Restaurant Information Research Articles

Performance of Deep Feed-Forward Neural Network Algorithm Based on Content-Based Filtering Approach

Background: Selecting a restaurant in a diverse city like Bandung can be challenging. This study leverages Twitter data and local restaurant information to develop an advanced recommendation system to improve decision-making. Objective: The system integrates content-based filtering (CBF) with deep feedforward neural network (DFF) classification to enhance the accuracy and relevance of restaurant recommendations. Methods: Data was sourced from Twitter and PergiKuliner, with restaurant-related tweets converted into rating values. The CBF combined Bag of Words (BoW) and cosine similarity, followed by DFF classification. SMOTE was applied during training to address data imbalance. Results: The initial evaluation of CBF showed a Mean Absolute Error (MAE) of 0.0614 and a Root Mean Square Error (RMSE) of 0.0934. The optimal DFF configuration in the first phase used two layers with 32/16 nodes, a dropout rate of 0.3, and a 20% test size. This setup achieved an accuracy of 81.08%, precision of 82.89%, recall of 76.93%, and f1-scores of 79.23%. In the second phase, the RMSprop optimizer improved accuracy to 81.30%, and tuning the learning rate to 0.0596 further increased accuracy to 89%, marking a 9.77% improvement. Conclusion: The research successfully developed a robust recommendation system, significantly improving restaurant recommendation accuracy in Bandung. The 9.77% accuracy increase highlights the importance of hyperparameter tuning. SMOTE also proved crucial in balancing the dataset, contributing to a well-rounded learning model. Future studies could explore additional contextual factors and experiment with recurrent or convolutional neural networks to enhance performance further.

Visual information and appearance: The impact of visual attributes of user-generated photos on review helpfulness

Images have become integral to consumers’ sharing of consumption experiences due to their abilities of carrying rich and vivid information. Drawing from the perspective of information theory and the elaboration likelihood model (ELM), this study investigates impacts of visual information and visual appearance of user-generated photos (UGPs) on customers’ perceived review helpfulness. We utilize deep learning techniques to calculate the breadth and depth of photos (visual information), and evaluate the aesthetic value of the photos (visual appearance). By collecting a substantial amount of review, reviewer and restaurant information from the Yelp platform, our results demonstrate that the visual breadth and visual depth of review photos have a significant positive impact on review helpfulness. Notably, reviewer status and review length moderate these effects. These insights offer valuable strategies for both restaurant managers and online restaurant platforms regarding UGP management.

Consumer attitude research regarding food hypersensitivity

BackgroundFood hypersensitivity (FHS) is a growing trend among consumers who require special kinds of foods from the commercial and hospitality sectors. These expectations cause hardship for the trader and service provider. To explore the current situation and potential trends we prepared a survey. MethodThe survey was performed in Hungary in August 2022. Our sample consisted of 1000 respondents and was representative based on gender, region and type of municipality. We analysed the data with descriptive statistic methods and in addition, we focused on relationship analysis, to explore the most important relationships between the effects and background variables. ResultsIn our research, we investigated which kinds of FHS are frequent, and which of these are medically confirmed or self-diagnosed. We found that the respondents follow a diet more often than as a result of medically diagnosed FHS. The most common ingredients that may cause FHS are milk and cereals containing gluten. Consumers affected by FHS have to be more conscious, so as our research shows, they read the product label more frequently than those who are not affected. Respondents are less satisfied with the allergen information in restaurants and from the staff of food stores and it is difficult to find an appropriate hotel with FHS. Fear of health damage because of FHS means a moderate problem for the respondents and it depends on the kind of FHS and its symptoms. ConclusionWe found that providing appropriate information is essential to satisfying special needs. Since FHS is a significant trend, this HoReCa industry has to be better prepared to cater for consumers with special needs. The education of consumers is important as well as the development of the HoReCa sector too.

Estimation of plates impression by restaurant information on the gourmet website

The selection of plates plays a significant role as a means of presentation that enhances a dish’s attractiveness. The Quality of Dish (QoD), a fulfilling experience of eating, improves by a selection of plates depending on the theme of the dining area. However, selecting the proper plates for a specific meal can be daunting for ordinary individuals, underscoring the need for a system to simplify this process. The research aims to create a system that supports plate selection. Such a system would recommend plates based on the impression assigned to each plate. However, collecting knowledge to use such a system is problematic. To address the problem, we focused on the restaurant information on the gourmet site. We set up the hypothesis that the view from the restaurant inside and the plates used in the restaurant give the same impression. We got word of mouth and dish pictures from the gourmet site based on the hypothesis. Using comments on restaurant impressions, we created a dataset of plates to which impressions were given. We tried assigning plates the impression by calculating the degree of similarity. In this paper, we examine the validity of measuring similarity between plates based on image features and the degree of agreement between impressions of similar plates. The results suggest that the proposed similarity calculation method has room for further study The impressions given to the plates by the proposed method were also seen in similar plates to a certain degree.

The relationship between hotel star rating and website information quality based on visual presentation.

The hotel industry is essential for tourism. With the rapid expansion of the internet, consumers only search for their desired keywords on the website when they trying to find a hotel to stay, causing the relevant hotel information would appear. To quickly respond to the changing market and consumer habits, each hotel must focus on its website information and information quality. This study proposes a novel methodology that uses rough set theory (RST), principal component analysis, t-Distributed Stochastic Neighbor Embedding (t-SNE), and attribute performance visualization to explore the relationship between hotel star ratings and hotel website information quality. The collected data are based on the star-rated hotels of the Taiwanstay website, and the checklists of hotel website services are used to obtain the relevant attributes data. The results show that there are significant differences in information quality between hotels below two stars and those above four stars. The information quality provided by the higher star hotels was more detailed than that offered by low-star hotels. Based on the attribute performance matrix, the one-star and two-star hotels have advantage attributes in their landscape, reply time, restaurant information, social media, and compensation. Furthermore, the three-five star hotels have advantage attributes in their operational support, compensation, restaurant information, traffic information, and room information. These results could be provided to the stakeholders as a reference.

EXPLORING CONSUMER BEHAVIOUR PATTERNS AND MODELLING CHURN PREDICTION IN THE FOOD DELIVERY SERVICE INDUSTRY: A CASE STUDY

In the current market, businesses are always looking for ways to sustain and maintain their stability. One of the key strategies is to maintain consumer loyalty and create a peak consumer experience. The study focuses on a third-party delivery service that collaborates with restaurants and fast-food stalls, which is finding it increasingly challenging to maintain individual relationships with its growing consumer base. To address this issue, the study uses churn analysis to extract semantic consumer behavioural information from transaction details, restaurant information, and consumer profiles. The churn analysis process involves six stages: forming objectives, data acquisition, data pre-processing, machine learning, data visualization, and data reporting. Datasets were obtained from a food delivery company and pre-processed using various techniques, including data cleaning, data integration, data transformation, and data reduction. During data pre-processing, the churn period was defined, and various machine learning algorithms such as Artificial Neural Network, Support Vector Machine, AdaBoost Classifier, Gradient Boosting Classifier, XGboost Classifier, and Deep Neural Network were used to predict outcomes. The models were evaluated using metrics such as Area Under the Curve, Confusion Matrix, and Receiver Operating Characteristic. The testing of classifiers was performed with varying feature reduction methods. The findings provide valuable insights into the churn analysis process and how it can be used to extract semantic consumer behavioural information, which can help improve consumer retention and satisfaction, leading to increased revenue and business growth.

A multi-country comparison of jurisdictions with and without mandatory nutrition labelling policies in restaurants: analysis of behaviours associated with menu labelling in the 2019 International Food Policy Study.

To examine differences in noticing and use of nutrition information comparing jurisdictions with and without mandatory menu labelling policies and examine differences among sociodemographic groups. Cross-sectional data from the International Food Policy Study (IFPS) online survey. IFPS participants from Australia, Canada, Mexico, United Kingdom and USA in 2019. Adults aged 18-99; n 19 393. Participants in jurisdictions with mandatory policies were significantly more likely to notice and use nutrition information, order something different, eat less of their order and change restaurants compared to jurisdictions without policies. For noticed nutrition information, the differences between policy groups were greatest comparing older to younger age groups and comparing high education (difference of 10·7 %, 95 % CI 8·9, 12·6) to low education (difference of 4·1 %, 95 % CI 1·8, 6·3). For used nutrition information, differences were greatest comparing high education (difference of 4·9 %, 95 % CI 3·5, 6·4) to low education (difference of 1·8 %, 95 % CI 0·2, 3·5). Mandatory labelling was associated with an increase in ordering something different among the majority ethnicity group and a decrease among the minority ethnicity group. For changed restaurant visited, differences were greater for medium and high education compared to low education, and differences were greater for higher compared to lower income adequacy. Participants living in jurisdictions with mandatory nutrition information in restaurants were more likely to report noticing and using nutrition information, as well as greater efforts to modify their consumption. However, the magnitudes of these differences were relatively small.

Restaurant recommendation model using textual information to estimate consumer preference: evidence from an online restaurant platform

研究目的关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。研究方法我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。研究发现我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。研究创新/价值据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

외식업 경영자의 배달 앱 활용을 통한 지각된 가치가 앱 서비스 행동 의도에 미치는 영향

Recently, restaurant consumers obtain restaurant information through social media restaurant-related companies as well as information obtained from people around them when choosing a restaurant, and various restaurant search activities using SNS are being conducted. In the previous study, research was conducted focusing on consumers' purchase behavioral intentions according to the emotional marketing of offline stores operated by restaurant-related companies, so research on experience marketing and co-experiences that can be felt online and offline through SNS in the restaurant field is insufficient. Therefore, in this study, after investigating the effect of four sub-factors of dining out-related experience marketing on co-experience through SNS, we will identify and study how the co-experience influenced by experience marketing is related to behavior intention. As a result of the study, sensory, emotional, behavior-cognitive, and relational experiences, which are sub-variables of experiential marketing, were found to have a significant effect on co- experience-participation. Emotional, behavior-cognitive, and relational marketing, which are sub-variables of experiential marketing, were found to have a significant effect on co-experience-sympathy/sharing, but sensory experience was found to have no significant effect. Participation and empathy/sharing, which are sub-variables of co-experience, were found to have a significant effect on action intention. Emotional, behavior-cognitive, and relational marketing, which are sub-variables of experiential marketing, were found to have a significant effect on behavioral intention, but sensory experience was found to have no significant effect.

수정된 IPA를 활용한 공공 배달앱 연구

The purpose of this study was to identify the following strategic priorities of public delivery app selection attributes. This study was conducted to find ways to improve and revitalize the satisfaction of public delivery app through Revised IPA analysis related to customers using public delivery app. The survey period was conducted from August 2 to September 2, 2022, and 262 structured questionnaires were distributed to customers who had experience using the public delivery app Mukkkebi. In order to find out the selection attributes of the public delivery app, a survey was conducted by classifying it into 25 items. The questionnaire used a Likert 5-point scale and 250 responses were effective samples. Revised IPA analysis results: variables such as order processing speed, accurate delivery, easy order/payment procedures, and various payment methods are included in the first quadrant, and variables such as restaurant details, easy coupon registration/use, easy product change/procedure, and food details are included in the second quadrant. Variables such as rapid feedback on reviews, providing review/discount events, and various restaurant information are included in the third quadrant. Based on the results of this study, it is expected to be used as useful data for marketing planning of public delivery app by suggesting improvement and activation plans.

Website System Design Using Agile Kanban Based On QR Code

Business developments that occur today cannot be separated from the role of technology and information systems. The system that will be discussed in this journal is a Web-based Restaurant System that uses a QR code. This system will handle at least 5 types of users, namely admin, waiter, chef, cleaning service, and customers where the five kind of users have different access rights. Restaurant information systems using this QR Code can speed up performance in ordering menus, serving ordered dishes, paying bills to customers and cleaning tables after use. The use of the QR code itself is very suitable in the restaurant business and marketing practices because of its advantages, namely being mobile fried and easy to get information. This will improve the quality of the restaurant in terms of service and time. This Web-Based Restaurant Menu Ordering Application is designed using web programming languages, namely PHP. For the database, it will use MySQL and the system development method will use the Agile Kanban method which can help an organization produce software that has been tested and is ready to use. The results of the system design will be in the form of a comparison table for the old and new systems depicted by the PIECES table, then UML for system modeling, and website interface. The system created will help in reducing the amount of paper used for order receipts. In addition, users can also order menus during the COVID-19 pandemic with restaurants that use this system.

Consumption Frequency and Purchase Locations of Foods Prepared Outside the Home in Australia: 2018 International Food Policy Study

BackgroundFoods prepared outside the home (e.g., fast-food chains, restaurants) represent increasing proportions of diets worldwide, and have been associated with higher energy intakes and BMIs. To improve the healthiness of population diets, it is important to understand patterns of consumption of these foods, and whether related policy measures are effective. ObjectivesThis study aimed to identify the frequency and sources of consumption of foods prepared outside the home in Australia, and to understand the impact of nutrition information in restaurants on related food choices. MethodsData were from a web-based survey (the International Food Policy Study) completed in 2018 by Australian adults aged ≥18 years (n = 4103). The number of meals prepared outside the home, their purchase locations, and the extent to which nutrition information was noticed and influenced purchasing decisions were each analyzed by sociodemographic characteristics and BMI, with linear models also adjusted for sex, age group, education, ethnicity, and BMI. ResultsAn average of 2.73 (95% CI, 2.61–2.86) meals per week were prepared outside the home, with higher frequencies among men, younger ages, and more highly educated participants. A wide variety of sources for these foods was observed, with fast-food outlets being most common. Around one-quarter of all foods prepared outside the home were delivered. A small percentage (14.9%; 95% CI, 13.3%–16.7%) of participants reported noticing nutrition information, but among those who did, around half reported that it influenced their behavior. ConclusionsFoods prepared outside the home are commonly purchased in Australia, particularly by young adults, from a variety of outlet types. While current menu energy labeling regulations may provide some population health benefit, a broader policy focus on foods prepared outside the home is needed as part of efforts to improve population diets.

Penerapan Metode Supply Chain Management Sistem Informasi Restoran

One of the trending business opportunities and much in demand by the public is in the culinary business. Many people are competing to open a restaurant because food is a basic need that must be met by humans, so opening a restaurant will certainly provide many benefits for restaurant owners. In a restaurant business process, of course, it involves many roles such as the role of a waiter, the role of a chef, the internal role of the raw material stock section, and so on are interrelated and must be synchronized. However, in practice, restaurants still use conventional methods that are inefficient, effective, and structured. Some restaurants in the city of Medan only use conventional records both in ordering and recording raw material stocks so that errors often occur due to the inaccurate calculation process. In addition, the business processes in a restaurant are still not well structured between each working part so that ordering errors often occur. Therefore, research will be conducted to build a restaurant information system using the Supply Chain Management method. The results showed that the application of the Supply Chain Management method to the restaurant information system that was built was proven to be able to integrate all parts of the restaurant business properly so as to prevent errors from occurring by every party working in the restaurant.

Information and communication technologies in food services and restaurants: a systematic review

PurposeInformation and communication technologies (ICTs) are a key player in the food services and restaurants sector; thus, the aim of this work consists in studying the previous research on ICTs in food services and restaurants in the context of tourism and hospitality through a systematic literature review.Design/methodology/approachThe systematic literature review is performed on full papers published in journals included in the Journal Citation Report of the WoS in the category of Hospitality, Leisure, Sport and Tourism. A total of 165 articles from 28 journals are analyzed, following different criteria, such as the research methods, perspectives, statistical techniques, geographical focus, topics, technologies, authors and universities.FindingsThe restaurant sector is more and more based on the creation of experiences and ICTs, through their multiple possibilities, can undoubtedly contribute to adding value to the simple meal and create and recreate experiences to attract and retain customers who are increasingly sophisticated and hooked on ICTs. ICTs are basic for managers taking decision at the highest level in food services and restaurants, so ICTs should not be seen as a technical tool but as an essential element for top management.Research limitations/implicationsThis paper examined articles from very well-known tourism and hospitality journals, leaving aside others as well as different publication formats such as books or papers presented at conferences.Originality/valueA significant contribution made with this paper is the availability of a list of topics in the context of ICTs in food services and restaurants. These topics are classified into three areas (Consumers, Suppliers and Environment and Tendencies) that can serve as a future research framework. The paper also provides useful information to restaurant managers about ICTs, to researchers for their future projects and to academics for their courses.

Analyzing the Impact of Components of Yelp.com on Recommender System Performance: Case of Austin

As people’s demand for eating out is steadily increasing, the number of restaurants is continuously increasing, and catering industry platforms such as Yelp, Open Table, and Zomato provide basic information and evaluation information of restaurants and restaurant recommendation services suitable for users. Existing research on recommending restaurants mainly uses only evaluation information to find neighbors, and the use of user and restaurant information is still in its infancy. In addition, there is little study on how various types of input information affect the performance of the recommender system. This study examines the influence of three component information provided by Yelp.com on the performance of the recommender system using various real restaurants, reviews, and users dataset provided by Yelp.com. For this purpose, Two Phase Experiment was designed, and restaurant data located in Austin, Texas, USA, which has the largest number of review data, was collected. As a result of the experiment, elite status, the cumulative number of reviews, price range and average rating of restaurants could improve the recommendation performance.

A Study on the Sustainable Use Intention of Restaurant Companies Using the Information Attributes of SNS: The Dual Process Theory

This study was conducted to define detailed factors by combining the factors of SNS (social network services) information attributes and dual processing process theory and to investigate the relationship between customer satisfaction, brand attitude, and sustainable use intention. An empirical analysis was conducted using data collected from 361 participants who have experience in SNSs for searching for restaurant information. The major results are as follows: First, two intuitive SNS information attributes (reliability and liveliness) and two analytical attributes (usefulness and conciseness) have a significant positive influence on customer satisfaction to use an SNS. In particular, the reliability was the most significant antecedent of the SNS information attributes in this study. In addition, the current study verified the significant relationships among customer satisfaction, brand attitude, and sustainable use intention. Based on these research results, it was verified that SNS information characteristics are important attributes in eliciting customer satisfaction, attitude toward the brand, and sustainable use intention from customers. This study will contribute to providing various practical implications to establish valuable marketing strategies.

The Impact of Restaurant Recommendation Information and Recommendation Agent in the Tourism Website on the Satisfaction, Continuous Usage, and Destination Visit Intention

Enjoying local food could be one of the motives for tourism, and local food and restaurant recommendation information would be important for tourists to decide their destination. Recommendation agents are the sorting and searching function to find the best local food and restaurant among the complexity of information, and they could also be helpful for the tourist to decide their destination. Online tourism websites (e.g., Ctrip.com ) started to provide restaurant recommendations containing food-related information and recommendation agents to attract tourists. However, few studies have investigated their impact on the destination visit intention of potential Chinese tourists. This study aims to empirically validate how restaurant recommendation information, including food-related information and recommendation agents, could impact online tourists’ reactions, such as satisfaction, continuous website usage, and destination visits. We developed our hypothesis based on the information system (IS) success model. We gathered 202 data points from potential tourists using quasi-experimental methods, and these data were analyzed by the PLS algorithm. The results indicate that restaurant recommendation information and recommendation agents significantly increase the perceived information quality and perceived system quality. Increased perceived information quality and system quality could significantly increase potential tourists’ satisfaction, website continuous usage intention, and destination visit intention. The results of this study could contribute to making tourism websites more attractive by using local food and restaurant information and recommendation agents.

방한 베트남 관광객의 정보탐색행동과 만족도에 관한 연구

The purpose of this study is to analyze the information content and information source used by Vietnamese tourists visiting Korea. And this study researches whether there is a difference in travel satisfaction according to their information search behavior. For analysis, raw data of 2019 international visitor survey by Ministry of Culture, Sports and Tourism were used.<BR> The findings are as follows. The first, the information that Vietnamese tourists mainly search for their trip to Korea are tourist destination information, food and restaurant information, travel distance and transportation, shopping place, and accommodation. The sources of information that Vietnamese tourists use most before traveling are global website/apps, relatives, friends and colleagues. And the Internet (smartphone) is the most frequently used source of information by Vietnamese tourists during their trip. The second, depending on the information search behavior (information content and information source) of Vietnamese tourists, there are statistically significant differences in overall satisfaction with Korean travel and some itemized satisfaction.<BR> These empirical results will be helpful to analyze the Vietnamese and other inbound tourists visiting Korea and help develop more effective marketing strategies for them.

Designing of Restaurant Information System using Rapid Application Development

Indonesia is one of the largest culinary attractions in Southeast Asia. The culinary diversity in Indonesia encourages stakeholders to create restaurant businesses with effective and efficient business processes. One of the efforts to make the restaurant business process effective and efficient is to build a restaurant management information system. By using a system, business actors will be able to control and evaluate their business with appropriate reports. For this reason, the purpose of this study is to build a restaurant management information system using the RAD (Rapid Application Development) method, because building this application requires a systematic, structured, and object-oriented process. The RAD method emphasizes a high quality systems, fast development and delivery and low costs development process [1] so that this process requires a serious role from business actors to build a restaurant management information system. The results of this system design can make it easier for business actors to control the business process from the customer to the kitchen so that the food and beverage ordering process will be served quickly. The GUI restaurant management information system is made interactively so that customers and business actors can easily understand the system. The test results of this system use black-box testing, with the conclusion that the system is successful to run.

Richard Sorge: Leading the Intelligence Network in Japan (1933-1941)

The public of the West learned about the activities of the outstanding Soviet intelligence agent Richard Sorge immediately after the end of the Second World War, thanks to the assessments of his activities in American sources. In the USSR, he became widely known only in 1964 thanks to Nikita Khrushchev. The article shows that the combination of personal and professional qualities of Sorge, ideological motivation allowed him to achieve outstanding results in intelligence activities. It is noted that under his leadership, illegal residencies in China, and then in Japan, received unique intelligence information, which helped the leadership and military command of the USSR to make informed political and military-strategic decisions. Richard Sorge was one of the most prominent intelligence officers in the history of the intelligence services. The authors describe facts that characterize the daily work of an intelligence officer: how he headed residencies in extremely difficult countries for work, successfully managed agent networks, personally conducted recruiting work, collected important intelligence information, analyzed it and prepared reports to Moscow. The organization of networks led by Sorge has been studied by many foreign intelligence services and has consistently been highly rated. In publications dedicated to Sorge, two approaches to creating his image are clearly traced, which is noted by the authors of the article. The first approach presents him as the archetype of a movie hero, agent and super spy, receiving classified information in alcoves and restaurants. It is designed for the general reader and first appeared in the West. Another image, partially ideologized and propagandistic, spread in the USSR and East Germany. He presented Richard as a knight of the revolution without fear or reproach. In this article, the authors made an attempt to create a real and objective assessment of his activities, in particular in Japan. The authors of the article, using Western and new Russian sources, have chosen the method of historical psychology in order to represent the way of thinking of this outstanding person and connect it with his actions in leading the group entrusted to him.