research-article

Recommendation systems with complex constraints: A course recommendation perspective

Authors:

Aditya Parameswaran,

Petros Venetis,

Hector Garcia-MolinaAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 29, Issue 4

Article No.: 20, Pages 1 - 33

https://doi.org/10.1145/2037661.2037665

Published: 08 December 2011 Publication History

Abstract

We study the problem of making recommendations when the objects to be recommended must also satisfy constraints or requirements. In particular, we focus on course recommendations: the courses taken by a student must satisfy requirements (e.g., take two out of a set of five math courses) in order for the student to graduate. Our work is done in the context of the CourseRank system, used by students to plan their academic program at Stanford University. Our goal is to recommend to these students courses that not only help satisfy constraints, but that are also desirable (e.g., popular or taken by similar students). We develop increasingly expressive models for course requirements, and present a variety of schemes for both checking if the requirements are satisfied, and for making recommendations that take into account the requirements. We show that some types of requirements are inherently expensive to check, and we present exact, as well as heuristic techniques, for those cases. Although our work is specific to course requirements, it provides insights into the design of recommendation systems in the presence of complex constraints found in other applications.

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Cited By

Zhang YTang YWang XTan Y(2024)The Effects of Natural Window Views in Classrooms on College Students’ Mood and Learning EfficiencyBuildings10.3390/buildings1406155714:6(1557)Online publication date: 28-May-2024
https://doi.org/10.3390/buildings14061557
Xu YPardos Z(2024)Extracting Course Similarity Signal using Subword EmbeddingsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636903(857-863)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636903
Sun ZFeng KYang JQu XFang HOng YLiu WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Adaptive In-Context Learning with Large Language Models for Bundle GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657808(966-976)Online publication date: 10-Jul-2024
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Index Terms

Recommendation systems with complex constraints: A course recommendation perspective
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 29, Issue 4

December 2011

172 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2037661

Issue’s Table of Contents

Copyright © 2011 ACM.

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Publication History

Published: 08 December 2011

Accepted: 01 June 2011

Revised: 01 April 2011

Received: 01 December 2010

Published in TOIS Volume 29, Issue 4

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Zhang YTang YWang XTan Y(2024)The Effects of Natural Window Views in Classrooms on College Students’ Mood and Learning EfficiencyBuildings10.3390/buildings1406155714:6(1557)Online publication date: 28-May-2024
https://doi.org/10.3390/buildings14061557
Xu YPardos Z(2024)Extracting Course Similarity Signal using Subword EmbeddingsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636903(857-863)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636903
Sun ZFeng KYang JQu XFang HOng YLiu WHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Adaptive In-Context Learning with Large Language Models for Bundle GenerationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657808(966-976)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657808
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