A versatile graph-based approach to package recommendation

Roberto Interdonato, Salvatore Romeo, Andrea Tagarelli, George Karypis

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

18 Scopus citations

Abstract

An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. The problem is challenging due to a variety of critical aspects, including context-based and user-provided constraints for the items constituting a package, but also the high sparsity and limited accessibility of the primary data used to solve the problem. Most existing works on the topic have focused on a specific application domain (e.g., travel package recommendation), thus often providing ad-hoc solutions that cannot be adapted to other domains. By contrast, in this paper we propose a versatile package recommendation approach that is substantially independent of the peculiarities of a particular application domain. A key aspect in our framework is the exploitation of prior knowledge on the content type models of the packages being generated that express what the users expect from the recommendation task. Packages are learned for each package model, while the recommendation stage is accomplished by performing a PageRank-style method personalized w.r.t. The target user's preferences, possibly including a limited budget. Our developed method has been tested on a TripAdvisor dataset and compared with a recently proposed method for learning composite recommendations.

Original languageEnglish (US)
Title of host publicationProceedings - 25th International Conference on Tools with Artificial Intelligence, ICTAI 2013
Pages857-864
Number of pages8
DOIs
StatePublished - 2013
Event25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013 - Washington, DC, United States
Duration: Nov 4 2013Nov 6 2013

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Other

Other25th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2013
Country/TerritoryUnited States
CityWashington, DC
Period11/4/1311/6/13

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