We introduce the labeled influence maximization problem in social networks for target marketing which focuses on target customers. We first present four baseline methods by extending the greedy
algorithm developed for the original influence maximization problem. We propose an efficient online algorithm based on the proximity to find the seeds for maximizing the influence spread. The experiment result suggests that the proposed methods perform faster while maintaining the influence spread guarantee. Future work includes the development of algorithms under weighted cascade model and linear threshold models.
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國科會補助專題研究計畫出席國際學術會議心得報告
日期： 年 月 日
一、參加會議經過
本計劃的研究成果包括兩篇分別發表在 IEEE International Conference on Social Computing 與 ACM International Workshop on Multimedia for Cooking and Eating Activities 的論文。
前者是影響力最佳化的演算法。2011 年的 IEEE SoialCom 在 MIT 舉行，其 accepting Rate 9%。由於 機會難得，因此鼓勵參與研究計劃的學生出國增廣見聞。但受限於經費與法規的限制，因此個人自掏 腰包補助學生到 IEEE SocailCom 發表。
後者是研究過程中，所衍生出的創意構想。我們將社群網絡演算法應用在食譜網站的智慧型套餐規 劃 。 此 成 果 投 稿 到 發 表 ACM International Workshop on Multimedia for Cooking and Eating Activities(CEA)，在 15 篇投稿論文中，被選為 oral presentation 的 3 篇論文之中(8 篇 poster)， accepting rate 20%。
計畫編號 NSC－100－2221－E－004－012 計畫名稱 標籤社群網絡之影響力最佳化 出國人員
姓名 沈錳坤 服務機構
及職稱 國立政治大學資訊科學系 會議時間
101 年 10 月 29 日至101 年 11 月 2 日
會議地點 日本奈良
會議名稱
(中文) ACM 計算機學會國際多媒體會議烹飪與飲食多媒體技術工作坊 (英文) ACM International Workshop on Multimedia for Cooking and Eating Activities in conjunction with ACM International Conference on Multimedia, 2012.
發表題目
(中文) 智慧型套餐規劃：根據食材推薦套餐的食譜
(英文) Intelligent Menu Planning: Recommending Set of Recipes by Ingredients
附件五
都有不少精彩的研究成果發表。每年的 ACM MM 都同時舉辦多場 Workshop，其中 CEA 已經舉辦了四 屆。本屆 ACM MM 會議分別收到 331 篇與 407 篇來自全球五大洲的 long paper, short paper 投稿，所發 表的論文包括了 67 篇 Full Paper 的 Oral Presentation。其中，Full Oral Presentation Paper 的 Accepting Rate 為 20.2%，Short Oral Presentation Paper 的 Accepting Rate 為 31.2%。今年的會議在日本的奈良舉行。台 灣今年包括台大資工的洪一平教授、徐宏民教授，中央研究院資訊所廖弘源，王新民、鄭文皇等研究 員、交大資工蔡文錦教授、成大資工系胡敏君教授，師大資工系葉梅珍教授、畢業於成大資工的黃建 霖博士等都有發表論文。日本人舉辦會議非常用心。無論是 Reception, Banquet 都非常令人驚艷。以 Banquet 為例，大會就安排了傳統的日本藝妓在會場。除了介紹藝妓文化，也讓與會人士與其合照。
ACM International Workshop on Multimedia for Cooking and Eating Activities 主要是由日本東京大 學、京都大學與法國的學者共同發起。至今已經舉辦了四屆，也是目前結合資訊技術與美食烹飪領域 的頂尖會議。
我的報告安排在 2 日下午，Session Chair 是來自於京都大學的 Yoko Yamakata 教授。Oral Session 總 共三篇論文發表。除了我們有關智慧型套餐規劃的研究之外，還包括自動辨識切菜時的食材、食物影 像中的 Segmentation 兩篇論文。
海報發表則有包括來自法國 Orange Lab.結合 Web Service 與智慧型智慧型廚房、來自於日本 Kyoto Sangyo University 的 Hirotada Ueda 烹飪機器人等有趣的研究。Poster 結束之後，很特別地邀請來自於 Osaka Institute of Technology 的 Mutsuo Sano 教授，由文化的角度，為大家介紹日本的飲食歷史、風俗、
特色與文化背景。
會後，大會還安排了傳統的日本晚宴，邀請有興趣的學者到傳統的日本餐廳用餐。因為是會議最後 一天，因此只有包括來自於京都大學、交通大學、法國 Orange Lab.、Osaka Institute of Technology 六位 學者參加。
二、與會心得
這幾年ACM MM都舉辦Grand Challenge，今年的Grand Challenge也非常精采，印象最深刻的包括 Analyzing Social Media via Event Facets, Automatic Cinemagraphs for Ranking Beautiful Scenes, "Where is the Interestingness?" Retrieving Appealing Video Scenes by Learning Flickr-based Graded Judgments, Scaring or Pleasing: Exploit Emotional Impact of An Image, Classification of Photos based on Good Feelings, Understanding the Emotional Impact of Images, The Acousticvisual Emotion Guassians Model for Automatic Generation of Music Video。其中Automatic Cinemagraphs for Ranking Beautiful Scenes是師大葉梅珍教授
王新民教授研究團隊的研究。在研究深度上，果然王新民教授的研究團隊獲得Grand Challenge的最大 獎。在成果的呈現上最令我印象深刻的是中國清華大學。他們的學生在準備Presentation非常用心，除 了投影片非常精采之外，開場白所播放的情境影片更令人印象深刻。除了研究深度與研究創意之外，
我們也應多培養學生呈現研究成果的能力，才能躍上國際舞台。
ACM International Workshop on Multimedia for Cooking and Eating Activities的會議上也看到日本與 法國對於結合資訊技術與美食文化的「數位廚房」之創意。或許這兩個國家也是重視飲食文化的國家。
在會議過程的討論中，也看到不少技術上的創意。例如有位日本教授就建議可以透過語音處理，辨識 切菜節奏的快慢，以辨別切菜的人是專家還是生手，進而推薦適合的食譜。目前全球也都興起不少與 廚房烹飪有關的新創公司，台灣就有批年輕人創立了iCook食譜網站，獲得媒體與創投資金的注目。透 過數位技術可以激發不少相關的研究創意。食衣住行育樂中，食是我們生活中的首要。飲食在人類生 活與文化中扮演重要的角色，台灣美食聞名全球，結合資訊技術與飲食，這也是台灣值得投入的研究 領域。
三、發表論文全文或摘要(如附件) 四、建議：無
五、攜回資料名稱及內容：論文集
Xie et al. [16] proposed a hybrid semantic item model for recipe search by example. The hybrid semantic item model represents different kinds of features of recipe data. Forbes presents an approach for recipe recommendation to incorporate recipe content into matrix factorization method [1]. Experimental results showed the algorithm not only improves the recommendation accuracy but is also useful for swapping ingredients and creating recipe variations. While most research models recipes in terms of ingredients, Wang et al. [15] model cooking procedures of Chinese recipes as directed graphs and proposed a substructure similarity measurement based on the frequent graph mining.
Another branch of research has focused on the recipe recommendation for healthy food. Mino et al. investigated the recommendation of cooking recipes for a diet in which the evaluation value of intake or consumption of calorie is considered in the events of a user's schedule during the period of a diet [5].
Linear programming approach is utilized with the constraints of carbohydrate, lipid, protein, salt, and increasing the amount of vegetable intake. Karikome and Fujii propose a system to help users for planning nutritionally balanced menus [3].
Considerations of recipes that correct the user’s nutritional imbalance are incorporated into the recipe retrieval process.
Visualization of dietary habits are also provided by this system.
Shidochi et al. proposed an approach to extract replaceable ingredients from recipes in to satisfy users' various demands, such as calorie constraints and food availability [9]. In order to develop a strategy for changing users eating and cooking behaviors, Pinxteren et al. proposed a user-centered similarity measure for recommendation of healthier alternatives which are perceived to be similar to users commonly selected meals [7]. The similarity measure can be used to promote new recipes that fit users’ lifestyle. By considering the user’s cooking competence, Wagner et al. presented a context-aware recipe retrieval and recommendation system to motivate users for healthy food preparation [14]. The system tracks the user’s cooking activities with sensors in kitchen utensils and recommends healthy recipes that may increase the user’s cooking competence.
While most work on cooking related research focus on recipe recommendation and retrieval, to the best of our knowledge, little work has been done on the menu planning by ingredients.
Figure 1. Proposed Framework for Intelligent Menu Planning.
3. PROPOSED FRAMEWORK
The framework of the proposed approach for intelligent menu planning is shown in Figure 1. First, menus of recipes generated by users are collected from social recipe sites such as food.com, allrecipe.com and myrecipes.com. Then, the equivalent recipes
are identified. Next, the recipe graph which captures the accompaniment information between recipes is constructed from collected menus of recipes as well as the recipe equivalence information. Finally, given query ingredients, the Menu Planning module with approximate Steiner Tree algorithm on the constructed recipe graph is utilized to generate the menu of recipes satisfying the query ingredients.
Figure 2 shows an example of the recipe graph. The graph consists of eleven nodes; each corresponds to a recipe. For ease of illustration, each node is labeled with recipe name and part of ingredients. There is an edge between two recipe nodes if two recipes appear in the same menu. Each edge is associated with its cost, which will be described in the later section. If two recipes tend to be more fit, the weight of their edge is lower.
4. RECIPE EQUIVALENCE IDENTIFICATION
In the crawled recipes from food.com (details are described in Section 7), since these recipes are manually contributed by users, some recipes, which actually represents for the same one, are considered to be different. To deal with such problem for accurate recipe recommendation, we develop the recipe equivalence identification component in our framework. The goal aims at identifying those very similar recipes and regarding them as the same recipe. From the collected data, each recipe is associated with a set of ingredient labels (after some preprocessing on the free texts of ingredient descriptions for each recipe). We consider that if two recipes possess more the same ingredients, they tend to be the equivalence with one another (i.e., have higher potential to be the same recipe). Given two recipes ! and ! , with the corresponding sets of ingredients !! and !! , we use Jaccard similarity to measure their extent of equivalence. Specifically, the Jaccard similarity is defined as ! !!, !! |!! ∩ !!| |!! ∪ !!|.
If ! !!, !! is higher than a pre-defined threshold ! , the recipes ! and ! are regarded to be equal one. We apply such similarity computation to all the recipes for aggregating equivalent recipes to the same one. The threshold ! is set to be 0.5 in this work. Note that in fact we can refer to Wang et al.’s sophisticated method [15], which considers the cooking procedure, to measure the similarity between two recipes. Since this is not our main purpose, here we devise the abovementioned simple but effective manner.
5. RECIPE GRAPH
In this section, we give the definition of the recipe graph and the formal definition of the menu planning problem.
[Definition 1] (Recipe Graph) Let A = {a1,..., am} be a universe of m ingredients. A recipe graph is defined as an undirected weighted graph G = (V, E). Each node i in V = {1,..., n} is a recipe that possesses a set of ingredients Ti ⊆ A. Each edge (i, j) in E is the relationship between two recipes, i and j, and edge weight represents the distance between recipes.
[Definition 2] (Recipe Distance) Given a recipe graph G = (V, E) and a collection of menus, the recipe distance between two recipes i and j is defined as the reciprocal of the number of co-occurrences of recipes i and j within the collection of menus. In other words, if two recipes tend to be co-occurring in menus, the weight of their edge is lower.
[Definition 3] (Menu Cost) Given a recipe graph G = (V, E) the cost of a menu, i.e. a set of recipes, P, P ⊆ V, is defined as the sum of the weights of edges of the minimum spanning tree on the induced subgraph G[P], denoted by C(P).
Social Recipe
[Definition 4] (Menu Planning Problem) Given a recipe graph G
= (V, E) and a query consisting of a set of query ingredients Q, and the designated number of required courses r, the menu planning problem is to return a menu plan, i.e., a set of recipes, P
⊆ V, |P| = r, such that (1) ! ⊆ ! ∈! !!, and (2) the menu cost C(P) is minimized.
Take Figure 2 as an example, the recipe node “Italian Bread” has co-occurrence relationship with five recipes. Among the recipes,
Take Figure 2 as an example, the recipe node “Italian Bread” has co-occurrence relationship with five recipes. Among the recipes,