In Proc. of CSCW'00, ACM Press, 2000, 241--250.

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Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T., Evaluating Collaborative Filtering Recommender Systems. ACM Trans. on Information Systems, 22, 1, 2004, 5--53.

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Jordan, B., and Dalal, B., Persuasive Encounters: Ethnography in the Corporation. Field Methods, 18, 4, 2006, 1--24.

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Fan YJi YZhang JSun A(2024)Our Model Achieves Excellent Performance on MovieLens: What Does It Mean?ACM Transactions on Information Systems10.1145/367516342:6(1-25)Online publication date: 18-Oct-2024
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Mythili JMenmoli SDhanusuya SSethuram M(2024)Intelligent Decision-making Algorithm for IoT Enabled Smart shopping2024 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IoT60841.2024.10550330(1-7)Online publication date: 17-Apr-2024
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Chiang HChen YSong YShuai HChang JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Shilling Black-box Review-based Recommender Systems through Fake Review GenerationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599502(286-297)Online publication date: 6-Aug-2023
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Index Terms

Case amazon: ratings and reviews as part of recommendations
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 October 2007

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Fan YJi YZhang JSun A(2024)Our Model Achieves Excellent Performance on MovieLens: What Does It Mean?ACM Transactions on Information Systems10.1145/367516342:6(1-25)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3675163
Mythili JMenmoli SDhanusuya SSethuram M(2024)Intelligent Decision-making Algorithm for IoT Enabled Smart shopping2024 International Conference on Communication, Computing and Internet of Things (IC3IoT)10.1109/IC3IoT60841.2024.10550330(1-7)Online publication date: 17-Apr-2024
https://doi.org/10.1109/IC3IoT60841.2024.10550330
Chiang HChen YSong YShuai HChang JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Shilling Black-box Review-based Recommender Systems through Fake Review GenerationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599502(286-297)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599502
Chang Bi NZhang R(2022)Electronic Word-of-Mouth and Social MediaThe Emerald Handbook of Computer-Mediated Communication and Social Media10.1108/978-1-80071-597-420221003(37-50)Online publication date: 27-Jun-2022
https://doi.org/10.1108/978-1-80071-597-420221003
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Ge YXu SLiu SFu ZSun FZhang YHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Learning Personalized Risk Preferences for RecommendationProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401056(409-418)Online publication date: 25-Jul-2020
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Barrière VKembellec G(2018)Short Review of Sentiment-Based Recommender SystemsProceedings of the 1st International Conference on Digital Tools & Uses Congress10.1145/3240117.3240120(1-4)Online publication date: 3-Oct-2018
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Whitman AJansen B(2016)Some commericial concerns of Amazon's community forums: The case of the Kindle2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)10.1109/AICCSA.2016.7945821(1-6)Online publication date: Nov-2016
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Recommendations

Joining Case-Based Reasoning and Item-Based Collaborative Filtering in Recommender Systems

TriRank: Review-aware Explainable Recommendation by Modeling Aspects

A Scalable, Accurate Hybrid Recommender System

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