CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.Comment: 8 pages, Submitted in CCIA 201

All that Glitters is not Gold: Understanding the Impacts of Platform Recommendation Algorithm Changes on Complementors in the Sharing Economy

Sharing platforms often leverage recommendation algorithms to reduce matching costs and improve buyer satisfaction. However, the economic impacts of different recommendation algorithms on the business operations of complementors remains unclear. This study uses natural quasi-experiments and proprietary data from a home-cooked food-sharing platform with two recommendation algorithms: word-of-mouth recommendation (WMR) and botler personalization recommendation (BPR). Results show the WMR negatively affects revenue while BPR has a positive effect. The contrast revenue effects have been attributed to capacity constraints for complementors and matching frictions for consumers. WMR encourages sellers to specialize in high-quality products but limits new product development. BPR promotes innovation to suit diverse customer tastes but may reduce quality. This reflects the exploration-exploitation trade-off: WMR exploits existing competences, while BPR explores new products to satisfy personal preferences. The authors discuss implications for how to utilize recommendation algorithms and artificial intelligence for the prosperity of sharing economy platforms

How to Meet the Diverse Needs of Consumers: Big Data Mining based on Online Review

This article applied Word2vec and image mining on OCRs analysis. Data from Dianping.com showed that in Beijing, good taste is the primary factor for customers to choose a restaurant. Unlike the general opinion, careers and locations have little influence on cuisine choice in Beijing. Hot pot is the most popular one all over the city. Warm color, medium dark light and saturation with certain amount of grey are three key aspects for an enjoyable dining environment. Offline mouth to mouth recommendation is the most useful way to spread a restaurants reputation. So making the antecedent consumer satisfy is the most applied way to appeal new ones. This findings can help restaurant owners to run a better business and promote the satisfactory

Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user's preferences and targeted item's features. Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.Comment: This paper has been accepted by the WWW 2018 Conferenc

Predicting missing pairwise preferences from similarity features in group decision making

In group decision-making (GDM), fuzzy preference relations (FPRs) refer to pairwise preferences in the form of a matrix. Within the field of GDM, the problem of estimating missing values is of utmost importance, since many experts provide incomplete preferences. In this paper, we propose a new method called the entropy-based method for estimating the missing values in the FPR. We compared the accuracy of our algorithm for predicting the missing values with the best candidate algorithm from state of the art achievements. In the proposed entropy-based method, we took advantage of pairwise preferences to achieve good results by storing extra information compared to single rating scores, for example, a pairwise comparison of alternatives vs. the alternative’s score from one to five stars. The entropy-based method maps the prediction problem into a matrix factorization problem, and thus the solution for the matrix factorization can be expressed in the form of latent expert features and latent alternative features. Thus, the entropy-based method embeds alternatives and experts in the same latent feature space. By virtue of this embedding, another novelty of our approach is to use the similarity of experts, as well as the similarity between alternatives, to infer the missing values even when only minimal data are available for some alternatives from some experts. Note that current approaches may fail to provide any output in such cases. Apart from estimating missing values, another salient contribution of this paper is to use the proposed entropy-based method to rank the alternatives. It is worth mentioning that ranking alternatives have many possible applications in GDM, especially in group recommendation systems (GRS).Andalusian Government P20 00673 PID2019-103880RB-I00 MCIN/AEI/10.13039/50110001103

Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web

In the Web Science Track of 26th International World Wide Web Conference (WWW 2017)In the Web Science Track of 26th International World Wide Web Conference (WWW 2017)In the Web Science Track of 26th International World Wide Web Conference (WWW 2017)Food and nutrition occupy an increasingly prevalent space on the web, and dishes and recipes shared online provide an invaluable mirror into culinary cultures and attitudes around the world. More specifically, ingredients, flavors, and nutrition information become strong signals of the taste preferences of individuals and civilizations. However, there is little understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences across the globe

Of Wines and Reviews: Measuring and Modeling the Vivino Wine Social Network

This paper presents an analysis of social experiences around wine consumption through the lens of Vivino, a social network for wine enthusiasts with over 26 million users worldwide. We compare users' perceptions of various wine types and regional styles across both New and Old World wines, examining them across price ranges, vintages, regions, varietals, and blends. Among other things, we find that ratings provided by Vivino users are not biased by cost. We then study how wine characteristics, language in wine reviews, and the distribution of wine ratings can be combined to develop prediction models. More specifically, we model user behavior to develop a regression model for predicting wine ratings, and a classifier for determining user review preferences.Comment: A preliminary version of this paper appears in the Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018). This is the full versio

Healthy Deliciousness': Discovering the Secret to Healthy Eating via Social Media.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018