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.
| Date of Award | 4 Oct 2010 |
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| Original language | English |
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| Awarding Institution | - City University of Hong Kong
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| Supervisor | Qing LI (Supervisor) |
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