Chinese Restaurant Research Articles

Calorie and carbon labels on menus in Chinese restaurants: effects of label presence and presentation format on customer behavior and brand perceptions

ABSTRACT This study employed a 2 (calorie and carbon emission) × 2 (numeric and reference) experimental design to assess the impact of displaying nutritional and environmental information on menus in hotel’s Chinese restaurant. Unlike previous studies primarily focused on simulated intention, it captured consumer actual behavior and perception. While such information did not significantly affect dietary choices, the inclusion of carbon emission information had a positive influence on perceived social responsibility. The reference format proved effective in enhancing transparency, especially when the information was complex. This research offers practical insights for restaurants to effectively communicate their commitment to health and sustainability.

Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization.

A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the reinforcement learning (RL) agent's behavior as the environment changes over its lifetime while minimizing the catastrophic forgetting of the learned information. To address this challenge, in this article, we propose DaCoRL, that is, dynamics-adaptive continual RL. DaCoRL learns a context-conditioned policy using progressive contextualization, which incrementally clusters a stream of stationary tasks in the dynamic environment into a series of contexts and opts for an expandable multihead neural network to approximate the policy. Specifically, we define a set of tasks with similar dynamics as an environmental context and formalize context inference as a procedure of online Bayesian infinite Gaussian mixture clustering on environment features, resorting to online Bayesian inference to infer the posterior distribution over contexts. Under the assumption of a Chinese restaurant process (CRP) prior, this technique can accurately classify the current task as a previously seen context or instantiate a new context as needed without relying on any external indicator to signal environmental changes in advance. Furthermore, we employ an expandable multihead neural network whose output layer is synchronously expanded with the newly instantiated context and a knowledge distillation regularization term for retaining the performance on learned tasks. As a general framework that can be coupled with various deep RL algorithms, DaCoRL features consistent superiority over existing methods in terms of stability, overall performance, and generalization ability, as verified by extensive experiments on several robot navigation and MuJoCo locomotion tasks.

Blocked Gibbs Sampler for Hierarchical Dirichlet Processes

Posterior computation in hierarchical Dirichlet process (HDP) mixture models is an active area of research in nonparametric Bayes inference of grouped data. Existing literature almost exclusively focuses on the Chinese restaurant franchise (CRF) analogy of the marginal distribution of the parameters, which can mix poorly and has a quadratic complexity with the sample size. A recently developed slice sampler allows for efficient blocked updates of the parameters, but is shown to be statistically unstable in our article. We develop a blocked Gibbs sampler that employs a truncated approximation of the underlying random measures to sample from the posterior distribution of HDP, which produces statistically stable results, is highly scalable with respect to sample size, and is shown to have good mixing. The heart of the construction is to endow the shared concentration parameter with an appropriately chosen gamma prior that allows us to break the dependence of the shared mixing proportions and permits independent updates of certain log-concave random variables in a block. En route, we develop an efficient rejection sampler for these random variables leveraging piece-wise tangent-line approximations. Supplementary materials, which include substantive additional details and code, are available online.

Markov chains generating random permutations and set partitions

The Chinese Restaurant Process may be considered to be a Markov chain which generates permutations on n elements proportionally to absorption probabilities θ|π|, θ>0, where |π| is the number of cycles of permutation π. We prove a theorem which provides a way of finding Markov chains, restricted to directed graphs called arborescences, and with given absorption probabilities. We find transition probabilities for the Chinese Restaurant Process arborescence with variable absorption probabilities. The method is applied to an arborescence constructing set partitions, resulting in an analogue of the Chinese Restaurant Process for set partitions. We also apply our method to an arborescence for the Feller Coupling Process. We show how to modify the Chinese Restaurant Process, its set partition analogue, and the Feller Coupling Process to generate derangements and set partitions having no blocks of size one.

The Potential and Limits of Spontaneous Labor Activism on Social Media: The Search for Justice among Chinese Restaurant Workers

Recent scholarship on social media in labor activism has centered on institutions such as labor unions. What is understudied is how “unorganized” workers use social media to reclaim agency. Employing digital ethnography this study explores the use of WeChat, an all-in-one Chinese mobile application, among Chinese restaurant workers to share stories of mistreatment and deteriorating work conditions in the United States. Tracing the development of a blacklist of Chinese/Asian fusion restaurants that garnered the interest of tens of thousands of Chinese restaurant workers, this article reveals the potential and limitations of social media as a medium for labor activism.

Chinese Restaurants and Neighborhood Change in New York City

This article analyzes the relationship between the presence of Chinese restaurants and neighborhood change in New York City. The core metric used for analysis in this article is a Chinese restaurant index, which is a transformed and scaled percentage of restaurants in a neighborhood that can be classified as Chinese. As an inexpensive and plentiful dining option, Chinese restaurants have an association with working-class neighborhoods, and neighborhood-level Chinese restaurant index decreases over time were associated with gentrification in New York City. This methodology further demonstrates the potential for the use of retail-based analysis as a supplement to conventional gentrification metrics based on demographic and real estate data.

Graph-Aligned Random Partition Model (GARP)

Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications such as inference on cell lineage due to the biological relations of the clusters. The increasing availability of large genomic data requires new statistical tools to perform model-based clustering and infer the relationship between homogeneous subgroups of units. Motivated by single-cell RNA data we develop a novel dependent mixture model to jointly perform cluster analysis and align the clusters on a graph. Our flexible graph-aligned random partition model (GARP) exploits Gibbs-type priors as building blocks, allowing us to derive analytical results for the probability mass function (pmf) on the graph-aligned random partition. We derive a generalization of the Chinese restaurant process from the pmf and a related efficient and neat MCMC algorithm to implement Bayesian inference. We illustrate posterior inference under the GARP using single-cell RNA-seq data from mice stem cells. We further investigate the performance of the model in recovering the underlying clustering structure as well as the underlying graph by means of simulation studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

How service quality affects consumer revisit behavior in chain restaurants: Lessons from COVID-19

Comparison studies in the context of Taiwanese and Chinese restaurant chains remain scarce. This research utilized a comparison study to examine the key distinctions between Taiwanese and Chinese restaurant chains in their response to COVID-19 pandemic. Additionally, this paper also focused on the indirect effects of service quality on customer revisit intention during pandemic. The first study involved a renowned Taiwanese restaurant chain and revealed that service quality impacts revisit intention through brand equity and safety management system. The second study involved a popular Chinese hotpot restaurant chain and demonstrated that service quality affects revisit intention through gratification and reputation. Additionally, the moderating effects of safety perceived and perceived value were considered critical on revisit intention. This study highlighted adapting to local market conditions while prioritizing safety and customer satisfaction amid pandemic. It enriches academic discourse and offer practical insights for post-pandemic business recovery strategies.

SWOT Analysis of Haidilao in Macau

As we all know since 2020, Macau and even Chinese restaurant industry development are in a bottleneck. But in Taipa Londoner, there is such a chain restaurant that not only does not have a serious decline in performance as the general trend, but even opened a second restaurant in Venetian Macau, it is Haidilao Hot Pot. Therefore, our group has chosen this restaurant chain as the object of our study, through the introduction of the company's background, analysis of the business model that motivates the company's owner and the competition, as well as swot analysis to introduce the reasons for the success of Haidilao in Macau, and give some suggestions for the analysis. At the same time, during the introduction we will mention the hot pot brand CouCou, the chain brand McDonald's to make a comparison.

Robots or humans: Who is more effective in promoting hospitality services?

Although consumers’ evaluation and adoption of robotic services have been widely investigated, the effectiveness of robotic sales promotion in the hospitality industry remains unclear. Utilizing the benefit congruency framework, this study proposes that robotic (human) promotion is more suitable for utilitarian (hedonic) benefits. Based on an experiment with and field data provided by a Chinese restaurant chain, this study demonstrates that robots perform better than humans in monetary promotion (e.g., price discounts) that offers utilitarian benefits but not in nonmonetary promotion (e.g., extra dish) associated with hedonic benefits. Moreover, an anthropomorphic language style can reduce the performance difference between robots and humans in nonmonetary promotion. Accordingly, this study’s findings inform hospitality marketers that humans and robots should be in a cooperative rather than substitutive relationship in sales promotion.

Another view of sequential sampling in the birth process with immigration

We explore properties of the family sizes arising in a linear birth process with immigration (BI). In particular, we study the correlation of the number of families observed during consecutive disjoint intervals of time. Letting S(a, b) be the number of families observed in (a, b), we study the expected sample variance and its asymptotics for p consecutive sequential samples Sp=(S(t0,t1),⋯,S(tp-1,tp))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_p =(S(t_0,t_1),\\dots , S(t_{p-1},t_p))$$\\end{document}, for 0=t0<t1<⋯<tp\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0=t_0<t_1<\\dots <t_p$$\\end{document}. By conditioning on the sizes of the samples, we provide a connection between Sp\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S_p$$\\end{document} and p sequential samples of sizes n1,n2,⋯,np\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n_1,n_2,\\dots ,n_p$$\\end{document}, drawn from a single run of a Chinese Restaurant Process. Properties of the latter were studied in da Silva et al. (Bernoulli 29:1166–1194, 2023. https://doi.org/10.3150/22-BEJ1494). We show how the continuous-time framework helps to make asymptotic calculations easier than its discrete-time counterpart. As an application, for a specific choice of t1,t2,⋯,tp\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$t_1,t_2,\\dots , t_p$$\\end{document}, where the lengths of intervals are logarithmically equal, we revisit Fisher’s 1943 multi-sampling problem and give another explanation of what Fisher’s model could have meant in the world of sequential samples drawn from a BI process.

Clustering spatial functional data using a geographically weighted Dirichlet process

AbstractWe propose a Bayesian nonparametric clustering approach to study the spatial heterogeneity effect for functional data observed at spatially correlated locations. We consider a geographically weighted Chinese restaurant process equipped with a conditional autoregressive prior to capture fully the spatial correlation of function curves. To sample efficiently from our model, we customize a prior called Quadratic Gamma, which ensures conjugacy. We design a Markov chain Monte Carlo algorithm to infer simultaneously the posterior distributions of the number of groups and the grouping configurations. The superior numerical performance of the proposed method over competing methods is demonstrated using simulated examples and a U.S. annual precipitation study.

Food waste from various origins as feedstocks for recombinant protein production by Yarrowia lipolytica using in situ fibrous bed bioreactor

Recombinant proteins (rProts) hold diverse applications and exhibit promising market potential. However, the impediment to their industrial production lies in the high associated costs, notably those related to the medium components. This study focuses on the heterologous production of the lipase B (CalB) from Candida antarctica, used as a case study, from food waste by Yarrowia lipolytica. Various food waste hydrolysates (FWHs) were assessed, and Chinese restaurant FWH emerged as the most suitable feedstock for both cell growth and rProt production. Employing a response surface methodology, the culture conditions and medium compositions were optimised, resulting in a 139.4% increase in lipase production in shake flasks, yielding a titre of 6.32 × 103 U/mL and a productivity of 65.83 U/(mL·h). The lipase titre and productivity achieved in a 2.5-L stirred-tank bioreactor were subsequently increased by 81.0% and 122.8%, respectively. In an in situ fibrous bed bioreactor (isFBB) with cells immobilised on sugarcane bagasse, a pulsed fed-batch strategy led to further enhancement, reaching a lipase productivity of 265.63 U/(mL⋅h). This represents an increase of 80.7% compared to a similar process using glycerol as a feedstock. This study demonstrates clearly that FWHs can be used for rProt production using Y.lipolytica as a cell factory.

RHDP: An Aspect Sharing-Enhanced Hierarchical Topic Model for Multi-Domain Corpus

Learning topic hierarchies from a multi-domain corpus is crucial in topic modeling as it reveals valuable structural information embedded within documents. Despite the extensive literature on hierarchical topic models, effectively discovering inter-topic correlations and differences among subtopics at the same level in the topic hierarchy, obtained from multiple domains, remains an unresolved challenge. This article proposes an enhanced nested Chinese restaurant process (nCRP), nCRP+, by introducing an additional mechanism based on Chinese restaurant franchise (CRF) for aspect-sharing pattern extraction in the original nCRP. Subsequently, by employing the distribution extracted from nCRP+ as the prior distribution for topic hierarchy in the hierarchical Dirichlet processes (HDP), we develop a hierarchical topic model for multi-domain corpus, named rHDP. We describe the model with the analogy of Chinese restaurant franchise based on the central kitchen and propose a hierarchical Gibbs sampling scheme to infer the model. Our method effectively constructs well-established topic hierarchies, accurately reflecting diverse parent-child topic relationships, explicit topic aspect sharing correlations for inter-topics, and differences between these shared topics. To validate the efficacy of our approach, we conduct experiments using a renowned public dataset and an online collection of Chinese financial documents. The experimental results confirm the superiority of our method over the state-of-the-art techniques in identifying multi-domain topic hierarchies, according to multiple evaluation metrics.

Comparative Analysis of Improved Dirichlet Process Mixture Model

Due to the development of information technology, large amounts of data are generated every day in various industries such as engineering, healthcare, finance, anomaly detection, image recognition, and artificial intelligence. This massive data poses the challenge of analyzing accurately and appropriate classifications. The traditional clustering methods require specifying the number of clusters and are mostly based on distance, which cannot effectively consider the correlations between different indicators of high-dimensional and multi-source data. Moreover, the number of clusters cannot automatically adjust when new data is generated. In order to improve the clustering analysis of high-dimensional and multi-source data in a big data environment, this study utilizes non-parametric mixture models based on distribution clustering, which does not require specifying the number of clusters and can auto update with the data. By combining Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and the non-parametric Bayesian method called Dirichlet Process Mixture Model (DPMM), the Bayesian non-parametric PCA model (PCA-DPMM) and Bayesian non-parametric t-SNE model (TSNE-DPMM) are proposed. The Chinese restaurant process of DPMM is used for sampling by introducing a finite normal mixture distribution. The clustering results on the iris dataset are compared and analyzed. The accuracy of DPMM and TSNE-DPMM reaches 0.97, while PCA-DPMM achieves a maximum accuracy of only 0.94. When different numbers of iterations are set, TSNE-DPMM maintains an accuracy ranging from 0.92 to 0.97, DPMM ranges from 0.66 to 0.97, and PCA-DPMM ranges from 0.73 to 0.94. Therefore, the proposed TSNE-DPMM ensures accuracy and exhibits better model stability in clustering results. Future research can explore the improvement of the model by incorporating deep learning algorithms, among others, to further enhance its performance. Additionally, applying the TSNE-DPMM model to data analysis in other fields is also a future research direction. Through these efforts, we can better tackle the challenges of analyzing high-dimensional and multi-source data in a big data environment and extract valuable information from it.

A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning.

While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interference when faced with lifelong streaming information. In this article, we propose a scalable lifelong RL method that dynamically expands the network capacity to accommodate new knowledge while preventing past memories from being perturbed. We use a Dirichlet process mixture to model the nonstationary task distribution, which captures task relatedness by estimating the likelihood of task-to-cluster assignments and clusters the task models in a latent space. We formulate the prior distribution of the mixture as a Chinese restaurant process (CRP) that instantiates new mixture components as needed. The update and expansion of the mixture are governed by the Bayesian nonparametric framework with an expectation maximization (EM) procedure, which dynamically adapts the model complexity without explicit task boundaries or heuristics. Moreover, we use the domain randomization technique to train robust prior parameters for the initialization of each task model in the mixture; thus, the resulting model can better generalize and adapt to unseen tasks. With extensive experiments conducted on robot navigation and locomotion domains, we show that our method successfully facilitates scalable lifelong RL and outperforms relevant existing methods.

LoRa-based outdoor localization and tracking using unsupervised symbolization

This paper proposes a long-range (LoRa)-based outdoor localization and tracking method. Our method presents an unsupervised localization approach that utilizes symbolized LoRa received signal features, such as RSSI, SNR, and path loss, where each symbol represents a system state. To identify the partitioning boundaries between the symbols in time series, we employ maximum entropy partitioning. The D-Markov machine is used to construct nondeterministic finite-state automata for extracting temporal patterns. We incorporate the Chinese restaurant process for online estimation, especially in scenarios with an unbounded number of probable areas around each LoRa gateway. An adaptive trilateration approach is then used to localize the target node from the estimated ranged radii of areas. The point-wise localization data was used for time-series continuous tracking. We collected a dataset using three LoRaWAN gateways, sensor nodes powered by single-use batteries, and a Chirpstack server on a sports oval. We thoroughly evaluated the proposed method from the perspectives of localization accuracy and tracking capability. Our method outperformed state-of-the-art machine learning-driven range-based and fingerprint-based localization techniques.

A multilevel investigation of the impact of error management culture on restaurant employee voice

PurposeThis study aims to examine the relation between error management culture and restaurant employee promotive and prohibitive voices. Drawing on socially desirable responding theory, the authors also propose a dual-mediation mechanism underlying the impact of error management culture on employee voice: psychological empowerment, as the agentic motive, and psychological safety, as the communal motive.Design/methodology/approachThe authors recruited 223 participants working in 37 restaurants in China for the two-wave surveys with a one-week interval. The authors use a multilevel modeling paradigm to test the study hypotheses.FindingsThis research examines a multilevel model suggesting that error management culture can boost employee promotive voice and prohibitive voice via the mechanisms of psychological safety and empowerment. In addition, the results suggest that psychological empowerment (vs psychological safety) has a strong mediation effect between error management culture and promotive voice, but the authors find no difference in mediating effects between error management culture and prohibitive voice.Practical implicationsRestaurants can encourage employee voice by developing and maintaining an error management culture. Organizations can also consider motivating employees from both agentic and communal perspectives. Moreover, managers should focus more on empowering employees in areas characterized by Confucianism or collectivism.Originality/valueThe current research adds to the voice literature by identifying an organizational cultural antecedent of employee voice–error management culture. Agentic and communal motives are two motivational paths of employee voice. It also extends the social desirability theory by highlighting the role of the agentic motive in the Chinese restaurant context.

CRP-Tree: a phylogenetic association test for binary traits

Abstract An important problem in evolutionary genomics is to investigate whether a certain trait measured on each sample is associated with the sample phylogenetic tree. The phylogenetic tree represents the shared evolutionary history of the samples and it is usually estimated from molecular sequence data at a locus or from other type of genetic data. We propose a model for trait evolution inspired by the Chinese Restaurant Process that includes a parameter that controls the degree of preferential attachment, that is, the tendency of nodes in the tree to subtend from nodes of the same type. This model with no preferential attachment is equivalent to a structured coalescent model with simultaneous migration and coalescence events and serves as a null model. We derive a test for phylogenetic binary trait association with linear computational complexity and empirically demonstrate that it is more powerful than some other methods. We apply our test to study the phylogenetic association of some traits in swordtail fish, breast cancer, yellow fever virus, and influenza A H1N1 virus. R-package implementation of our methods is available at https://github.com/jyzhang27/CRPTree.

Analyzing the Popularity of Hai Di Lao through Digital Marketing Methods

This paper examines the marketing strategy of Hai Di Lao, a successful Chinese hot pot restaurant chain that has achieved significant growth in recent years. This paper focuses on Haidilao's marketing strategies, specifically the effectiveness of social media, mobile app, and search engine marketing. The research methods used in this study include a literature review and analysis of articles related to social media and app marketing, as well as user behavior on different platforms. The paper also highlights the importance of online marketing methods, such as the use of online celebrities and incentivizing customers to leave comments and write reviews through different platforms, in addressing the shortcomings of Hai Di Lao's marketing efforts. This paper aims to investigate the shortcomings of Haidilao's marketing strategies and propose effective solutions to address these issues. This research can provide insights and suggestions for catering companies to improve their marketing methods and ultimately increase their sales. The suggestions are beneficial for Haidilao to generate brand awareness, attract new customers, and increase customer loyalty. The paper argues that Hai Di Lao can improve its marketing strategy by investing in social media and app marketing, using online celebrities, incentivizing customers to leave comments and write reviews, and tailoring marketing efforts to specific user groups. By implementing these strategies, Hai Di Lao can increase brand awareness, customer loyalty, and ultimately, sales.