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Flexible recommendations over rich data

Published: 23 October 2008 Publication History

Abstract

CourseRank is a course planning tool aimed at helping students at Stanford. Recommendations comprise an integral part of it. However, implementing existing recommendation methods leads to fixed recommendations that cannot adapt to each particular student's changing requirements and do not help exploit the full extent of the available learning opportunities at the university. In this paper, we describe the concept of a flexible recommendation workflow, i.e., a high-level description of a parameterized process for computing recommendations. The input parameters of a flexible recommendation process comprise the "knobs" that control the final output and hence generate flexible recommendations. We describe how flexible recommendations can be expressed over a relational database and we present our prototype system that allows defining and executing different, fully-parameterized, recommendation workflows over relational data. Finally, we describe a user interface in CourseRank that allows students customize recommendations.

References

[1]
CourseRank: url: http://courserank.stanford.edu.
[2]
The Stanford Daily: url: http://stanforddaily.com/article/2007/12/5/-editorialcourserankalongoverduesuccess.
[3]
G. Adomavicius and Y. Kwon. New recommendation techniques for multi-criteria rating systems. IEEE Intelligent Systems, 22(3), 2007.
[4]
G. Adomavicius and A. Tuzhilin. Multidimensional recommender systems: A data warehousing approach. In WELCOM, 2001.
[5]
G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734--749, 2005.
[6]
M. Balabanovic and Y. Shoham. Fab: Content-based, collaborative recommendation. Communications of ACM, 40(3):66--72, 1997.
[7]
R. B. Bamshad Mobasher, Robin Burke and C. Williams. Towards trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology, 7(2), 2007.
[8]
D. Billsus and M. Pazzani. Learning collaborative information filters. In ICML, 1998.
[9]
D. Billsus and M. Pazzani. User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10(2-3):147--180, 2000.
[10]
J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In 14th Conf. Uncertainty in Artificial Intelligence, 1998.
[11]
A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW, pages 271--280, 2007.
[12]
D. Goldberg, D. Nichols, B. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. C. of ACM, 35(12):61--70, 1992.
[13]
J. Herlocker, J. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In ACM SIGIR Conf., 1999.
[14]
J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. TOIS, 22:5--53, 2004.
[15]
T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In ACM SIGIR Conf., 2003.
[16]
G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, Jan/Feb 2003.
[17]
M. O. Mahony, N. Hurley, N. Kushmerick, and G. Silvestre. Collaborative recommendation: A robustness analysis. ACM Transactions on Internet Technology, 4(4):344--377, 2004.
[18]
B. Miller, I. Albert, S. Lam, J. Konstan, and J. Riedl. Movielens unplugged: Experiences with an occasionally connected recommender system. In Int'l Conf. Intelligent User Interfaces, 2003.
[19]
M. Pazzani and D. Billsus. Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27:313--331, 1997.
[20]
P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Conf. on Computer Supported Cooperative Work, 1994.
[21]
A. Schein, A. Popescul, L. Ungar, and D. Pennock. Methods and metrics for cold-start recommendations. In ACM SIGIR Conf., 2002.
[22]
U. Shardanand and P. Maes. Social information filtering: Algorithms for automating word of mouth. In Conf. Human Factors in Computing Systems, 1995.
[23]
B. Sheth and P. Maes. Evolving agents for personalized information filtering. In IEEE Conf. Artificial Intelligence for Applications, 1993.

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cover image ACM Conferences
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
October 2008
348 pages
ISBN:9781605580937
DOI:10.1145/1454008
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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Publication History

Published: 23 October 2008

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Author Tags

  1. courserank
  2. flexible recommendations
  3. workflows

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RecSys08: ACM Conference on Recommender Systems
October 23 - 25, 2008
Lausanne, Switzerland

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

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  • (2015)Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative SurveyIEEE Transactions on Learning Technologies10.1109/TLT.2015.24388678:4(326-344)Online publication date: 1-Oct-2015
  • (2014)Recommendation Systems for Personalized Technology-Enhanced LearningUbiquitous Learning Environments and Technologies10.1007/978-3-662-44659-1_9(159-180)Online publication date: 11-Sep-2014
  • (2014)A Negotiation-Based Genetic Framework for Multi-Agent Credit AssignmentProceedings of the 12th German Conference on Multiagent System Technologies - Volume 873210.1007/978-3-319-11584-9_6(72-89)Online publication date: 23-Sep-2014
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