Improving package recommendations through query relaxation

Matteo Brucato, Azza Abouzied, Alexandra Meliou

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

Abstract

Recommendation systems aim to identify items that are likely to be of interest to users. In many cases, users are interested in package recommendations as collections of items. For example, a dietitian may wish to derive a dietary plan as a collection of recipes that is nutritionally balanced, and a travel agent may want to produce a vacation package as a coordinated collection of travel and hotel reservations. Recent work has explored extending recommendation systems to support packages of items. These systems need to solve complex combinatorial problems, enforcing various properties and constraints defined on sets of items. Introducing constraints on packages makes recommendation queries harder to evaluate, but also harder to express: Queries that are under-specified produce too many answers, whereas queries that are over-specified frequently miss interesting solutions. In this paper, we study query relaxation techniques that target package recommendation systems. Our work offers three key insights: First, even when the original query result is not empty, relaxing constraints can produce preferable solutions. Second, a solution due to relaxation can only be preferred if it improves some property specified by the query. Third, relaxation should not treat all constraints as equals: some constraints are more important to the users than others. Our contributions are threefold: (a) we define the problem of deriving package recommendations through query relaxation, (b) we design and experimentally evaluate heuristics that relax query constraints to derive interesting packages, and (c) we present a crowd study that evaluates the sensitivity of real users to different kinds of constraints and demonstrates that query relaxation is a powerful tool in diversifying package recommendations.

Original languageEnglish (US)
Title of host publication1st International Workshop on Bringing the Value of "Big Data" to Users, Data4U 2014 - In Conjunction with the 40th International Conference on Very Large Data Bases
PublisherAssociation for Computing Machinery
Pages13-18
Number of pages6
ISBN (Print)9781450331869
DOIs
StatePublished - Jan 1 2014
Event1st International Workshop on Bringing the Value of "Big Data" to Users, Data4U 2014 - In Conjunction with the 40th International Conference on Very Large Data Bases - Hangzhou, China
Duration: Sep 1 2014Sep 1 2014

Publication series

NameACM International Conference Proceeding Series

Other

Other1st International Workshop on Bringing the Value of "Big Data" to Users, Data4U 2014 - In Conjunction with the 40th International Conference on Very Large Data Bases
CountryChina
CityHangzhou
Period9/1/149/1/14

Keywords

  • packages
  • query relaxation
  • recommendation system

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Brucato, M., Abouzied, A., & Meliou, A. (2014). Improving package recommendations through query relaxation. In 1st International Workshop on Bringing the Value of "Big Data" to Users, Data4U 2014 - In Conjunction with the 40th International Conference on Very Large Data Bases (pp. 13-18). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/2658840.2658843