Enhancing content-based recommendation with the task model of classification

Yiwen Wang*, Shenghui Wang, Natalia Stash, Lora Aroyo, Guus Schreiber

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

13 Citations (Scopus)

Abstract

In this paper, we define reusable inference steps for content-based recommender systems based on semantically-enriched collections. We show an instantiation in the case of recommending artworks and concepts based on a museum domain ontology and a user profile consisting of rated artworks and rated concepts. The recommendation task is split into four inference steps: realization, classification by concepts, classification by instances, and retrieval. Our approach is evaluated on real user rating data. We compare the results with the standard content-based recommendation strategy in terms of accuracy and discuss the added values of providing serendipitous recommendations and supporting more complete explanations for recommended items.

Original languageEnglish
Title of host publicationKnowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings
Pages431-440
Number of pages10
ISBN (Electronic)978-3-642-16438-5
DOIs
Publication statusPublished - 22 Dec 2010
Externally publishedYes
Event17th International Conference on Knowledge Engineering and Management by the Masses, EKAW 2010 - Lisbon, Portugal
Duration: 11 Oct 201015 Oct 2010
Conference number: 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6317 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Knowledge Engineering and Management by the Masses, EKAW 2010
Abbreviated titleEKAW 2010
CountryPortugal
CityLisbon
Period11/10/1015/10/10

Fingerprint Dive into the research topics of 'Enhancing content-based recommendation with the task model of classification'. Together they form a unique fingerprint.

  • Cite this

    Wang, Y., Wang, S., Stash, N., Aroyo, L., & Schreiber, G. (2010). Enhancing content-based recommendation with the task model of classification. In Knowledge Engineering and Management by the Masses - 17th International Conference, EKAW 2010, Proceedings (pp. 431-440). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6317 LNAI). https://doi.org/10.1007/978-3-642-16438-5_33