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Please use this identifier to cite or link to this item: http://hdl.handle.net/2031/6220

Title: Cooking recipe manipulations : modeling, organization, personalized search and recommendation
Other Titles: Peng ren cai pu guan li : jian mo, zu zhi, ge xing hua jian suo yu tui jian
烹飪菜譜管理 : 建模, 組織, 個性化檢索與推薦
Authors: Yu, Lijuan (余麗娟)
Department: Department of Computer Science
Degree: Master of Philosophy
Issue Date: 2010
Publisher: City University of Hong Kong
Subjects: Cooking -- Computer network resources.
Cooking -- Data processing.
Internet searching.
Notes: CityU Call Number: TX643 .Y8 2010
vii, 68 leaves : ill. 30 cm.
Thesis (M.Phil.)--City University of Hong Kong, 2010.
Includes bibliographical references (leaves 61-68)
Type: thesis
Abstract: Cooking is a daily and necessary activity in our real life. Recipe relevant applications, such as resource organization, browsing, search and recommendations, are of great values to users who like or enjoy cooking greatly. In this thesis, we start our work from recipe data modeling. Cooking recipes can be viewed as a kind of complex data containing rich information like ingredients, seasonings, cooking methods, tastes, nutrition, etc, many of which are difficult to be represented by simple data structures. In our model, we divide recipe features into three categories: cooking features, nutrition features, flavor and other features according to users' concerning aspects over recipe information, and employ a hybrid scheme consisting of three parts to model these features comprehensively. The second problem addressed in this thesis is to provide a personalized content organization schema for recipe resources. We set up a folksonomy environment for collecting user annotations, and index the recipe resources using tags. Based on the semantic network, we propose three types of personal views for content organization, named as Media View, Semantic View, and Personalized View, respectively. Next, we move on to the personalized recipe search strategy in a folksonomy environment that can help a user quickly find out his/her desired recipe(s) with simple hints. In such an environment, users are invited to tag their favorite recipes using interested terms, and by aggregating such interactions it enables the system to build tag-based user profiles. Meanwhile, each recipe may receive a list of collaboratively edited tags from multiple users, describing its semantic Cooking is a daily and necessary activity in our real life. Recipe relevant applications, such as resource organization, browsing, search and recommendations, are of great values to users who like or enjoy cooking greatly. In this thesis, we start our work from recipe data modeling. Cooking recipes can be viewed as a kind of complex data containing rich information like ingredients, seasonings, cooking methods, tastes, nutrition, etc, many of which are difficult to be represented by simple data structures. In our model, we divide recipe features into three categories: cooking features, nutrition features, flavor and other features according to users' concerning aspects over recipe information, and employ a hybrid scheme consisting of three parts to model these features comprehensively. The second problem addressed in this thesis is to provide a personalized content organization schema for recipe resources. We set up a folksonomy environment for collecting user annotations, and index the recipe resources using tags. Based on the semantic network, we propose three types of personal views for content organization, named as Media View, Semantic View, and Personalized View, respectively. Next, we move on to the personalized recipe search strategy in a folksonomy environment that can help a user quickly find out his/her desired recipe(s) with simple hints. In such an environment, users are invited to tag their favorite recipes using interested terms, and by aggregating such interactions it enables the system to build tag-based user profiles. Meanwhile, each recipe may receive a list of collaboratively edited tags from multiple users, describing its semantic features. By building up connections between the tag-based user and recipe profiles, it facilitates the goal of personalized search. The fourth relevant issue addressed by this thesis is to devise a personalized recipe recommendation strategy. The basic idea of our approach is to blend the content-based and collaborative filtering methods, with the goal of exploring the folksonomy to identify interest-similar users. As a result, not only can it overcome the 'cold start' problem, but also will the resultant system keep improving over time with more users joinning and becoming members of it. As a part of this dissertation research, we have conducted some empirical studies on a real data set based upon a prototype recipe system that we have implemented, so as to evaluate our approach. The experiment results demonstrate the validity and efficiency of our proposed methods for both personalized recipe search and recommendation.
Online Catalog Link: http://lib.cityu.edu.hk/record=b3947821
Appears in Collections:CS - Master of Philosophy

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