Recipe Recommendation: Accuracy and Reasoning

Freyne, Jill; Berkovsky, Shlomo; Smith, Gregory A.

doi:10.1007/978-3-642-22362-4_9

Cited by 30 publications

(14 citation statements)

References 9 publications

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“…For the sake of simplicity, in all cases the recipes selected for addition to the profile were limited to those for which we a real rating was available. As in the previous analyses, we evaluated the accuracy of the M5P prediction generation algorithm [Freyne et al 2011b]. …”

Section: Discussionmentioning

confidence: 99%

“…Our previous work focussed on the exploration algorithms for accurate recommendations in the food domain Berkovsky 2010a, 2010b;Freyne et al 2011aFreyne et al , 2011b. We have analysed and compared several algorithms, such as collaborative filtering, content based filtering, and a variety of hybridizations, in an effort to understand the strengths of these techniques when applied for recipe recommendation generation.…”

Section: Identifying Predictive Featuresmentioning

confidence: 99%

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Rating Bias and Preference Acquisition

Freyne

Berkovsky

Smith

2013

ACM Trans. Interact. Intell. Syst.

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Personalized systems and recommender systems exploit implicitly and explicitly provided user information to address the needs and requirements of those using their services. User preference information, often in the form of interaction logs and ratings data, is used to identify similar users, whose opinions are leveraged to inform recommendations or to filter information. In this work we explore a different dimension of information trends in user bias and reasoning learned from ratings provided by users to a recommender system. Our work examines the characteristics of a dataset of 100,000 user ratings on a corpus of recipes, which illustrates stable user bias towards certain features of the recipes (cuisine type, key ingredient, and complexity). We exploit this knowledge to design and evaluate a personalized rating acquisition tool based on active learning, which leverages user biases in order to obtain ratings bearing high-value information and to reduce prediction errors with new users.

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Section: Discussionmentioning

confidence: 99%

Section: Identifying Predictive Featuresmentioning

confidence: 99%

Rating Bias and Preference Acquisition

Freyne

Berkovsky

Smith

2013

ACM Trans. Interact. Intell. Syst.

Self Cite

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“…First, this strategy generates individual predictions pred(u x , r i ) for user u x and unrated recipe r i by using the standard CF algorithm (see (5)). In this prediction, the degree of similarity sim(u x , u y ) between the target user u x and all other users u y ∈ U is calculated according to (6) Freyne et al (2011). Then, individual ratings rat (u y , r i ) of users who rated r i are aggregated according to the similarity degree sim (u x , u y …”

Section: Type 4: Food Recommender Systems For Groupsmentioning

confidence: 99%

An overview of recommender systems in the healthy food domain

et al. 2017

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Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.

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“…The presence of food related content on the web has become prominent in recent years. Along with the overwhelming volume of images on social-media, the use of online sites as a source for recipes and culinary ideas is expanding [1,2,3]. With the large quantity of available data, finding the right recipe becomes a difficult task.…”

Section: Introductionmentioning

confidence: 99%

Eating healthier: Exploring nutrition information for healthier recipe recommendation

Chen

Jia

Gorbonos

et al. 2020

Information Processing & Management

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With the booming of personalized recipe sharing networks (e.g., Yummly), a deluge of recipes from different cuisines could be obtained easily. In this paper, we aim to solve a problem which many home-cooks encounter when searching for recipes online. Namely, finding recipes which best fit a handy set of ingredients while at the same time follow healthy eating guidelines. This task is especially difficult since the lions share of online recipes have been shown to be unhealthy.In this paper we propose a novel framework named NutRec, which models the interactions between ingredients and their proportions within recipes for the purpose of offering healthy recommendation. Specifically, NutRec consists of three main components: 1) using an embedding-based ingredient predictor to predict the relevant ingredients with user-defined initial ingredients, 2) predicting the amounts of the relevant ingredients with a multi-layer perceptronbased network, 3) creating a healthy pseudo-recipe with a list of ingredients and their amounts according to the nutritional information and recommending the top similar recipes with the pseudo-recipe. We conduct the experiments on two recipe datasets, including Allrecipes with 36,429 recipes and Yummly with 89,413 recipes, respectively. The empirical results support the framework's * This is to indicate the corresponding authors.intuition and showcase its ability to retrieve healthier recipes.

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Recipe Recommendation: Accuracy and Reasoning

Cited by 30 publications

References 9 publications

Rating Bias and Preference Acquisition

Rating Bias and Preference Acquisition

An overview of recommender systems in the healthy food domain

Eating healthier: Exploring nutrition information for healthier recipe recommendation

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