Exploring folksonomy and cooking procedures to boost cooking recipe recommendation

Lijuan YU*, Qing LI, Haoran XIE, Yi CAI

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

13 Citations (Scopus)


Recommender systems have gained great popularity in Internet applications in recent years, due to that they facilitate users greatly in information retrieval despite the explosive data growth. Similar to other popular domains such as the movie-, music-, and book- recommendations, cooking recipe selection is also a daily activity in which user experiences can be greatly improved by adopting appropriate recommendation strategies. Based on content-based and collaborative filtering approaches, we present in this paper a comprehensive recipe recommendation framework encompassing the modeling of the recipe cooking procedures and adoption of folksonomy to boost the recommendations. Empirical studies are conducted on a real data set to show that our method outperforms baselines in the recipe domain.

Original languageEnglish
Title of host publicationWeb Technologies and Applications : 13th Asia-Pacific Web Conference Proceedings
EditorsXiaoyong DU, Wenfei FAN, Jianmin WANG, Zhiyong PENG, Mohamed A. SHARAF
Place of PublicationBerlin
PublisherSpringer Berlin Heidelberg
Number of pages12
ISBN (Electronic)9783642202919
ISBN (Print)9783642202902
Publication statusPublished - 2011
Externally publishedYes
EventThe 13th Asia-Pacific Web Conference - Beijing, China
Duration: 18 Apr 201120 Apr 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6612 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceThe 13th Asia-Pacific Web Conference
Abbreviated titleAPWeb 2011
Internet address


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