Demographics, Weather and Online Reviews:
A Study of Restaurant Recommendations
Saeideh Bakhshi

Partha Kanuparthy

Georgia Tech

Yahoo Labs∗

sbakhshi@cc.gatech.edu

parthak@yahoo-inc.com

ABSTRACTINTRODUCTION

Taking a trip to someplace warm in the middle of winter, or being
outdoors when spring arrives, can be particularly beneficial. Research shows that pleasant weather improves mood and memory
and broadens cognitive performance, thinking and judgement [41,59]. Low levels of humidity and high levels of sunlight are associated with high mood [8, 40, 48, 50]. Demographics of a region
have been associated with people’s spending time online [21, 22]. A
common underlying aspect of these observations is that they study
exogenous factors – that are generally not considered when studying people’s online activities such as participation in recommendation communities.
∗

Georgia Techeegilbert@cc.gatech.edu

In this paper, we ask the question: might these phenomena documented in psychology studies also affect1 large-scale online behavior? Could weather and local demographics of restaurants drive
how we rate them online?
Review and recommender sites are highly popular online resources. People contribute content in the form of recommendations to these communities. A recent work by Anderson and Magruder [2] found that an extra half star rating on Yelp causes restaurants to sell out 19% more frequently. People increasingly basetheir decisions on input from such online reviews and ratings. A recent survey found that 64% of consumers search for online reviews
before spending on services [7], and 85% of them are more likely
to purchase services when they can find online recommendations.
The same study found that 87% of consumers say that positive online reviews reinforce their decisions, while 80% say that negative
online reviews have led them to change their minds. These findings
imply significant returns on an extra half-star rating or more number of reviews and suggest that restaurants have strong incentives
to improve their online reviews and ratings.
Despite their widespread use, little is known about the dynamics of participation and of contributions by people in online recommendation sites. For example, would factors such as weather
conditions, that are shown to influence mood and behavior, affectpeople’s participation and recommendations? Are restaurants fromneighborhoods with high education levels more likely to receive
reviews? Are restaurants in highly populated urban neighborhoods
more likely to be reviewed rather than restaurants in regions withlower population density? What is the role, if any, of racial diversity
in a restaurant’s neighborhood in shaping online participation?
Our insights could help consumers better understand online reviews and ratings, and aid review sites in calibrating recommendations. For example, exogenous factors may introduce systematic
bias in online ratings of a highly-reviewed restaurant in San Fransisco, compared to a similar restaurant in a rural area. Our study
aims to understand such differences – what factors can influence
the likelihood of being reviewed in a region?
Three primary aspects of a restaurant are available to consumers
via online reviews: user evaluation (overall rating), user participation (number of reviews, which also indicates popularity) and thereviews themselves (i.e., the text and rating of each review). We
study the effect of these aspects of three broad classes of factors:
restaurant attributes, local demographics and local weather conditions at the date of visit. Our study is the first to look at exogenous
factors and how they affect online ratings. We explain the importance of these classes and why we choose them in our related work.

Online recommendation sites are valuable information sources thatpeople contribute to, and often use to choose restaurants. However,
little is known about the dynamics behind participation in these online communities and how the recommendations in these communities are formed. In this work, we take a first look at online restaurant recommendation communities to study what endogenous (i.e.,
related to entities being reviewed) and exogenous factors influencepeople’s participation in the communities, and to what extent. We
analyze an online community corpus of 840K restaurants and their
1.1M associated reviews from 2002 to 2011, spread across every
U.S. state. We construct models for number of reviews and ratings by community members, based on several dimensions of endogenous and exogenous factors. We find that while endogenous
factors such as restaurant attributes (e.g., meal, price, service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education) and weather(e.g., temperature, rain, snow, season) also exert a significant effecton reviews. We find that many of the effects in online communities
can be explained using offline theories from experimental psychology. Our study is the first to look at exogenous factors and how it
related to online online restaurant reviews. It has implications fordesigning online recommendation sites, and in general, social media and online communities.

1.

Eric GilbertThis work was done before Partha Kanuparthy joined Yahoo.

Copyright is held by the International World Wide Web Conference Committee (IW3C2). IW3C2 reserves the right to provide a hyperlink to theauthor’s site if the Material is used in electronic media.
WWW’14, April 7–11, 2014, Seoul, Korea.
ACM 978-1-4503-2744-2/14/04
http://dx.doi.org/10.1145/2566486.2568021.

1
We use the term “effect” in this paper to refer to the statisticaleffect measured by regression, and not causality.

443

We study 840,000 restaurants spread across 32,402 cities andtowns in the US. We collect over 1.1 million reviews and the corresponding ratings across these restaurants, spanning 10 years. We
combine this with data on demographic information and daily weather
conditions local to each restaurant. While this is certainly not an exhaustive compilation of factors, we show that the models presented
here have significant explanatory power.
All told, we find the following:• Restaurants that are marked online as “low-price” tend to get
fewer reviews and lower ratings. At the same time, online
promotions are related with higher number of reviews, but
not necessarily higher ratings.
• Specific features of a restaurant, such as having a bar might
be associated with higher number of reviews but does notaffect the ratings. The same relationship holds for restaurantsthat are featured via online advertisements.
• Service related factors such as delivery and carry-out arestrongly tied with the population density of the neigborhood,and the interplay of the two can influence the number of reviews and ratings.
• Restaurants that are in neighborhoods with higher population
density and higher education levels are more likely to be reviewed. The education level does not seem to affect ratings.
• Restaurants in some regions of United States (e.g. Pacific andNortheast) are more likely to receive reviews compared toother regions (Midwest and South).
• There is a seasonal pattern among rating and reviews, showing lower ratings and higher number of reviews in months ofJuly and August.
• Weather conditions are significantly associated with ratings.
Reviews written on warm or cool days are more likely to be
rated high than those written in cold or hot days. Reviews
written on rainy or snowy days tend to have lower ratings
than those written on days without rain or snow.
We perform conditional analyses on our data and show examples of online review text where feasible to substantiate our claims.
Our study looks at the effect of several factors on online reviews,
but at the same time, opens a number of research directions for future work. We discuss some of these towards the conclusion of thepaper.

2.

RELATED WORK

There has been significant body of work that studied endogenous factors behind participation in online communities, and psychology of diners in the offline context. An interesting and not yet
understood socio-technical system that bridges both research areas
is online restaurant recommendation sites. Our work complements
online studies by being the first to do a large-scale study of restaurant recommendation communities, and the first to look at effects
of exogenous factors on ratings and reviews. Our work complements theories from experimental psychology by validating themin online communities.
Online communities: Several research efforts studied the factors
behind people’s participation in online communities. These studies
focused on factors that are endogenous to the online community
and its members. Ridings et al. [44] studied various types of communities to understand why people participate in them. They reportthat participation is usually a factor of the community type; andthat the common reasons behind participation include informationexchange, social support and friendship. Social loafing, a theory
that shows the effect of group size on an individual’s motivation tocontribute [29], has been observed in online communities [51].

444

Class
Food
Atmosphere
Service
Monetary
Advertising
Location
Miscellany

Example variables
cuisine, vegetarian, fine dining
ambience (romantic, quiet, view, etc.)
delivery, carryoutmenu prices, promotions
featured on websites
latitude-longitude, city/town, neighborhood
name

Table 1: Restaurant attributes in our data.
Researchers have looked at factors affecting participation in specific online communities. Lakhani and Wolf [32] studied contributors to Open Source Software (OSS) projects and found that career
benefits and intellectual stimulation drive OSS community members to contribute to the projects. Nov studied motivations behindsignificant contributors to Wikipedia content [39] and found thathaving fun, learning and emotional aspects are significant driversof contributions. Lampe et al. studied member participation in theEverything2 online encyclopedia [33] and found that a sense of belonging to the online community was a significant factor towards
participation, while social interaction was not a strong motivator.
To our knowledge, our work is the first to look at online restaurant
recommendation communities which are highly popular. While priorwork only considers endogenous predictors of online participation,
we also consider exogenous predictors (i.e., factors not related tothe community or its members), and we show that they play a rolein community participation.
An integral part of an online recommendation community is reviews written by people. Researchers have analyzed review text andfound some interesting features. Two prior studies have shown that
review text has temporal correlation patterns [18, 60] and genderdifferences [43]. David and Pinch found evidence of duplicate reviews between products [11]; and Feng et al. found common patterns in deceptive reviews [16]. Certain patterns were also discovered among helpfulness evaluation of reviews [9]. Language analysis of review text showed a significant association between language features and sales [17]. A related research thread focuses on
analysis of review text to identify and predict features: for example,
identifying fake reviews, and summarizing and predicting productratings [28, 36, 38, 43]. In this work, we consider reviews and ratings as they are available to users on recommendation sites, sinceusers make choices based on these reviews.
Offline psychology: Consumers have many dining choices, andtend to consider many factors when making their decisions [55].
Traditionally, diners have traded recommendations using offline methods (e.g., word-of-mouth). In this context, there has been a significant effort in experimental psychology to understand, using inperson experiments, how people perceive dining and the effect ofdifferent factors. Our work shows that these observations of behavior in the offline world also hold true in online recommendation
communities.
Experimental psychologists proposed theories of hedonic andutilitarian consumer experiences to understand the effect of prices,
service and restaurant ambience on consumer behavior. For example, it has been shown that 91% of consumers dissatisfied with service do not revisit a restaurant, and further, tell eight to ten others
about their negative experience [42]. Studies [15, 57] have foundthat consumers relate to ambience in an emotional (hedonic) terms,
rather than cognitively. A similar effect of price was demonstrated
by Wakefield et al. [58].
Researchers have also studied the effect of promotions on consumers, which can be summarized into three classes of approaches.
The first approach consists of analytical and empirical approacheswhich studied the effects of the price change or incentives on aggregate metrics such as sales or market share. For example, a recent
study found that online Groupon promotions can have a negative
effect on subsequent online ratings [5]. The second approach looks
at demographics of consumers who respond better to promotions[4]. The third approach studies psychological effect of promotions
on consumers’ behavior [12].
There has been work on consumers’ perceptions of quality, priceand value [62]. The authors showed that consumers perceive quality and value as being similar. A study on the perception of foodshowed that food can be considered as a social concept, one thatdefines social status, based on the quality, cost and presentation offood [3].
Exogenous factors affecting mood and behavior. Psychologists
have long known - in the offline world - that sunlight, and weather
in general, influences people’s moods, thinking and judgement [41,59]. Both seasonal and daily variations in weather have been documented to have effects on mood, depression and behavior [23,30, 46]. In a study that manipulated temperature, Allen and Fischer [1] found that performance on a paired association memorytask peaked at 72o F (22o C) and declined with warmer or cooler
temperature. Sinclair et al. [52] found that days that were sunny
and warm were associated with more heuristic and less systematic
processing than cloudy and cool days. In some studies, low levelsof humidity [48], high levels of sunlight [8, 40, 50] and high temperature [8, 27] have been associated with high mood. However,high temperature has also been associated with low mood [20] andlow potency (low potency is similar to low mood [27]).
Changes result from physical characteristics of environment, often without the organism’s awareness [45]. Cunnigham [8] foundthat weather also has an affect on helping behavior. In the study
the weather was found to have an effect on the size of the tips
that people left a waitress in a restaurant and the waitress’s self
reported mood. The study also suggested that sunshine levels affectmood through the symbolic connections it has with pleasant events.
Therefore sunshine could increase a person’s mood by stimulating
thoughts of positive and pleasant activities such as swimming, outdoor outings/activities and picnics.
Weather and lunar cycles have been found to have an effect on
stock market as well [14, 19, 25, 34, 49, 56, 61]. Saunders [49]shows that the NYSE rises more on sunny days in New York City,
resulting in lower stock returns on cloudy days.
In addition to the outside weather, differences in the indoor lighting environment (levels, spectral distribution, temporal patterns,etc.) have been found to affect people in various ways. Daurat et al.
[10] found that subjects reported a more positive mood under 2000
Lux compared to being under 300 Lux. Belcher and Kluczny [31]proposed a model in which mood, visual performance and decisionmaking strategy are affected by the visual environment and compete for mental processing capacity.
Demographics is an important factor in usage of the Internet [21,22], and so it might affect the online behavior and level of activities.
Our work builds on this research by looking for the first timeat exogenous factors affecting the online reviews. We connect the
anecdotal, experimental and theoretical studies of mood and behavior in psychology with the participation in online communities.

3.

Figure 1: Locations of weather stations in our data. We consider restaurants located within a 20-mile radius of each station.
sufficient duration for formation of opinions via online reviews. We
have a total of 840,000 restaurants across 32,402 towns/cities and
50 (+DC) states in United States, comprising a total of 1.1 million
text reviews and ratings across the restaurants. Our data contains 46
dimensions which span the restaurant, demographics and weather
classes. We expand on the data variables in subsections below. Notethat our study is geographically limited to the US, since we have access to data for the region; we leave a study across other geographic
regions for future work.
Our data collection process is as follows. First, we mark (possibly) overlapping regions in US that have a radius of 20 miles. We
choose the centers of these regions as the locations of 1,219 weather
monitoring stations operated by the United States Historical Climatology Network (USHCN). At a given time, we expect that theweather conditions are stationary in a 20-mile radius around eachstation. We note that weather monitoring stations are deployed inregions that contain a reasonable population (and hence include areasonable number of restaurants). Second, we find all restaurantsthat are located in each region of interest, and mine their attributes,
reviews and ratings. Third, we mine demographic information in
the neighborhood of each restaurant. Figure 1 shows the locationsof weather stations in our data.

3.1

Restaurants and Reviews

We collect restaurant-related data using the CityGrid API2 between 2002 and 2011 (inclusive). The CityGrid database indexes
up-to-date restaurant snapshots from several popular online business information and recommendation websites such as Citysearch,
AllMenus, Foursquare, GrubHub, Demandforce, Factual, GoMobo,
InfoUSA, SpaFinder and TripAdvisor. We extract restaurant datafrom CityGrid3 . Specifically, for each USHCN weather monitoring station, we find all restaurants located in a radius of 20 miles4of the station. We ensure that each restaurant is associated with its
nearest weather station. We collect data for 840,000 restaurants in
50 (+DC) states across United States. Large cities are divided intoneighborhoods, giving us a total of 32,402 neighborhoods, towns
and cities.
Restaurants. Each restaurant in our data contains several attributes of its online presence. These attributes can be divided into
seven classes; Table 1 gives examples in each class.

DATASET

We consider three broad classes of variables: (1) control variables: restaurant attributes, including reviews and ratings; (2) demographics near the restaurant; and, (3) weather conditions at thetime of visit, near the restaurant. Our data spans 2002 to 2011 (inclusive). We do not consider data after 2011, since we want to allow2

http://docs.citygridmedia.com/display/citygridv2/CityGrid+APIs
We do not consider Yelp data in this study since the API does notgive us sufficient variables for analysis.
4
The restaurant location is specified as a latitude-longitude pair,
and we use the Haversine formula to compute distances.
3

445

Type

Variable

min

mean

medianmax

std.dev

Restaurant

number of reviews∗
restaurant rating

0
0

1.23
7.8

0
8

595
10

5.19
2.03

review rating

0

7.26

8

10

3.11

polarity
subjectivity

-1
0

0.26
0.57

0.25
0.57

1
1

0.22
0.13

Demographics

median income∗population density∗
diversity index
higher education

0
0
0
0

52.0K
3.9K
0.46
0.27

46.9K
2.4K
0.51
0.22

278K
69.4K
0.86
1

26.8K
5.3K
0.22
0.19

Weathermean temperature
precipitation∗
snow∗

0
0
0

29.43
5.68
0.46

23
0
0

104.5
823
640

30.48
27.49
5.57

Review

Distribution

Table 2: Distributions of quantitative variables used in this paper. Variables marked with ’*’ are log transformed.

We define population as a categorical variable based on density,with intervals starting with densities 1000, 2500, 5000 and 10000.

Note that some of the data may not be available for a restaurant;
we mark such data as missing data. We pre-process the dataset to
exclude missing data, assuming that missing data occurs at random.
Reviews. For each restaurant, we collect all available user reviews and ratings using the CityGrid API. Hence, each restaurant
is associated with a number of reviews attribute, which quantifies
online user participation for that restaurant. Each review is associated with a unique author (online user), restaurant and timestamp
tuple. For each review, we collect the complete review text and rating. The rating is an integer between 0 (low) and 10 (high). We
collect a total of 1.1 million reviews.
We post-process the review text for each review to quantify the
subjectivity (between 0 and 1) and polarity (between -1 and 1) of
the text using the Pattern Toolkit [53].

3.2

3.3

For each review for a restaurant, we collect several weather variables at the time of review. We choose weather-related factors, since
they have been shown to affect human behavior and mood [13] –and among others, retail sales [54] and the stock market [49]. We
assume that the day when a review is written is representative ofthe day when the reviewer formed her opinions of the restaurant.
A restaurant is associated with a unique USHCN weather monitoring station. We collect data about weather conditions for eachof the 1,219 weather monitoring stations in our dataset (Figure 1)
using the USHCN Daily Dataset [37]. The USHCN weather data
is provided at the granularity of a day, which works well for our
purpose.
We collect the following weather variables for each restaurant
review: (1) the minimum temperature in Farenheit (Tmin ) in the
24 hour period, (2) similarly, the maximum temperature (Tmax ),(3) precipitation in hundredth of inches, quantifying rainfall, and(4) snow in tenth of inches. Since Tmin and Tmax quantify extreme conditions that last for a small duration in the day, we process the temperature data to calculate a mean temperature estimate,T̄ = (Tmin + Tmax )/2, on the day of the review. We then categorize temperature into four buckets that describe better how humans
perceive temperature: very cold T̄ ∈ [0, 20], cold T̄ ∈ (20, 40],cool T̄ ∈ (40, 70], warm T̄ ∈ (70, 100] and hot T̄ > 100. Unlessotherwise mentioned, we measure temperature in degree Farenheit.
We also categorize precipitation into three groups of no precipitation prcp = 0, medium precipitation prcp ∈ (0, 100] and highprecipitation prcp > 100. We process snow into a binary variableshowing whether there was snow snow > 0 or there was no snow
snow = 0.
We use temperature, precipitation and snow to quantify weather
conditions during a diner’s restaurant visit. Distribution of quantitative variables are summarized in Table 2.

Demographics

We expect that demographics may have an impact on online reviews and ratings of a restaurant as it is an important factor in usageof the Internet [21, 22]. For each restaurant, we collect four dimensions of demographics at the restaurant location (latitude-longitude
or neighborhood) as follows.
First, we collect the median income for residents in the location(latitude-longitude). We represent median income (in USD) as acategorical variable with five values: less than 25K, 25K-50K, 50K100K, 100K-200K and greater than 200K.
Second, we collect the education level for that location, definedas the fraction of residents who have a bachelors degree or higher.
We use the US National Broadband Map5 for this purpose. We represent the education level as a categorical variable with the following values: less than 10%, 10-25%, 25-50% and higher than 50%.
Third, we collect the diversity index for the city/town where eachrestaurant is located. The diversity index for an area is defined by
the US Census Bureau as the probability (on a scale of 0-1) that
two randomly chosen people from the area will have different racial(or Hispanic/non-Hispanic) backgrounds. We use the USA Today
Census database 6 to collect diversity data. We represent diversity
index as a categorical variable with values corresponding to: lessthan 0.3, 0.3-0.5, 0.5-0.7 and greater than 0.7.
The fourth dimension of demographics data is the population
density, defined as population of the neighborhood per square mile.
5
6

Weather Conditions

4.

MODELING REVIEWS AND RATINGSIn this section, we describe our statistical models for the number of reviews and ratings. We then summarize the results of eachmodel by comparing effects of different factors.

http://www.broadbandmap.gov/
http://developer.usatoday.com/docs/read/Census

446Modelθ
ed model0.16
ed+ex model
0.19
SummaryLR.statdegrees of freedom
Pr(>Chisq)

Resid. df
846466846443Model
no. param
ed model25
ed+ex model71
SummaryLR.statdegrees of freedom
Pr(>Chisq)

2 x log-lik.
-1937755-1903529

34225.65
23< 2.2e-16

Table 3: Summary of the models from equation 2 and 4, θ isthe shape parameter of negative binomial distribution, Resid.
df is the residuals degree of freedom for the fitted model. The
chi-square test rejects the hypothesis and so the model with exogenous variables is significant.

4.1

The number of reviews is a count variable. We model numberof reviews using negative binomial regression, on two classes of
independent variables: restaurant attributes (endogenous) and local
demographics (exogenous). Negative binomial regression is wellsuited for overdispersed distributions of count dependent variable [6].
We use negative binomial regression instead of Poisson regression
since the variance of the dependent variable is larger than the mean(µ = 1.23, σ = 5.19). We use overdispersion to test whether Poisson or negative binomial regression should be used. This test wassuggested by Cameron and Trivedi [6], and involves a simple leastsquares regression to test the statistical significance of the overdispersion coefficient.
The negative binomial regression models the expected number ofreviews y for a restaurant as a function of endogenous and exogenous independent variables. We construct two regression models toevaluate the impact of endogenous and exogenous variables: first
to model endogenous variables alone (ed model), and the second tomodel both exogenous and endogenous variables (ed+ex model).
The reduction in deviance from the full model to the endogenousonly model shows the significance of exogenous variables on explaining the number of reviews.
The first model uses restaurant attributes (endogenous variablesed) as predictors of the number of reviews a restaurant receives.
x ∈ed

AIC = −2L + 2k

where, k is the number of parameters and L is the maximum loglikelihood of the model. The smaller the value of AIC, the betterthe fit of the model. Starting with a full set of independent variables
listed in the data section, and all possible interactions of those, we
use a step-wise procedure to select the model that minimizes AIC.
Using the model with minimum AIC also reduces the chances ofchoosing a model that overfits the data.
We test coefficients of all independent variables for the null hypothesis of a zero-valued coefficient (two-sided). This method isbased on standard errors of coefficients, which is analogous to the ttest used in conventional regression analyses. We use a Chi-squared
test with one degree of freedom to test the hypothesis that each coefficient βj is zero. To do this, we compute the following term:χ2 =where I is the intercept for the model and the endogenous sum
is computed using the following restaurant-related attributes:
x ∈ed

Σi i

This model allows us to understand the effect on the number ofreviews of endogenous variables alone.
We then model the impact of exogenous factors (local weather
and demographics) on the number of reviews as follows. We construct a second model that includes both restaurant attributes andexogenous attributes as predictors. We also include predictors forthe interaction between some pairs of the independent variables.
x ∈ed

x ∈ex

βi xi + Σ j j

β j xj

D=2

x ∈ex(SEj )2nX(ζ(yi ; yi ) − ζ(µi ; yi ))i=1

with ζ(yi ; yi ) indicating a log-likelihood function with every valueof µ given the value y in its place. The ζ(µi ; yi ) is the log-likelihood
function for the model being estimated.
The deviance is a comparative statistic. We use the Chi-square
test to find the significance of the regression model, with the valueof deviance and the degrees of freedom as two Chi-square parameters. The degrees of freedom is the number of predictors in eachmodel. Table 3 summarizes the model parameters and the goodnessof fit test results, showing that the regression models are a good fit
for our data.

(3)

where, the endogenous sum is taken from equation 2 and exogenous sum is computed using demographics variables and interaction between endogenous and demographic variables:
Σj j

b2j

where, bj is the estimate of βj and SEj is the standard error of thecoefficient βj . Table 6 shows the β coefficients and the p-values
from the Chi-squared test. We see that almost all independent variables (and interaction variables) have coefficients that are statistically significant.
We use the deviance goodness of fit test to assess our regression
fit [24]. The deviance is expressed as:

βi xi = βprice xprice + βof f ers ∗ xof f ers + βbar ∗ xbar
+βdelivery ∗ xdelivery + βcarryout xcarryout+βmeal xmeal + βf eatured ∗ xf eatured(2)ln(y) = I + Σi i

2348.146< 2.2e-16

Here, xpop is the categorical variable for population density andxedu is the catgeorical variable representing ranges of percentage
of population in the neighborhood with higher education.
The regression coefficients β allow us to understand the effectof an independent variable on the number of reviews (note that to
be able to compare coefficients, we z-score all numerical variables
before performing regression).
In order to choose which subset of independent variables should
be included in the number of reviews model, we use the Akaike Information Criterion (AIC). AIC is a measure of the relative qualityof one model against another, and is defined as following:(1)

β i xilog-Lik-1515507-1514333

Table 4: Summary of the ed and ed+ex models for review ratings. The Chi-square test on the difference between deviancesin the models shows significance of ed+ex compared to edmodel.

Modeling Reviews

ln(y) = I + Σi i

AIC
3031064
3028808

βj xj = βregion xregion + βpop xpop + βincome xincome+βedu xedu + βdiversity xdiversity+βcarryout∗pop xcarryout xpop + βdelivery∗pop xdelivery xpop
(4)

447

4.2

Modeling Ratings

We choose a subset of restaurant attributes that we expect would
not have collinearity between them based on observations fromthe data. For example, we find that the restaurant type “steak andseafood” is correlated with the higher price range, while “hamburger joints” are correlated with lower price range. This is also
the case with dining type; for example, the “cheap eats” and “finedining” types are not independent of price range. Hence, when wetalk about effect of price range, we control for restaurant and dining types as well. In our model, we do not consider other restaurant
attributes such as cuisine and ambience, since we find that the pricerange is generally correlated with these variables in our dataset;hence, price range could be treated as representative of these variables.
Next, we look at the effect of different endogenous variables onuser reviews and ratings.
Price range. Price range is characterized using a scale of four in
websites: “$” (low) to “$$$$” (high). Our regression model forboth number of reviews and ratings consider the lowest price range($) as the reference to evaluate effect of each category on number of reviews and ratings. The results show that the higher pricerange restaurants are more likely to be reviewed by online users
and they are more likely to receive higher ratings. For example therestaurants with price range “$$$$” receive on average 10.9 times
more reviews than the restaurants of price range “$” (β$$$$ = 2.39,
IRR = 10.97 ). We see similar effect for the other two price ranges:
for “$$$” β = 2.19, IRR = 8.93, and for “$$” β = 1.77,
IRR = 5.87.
One reason for the low number of reviews for “$” comparedto the higher price ranges could be that diners do not look for acomplete dining experience in low-priced restaurants, and this may
mean a lower propensity to spend time reviewing online. The dining atmosphere, which makes for a more complete experience, iscorrelated with the price range – suggesting that “$” restaurants
may be focused towards quick or self-service and not a complete
dining experience. We found in our data that more than 95% of thequick serve and family dining restaurants have a price range “$”.
Our observations confirm market research studies by Wakefield
et al. [58] who found that consumers become less sensitive to prices
when they are looking for a hedonic experience (as opposed tofunctional experience). For example, a diner of a “$$$$” restaurant admits in the review that although the place is expensive, thedining experience is enjoyable:

Users specify ratings as an integer on a scale of 0 to 10. We
model ratings using a cumulative link model, also known as orderedlogistic regression [26] on the endogenous and exogenous independent variables for a review (restaurant attributes, demographics andweather conditions), since the model is well-suited for ordinal dependent variables and dichotomous dependent variables.
A cumulative link model is a model for an ordinal response variable, Yi that can fall in j = 1, ..., J categories. Then Yi follows
a multinomial distribution with parameter π where πij denotes theprobability that the i’th observation falls in response category j.
We can write the cumulative probabilities as follows:
γij = P (Yi ≤ j) = πi1 + ... + πij
π) and cumulaThe logit function is defined as logit(π) = log( 1−πtive logits follow:

logit(γij ) = log

P (Yj ≤ j)
, j = 1, .., J − 1
1 − P (Yi ≤ j)(5)

Note that the cumulative logits are defined for all but the last category. A cumulative link model with a logit link is a regression
model for cumulative logits:
logit(γij ) = θj − xTi β
where xi is a vector of independent variables for the i’th observation and β is the corresponding vector of regression coefficients.
The θj variables provide the j’th cumulative logit with its own intercept. A key point is that the regression part xTi β is independentof j, so β has the same effect for each of the J − 1 cumulative
logits.
The regression coefficients β allow us to understand the effect of
an independent variable on the rating (we scale numeric variables
as before). Most of the weather-related distributions (see Table 2)have a heavy tail; we hence consider the logarithm and scale forzero mean before using as input to regression.
In order to understand the effect of exogenous variables on ratings, we build two models for ratings, along the lines of the models for number of reviews. These include logit regression to model
effect of endogenous variables (ed model), and a logit regression
to model the effect of both endogenous and exogenous variables(ed+ex model). For each model, we choose the set of independent
variables that give us a statistical model with the lowest AIC value.
Similar to the reviews model, we use deviance to test for the
goodness of fit of the ratings regression models. The deviance asymptotically follows as χ2 distribution with degrees of freedom equal to
the number of predictors in each model. We use the Chi-square test
to find the significance of the regression model, as before. Table 4summarizes the model parameters and their significance, showingthat the regression models are a good fit for our data.

5.

“Yes it is expensive, but we enjoy eating here. Great
service, great food and a very nice wine list with something for everyone.” (Rating: 10/10)

EFFECT OF RESTAURANT ATTRIBUTES(ENDOGENOUS)

The first class of variables we study are our control variables,i.e., restaurant attributes. In particular, we look at the effect of monetary attributes, atmosphere, service and online advertising on recommendations. Understanding the effect of endogenous factors is a
critical part of this work, since such a study provides a framework
for evaluating the effects of exogenous factors.
Table 6 summarizes the regression coefficients β of the negative
binomial and cumulative link models with and without the exogenous variables (both ed and ed+ex models). The models’ significance is summarized in Tables 3 and 4.

The strong effect of restaurant price which is correlated with ambience may be explained by experimental psychology. Ambience isrelated to consumers’ hedonic experiences (fantasy and emotional
aspects) of dining. Wakefield and Blodgett found that consumers
look for hedonic consumption to experience pleasure and excitement [58]. The “Servicescape” theory [15, 57] argues that hedonic
purposes are more involved than utilitarian (functional) purpose in
consumers. Research shows that the degree of pleasure that consumers experience in a hedonic consumption has a significant effect on their degree of satisfaction and subsequent behavior [35,47]. For example, celebrating a special occasion may have a strong
hedonic purpose, and hence, the dining perception is likely to be7
We use IRR to refer to Incidence Rate Ratio. We compute IRR
for a categorical independent variables x as the ratio of amountof change in the dependent variable (outcome) for x relative to areference level of x.

448

449

Predictor

ref. category(Intercept)
βf eatured
not featured
βprice=$$price=$
βprice=$$$price=$
βprice=$$$$
price=$
βbar
no barβcarryoutno carryout
βdelivery
no delivery
βof f ers
no offers
βmeal=breakf ast
meal=others
βmeal=brunch
meal=others
βmeal=latenight
meal=others
βmeal=lunch
meal=others
βregion=mountainregion=midwest
βregion=northeast
region=midwest
βregion=pacif ic
region=midwest
βregion=south
region=midwest
βpop∈(1000,2500)
pop<1000
βpop∈(2500,5000)
pop<1000
βpop∈(5000,10000)
pop<1000βpop>10000
pop<1000
βedu∈(10%,25%)
edu<10%βedu∈(25%,50%)
edu<10%βedu>50%
edu<10%βdiversity∈(0.3,0.5)
diversity<0.3
βdiversity∈(0.5,0.7)
diversity<0.3
βdiversity>0.7
diversity<0.3
βcarryout∗pop∈(1000,2500)
βcarryout∗pop∈(2500,5000)
βcarryout∗pop∈(5000,10000)
βcarryout∗pop∈>10000
βdelivery∗pop∈(1000,2500)
βdelivery∗pop∈(2500,5000)
βdelivery∗pop∈(5000,10000)
βcarryout∗pop>10000
βpolarity
βsubjectivity

ed model
β
p
0.23
<e-15
1.38
<e-15
1.77
<e-15
2.19
<e-15
2.39
<e-15
0.82
<e-15
-0.77 <e-15
0.68
<e-15
1.69
<e-15
0.40
<e-15
1.52
<e-15
1.92
<e-15
0.77
<e-15

ed+ex modelβp-0.97 <e-15
1.33
<e-15
1.59
<e-15
1.93
<e-15
2.10
<e-15
0.72
<e-15
-0.41 <e-15
0.28
<e-5
1.43
<e-15
0.33
<e-15
1.32
<e-15
1.55
<e-15
0.58
<e-15
0.22
<e-15
0.23
<e-15
0.60
<e-15
-0.08 <e-15
0.20
<e-15
0.39
<e-15
0.32
<e-15
0.21
<e-15
0.31
<e-15
0.71
<e-15
1.02
<e-15
0.13
<e-15
0.18
<e-15
-0.12 <e-15-0.26 <e-15-0.27 <e-15-0.09 <e-3
0.03
0.42
0.13
0.07
0.28
<e-5
0.31
<e-3
0.70
<e-14
0.84
<e-15
0.13
<e-15

Table 5: Regression coefficients and their statistical significancefor both models describing the number of reviews. The reference category refers to the category of the variable that eachfactor was compared against. For the interaction predictors,
the reference can be determined by using the reference category of each of the variables.

Predictor

ref. category

ed modelβpβf eatured
not featured
0.07
βprice=$$price=$
0.30
βprice=$$$price=$
0.38
βprice=$$$$
price=$
0.43
βbar
no bar-0.16
βcarryoutno carryout-0.46
βdelivery
no delivery
0.04
βof f ers
no offers
0.45
βmeal=breakf ast
meal=others-0.04
βmeal=brunch
meal=others
0.00
βmeal=latenight
meal=others
0.10
βmeal=lunch
meal=others
0.11
βregion=mountain region=midwest
βregion=northeast region=midwest
βregion=pacif ic
region=midwest
βregion=south
region=midwest
βpop∈(1000,2500)
ppp<1000
βpop∈(2500,5000)
pop<1000
βpop∈(5000,10000)
pop<1000βpop>10000
pop<1000
βedu∈(10%,25%)
edu<10%βedu∈(25%,50%)
edu<10%βedu>50%
edu<10%βdiversity∈(0.3,0.5) diversity<0.3
βdiversity∈(0.5,0.7) diversity<0.3
βdiversity>0.7
diversity<0.3
βcarryout∗pop∈(1000,2500)
βcarryout∗pop∈(2500,5000)
βcarryout∗pop∈(5000,10000)
βcarryout∗pop∈>10000
βdelivery∗pop∈(1000,2500)
βdelivery∗pop∈(2500,5000)
βdelivery∗pop∈(5000,10000)
βcarryout∗pop>10000
βtemp∈(20,40)
βtemp∈(40,70)
βtemp∈(70,100)
βtemp>100
βprcp∈(1,100)
βprcp>100
βsnow
βpolarity
βsubjectivity

<e-6
<e-15
<e-15
<e-15
<e-15
<e-15
<e-4
<e-15
0.08
0.78
<e-13
<e-15

temp<20
temp<20
temp<20
temp<20
prcp=0
prcp=0snow=0
0.84
0.13

<e-16
<e-16

ed+ex model
β
p
0.06
0.29
0.37
0.42
-0.16
-0.54
-0.20
0.42
-0.04
-0.00
0.09
0.09
0.05
-0.07
-0.02
0.02
0.00
0.03
0.02
0.04
0.01
0.03
0.04
-0.01
-0.01
0.002
-0.01
0.09
0.26
0.34
0.03
0.26
0.26
0.26

<e-6
<e-15
<e-15
<e-15
<2-15
<e-15
<e-5
<e-15
0.10
0.69
<e-11
<e-15
<e-8
<e-15
<e-2
<e-2
0.92
<e-3
0.03
<e-05
<e-2
<e-4
<e-8
<e-2
0.26
0.83
0.44
<e-8
<e-15
<e-15
0.62
<e-7
<e-6
<e-7

0.29
0.34
0.54
-0.18
-0.32
-0.34
-0.23
0.82
0.16

< e-16< e-16
<e-12
<e-13
<e-9
<e-7
<e-7
<e-15
<e-15

Table 6: Regression coefficients and their statistical significancefor both models describing the ratings.
We consider three demographics dimensions in our study; these
dimensions are categorical variables defined over a geographic region such as a city or town (for larger cities we consider neighborhoods as a unit of geography): (1) population density (per squaremile), (2) education level, (3) diversity index. We also considered
median income (dollars) in initial models, but the predictor did
show any significance and was suggested inefficient by the model.
Hence we removed income as a predictor to both models. Education level is defined as the fraction of population who have a bachelor degree or higher in the region. Diversity index (or “diversity”)
is defined by the US Census Bureau as the probability that two
randomly selected people from the region have different racial or
Hispanic/non-Hispanic backgrounds.
Population. We begin with the question: Does population density in the region of a restaurant shape the online review participation and ratings?
We represent population density as a categorical variable in ourmodel (with buckets starting at 0, 1000, 2500, 5000 and 10000).
We use the first bucket (0-1000) as a reference category. Hence, allβ coefficients are compared against the reference category.
We see from our regression models that the effect of neighborhood population on number of reviews is positive but not too large(β between 0.20 and 0.39). Restaurants in regions with density between 1000 and 2500 have the largest number of reviews while regions with lower population densities (less than 1000) are the ones
with fewer number of reviews. Restaurants in the most dense areas (> 10000), however, do not get the highest number of reviews.
The effect of population is not as large as some other demographic
factors such as education, which we will discuss later.
The effect of population on rating is small and negligible; inother words, the higher populated areas do not necessarily impact
the ratings of restaurants in the neighborhood.
We also look at interaction effects between endogenous service
factors (carryout and delivery) and the (exogenous) neighborhood
population. We find that regions with higher population and availability of delivery service have higher chances of being reviewed(βdelivery∗pop>10000 = 0.7, IRR = 2.0). Restaurants in regionswith population density between 1000 and 10000 are likely to be
rated lower than those in low density regions (less than 1000) andhigh density regions (greater than 10000). The effect on ratings ofinteraction between the carryout attribute and local population isrelatively more significant. A case in point is restaurants with car-

450

0.75

0.75

0.25
0.00
0.0

0.50

education
low
high

0.5
1.0
number of reviews

1.5(a) Effect of education level.

review rating

0.50

8.0

CDF

1.00

CDF

1.00

7.5

diversity
low
high

0.25
0.00
0.0

0.5

1.0
1.5
number of reviews

2.0

●
●
●

●
●
●

●

●
●

●

7.0

(b) Effect of diversity.

●●

Figure 3: Effect of demographics: controlled analyses showing
distribution of number of reviews for pairs of restaurants inneighborhoods having similar population densities but highly
dissimilar education and diversity levels. We control for population since it has a strong effect on the number of reviews.

Dec

Oct

Nov

Aug

Sep

Jul

Jun

Apr

May

Feb

Mar

Jan

6.5

Figure 4: Confidence intervals for average ratings in a month.
ryout service that are located in neighborhoods with density greaterthan 10000: they are more highly rated than restaurants in other regions. This finding emphasizes on importance of studying the interplay between endogenous and demographic factors.
Region. We group states of US into five geographical regions:
Midwest, South, Northeast, Mountain and Pacific, based on census
bureau designated areas. Note that this variable would not be correlated with restaurant-local demographics and weather variables,
since it captures relatively coarse-grained region effects. We estimate the effect of regional properties on reviews and ratings. We
use Midwest as our reference region category. Our results showthat region plays a role in explaining the variance in number of reviews. Restaurants in the Pacific region are 1.8 times more likely(β = 0.60, IRR = 1.82) to receive reviews compared to the onesin Midwest. On the other hand, while Mountain and Northeast regions are more likely to receive reviews compared to Midwest, theSouth region is relatively less likely. When it comes to ratings, however, we find that regions have a negligible effect on ratings.
Education. We see that region has a relationship with numberof reviews. What factors in a region are behind these effects? Wefind that education shows a strong positive effect on the number ofreviews. More specifically, neighborhoods with high percentage ofcollege degrees (greater than 50%) are highly likely to have restaurants with high number of reviews. The reference category for education is neighborhoods with percentage of higher education lessthan 10%. Neighborhoods with highly educated residents receive
2.78 times more reviews (β = 1.02, IRR = 2.78). Neighborhoods with 10-25% higher degree penetration (β = 0.31, IRR)
and 25-50% penetration (β = 0.18, IRR) are more likely to receive reviews than neighborhoods with less than 10% of highereducated population. In general, the higher the education level in aregion, the higher are the chances of restaurants in that region being reviewed online. Higher education level, however, has relatively
lesser effect on ratings.
Racial diversity. Another demographic factor we considered inour study is the role of racial diversity (as defined by the US Census
Bureau). We divide regions into four categories in terms of diversity, and use the lowest diversity category as our reference. We findthat medium diversity positively impacts the number of reviews(βdiversity∈(0.3,05) = 0.13, IRR = 1.14 and βdiversity∈(0.5,0.7) =0.18, IRR = 1.20); while the effect of high diversity is less thanthe effect for reference category. We do not see significant effect ofdiversity on ratings.
Weather. Weather conditions at the time of review are an attribute of a review. Hence, we can only test the effects of weather
on ratings. Our results show that in all cases of precipitation greaterthan 0 (rainy days) the rating is affected negatively. This is inline with previous studies showing sunshine having positive affecton mood while cloudy weather having negative affect [8, 40, 50].
Specifically, we find that when it rains but the precipitation is lessthan 100, users rate restaurants lower than when it does not rain
(β = −0.32). When precipitation is greater than 100, the same
relationship holds (β = −0.34).
For temperature we use the the very cold category (0-20◦ F) as
the reference category. We find that when the temperature is in the
20-100◦ F range, it positively affects the rating (βtemp∈(20,40) =0.29, βtemp∈(40,70) = 0.34, βtemp∈(70,100) = 0.54); but at hightemperatures (over 100◦ F), the ratings given by users are lower
than the reference category ratings (βtemp>100 = −0.18). In otherwords, the ratings for a restaurant when the user visited it in moderate (cold, cool and warm) weather conditions are likely to behigher than ratings when the visit was during very cold or very hot
weather. Users tend to give the highest ratings when the weather
is warm (between 70-100◦ F). A related study by Allen and Fischer [1] found that temperature can affect humans - in particular,
performance on a paired association memory task peaked at 72◦ F(22◦ C) and declined with warmer or cooler temperature.
During days when it snows (snow is a binary variable), we seethat users rate restaurants lower than other days (β = −0.23). Notethat the binary snow variable is not necessarily correlated with temperature; hence, we can use the two variables as predictors in ourmodel.
Seasonal trends. Taking a closer look at the data, we find twointeresting temporal patterns of online reviews (see Figure 4). First,
we find that the number of reviews as well as ratings change withthe month of the year (across 2002 to 2011). Specifically, diners
tend to give lower ratings to restaurants in the Summer months ofJuly and August. We also find that the number of reviews shows an
opposite trend, increasing in the months of Summer (June, July andAugust) and a month in Fall (November).

7.

DISCUSSION

Restaurant-goers are increasingly using and relying on social
recommendation communities to make their dining choices. Understanding the dynamics of participation on social recommendation sites is vital to improving such services, and to calibrate recommendations. Prior research has focused on endogenous factors,
which are based on the recommendation community and its users.
In this paper, we augment prior work by studying the effects ofexogenous factors and comparing them with endogenous factors.

451

sary to understand how users rate online, and how a particular user
demographic perceives different restaurants (as opposed to demographics of the restaurant neighborhood). This would require demographic data about online reviewers (or inference of such dimensions from available information). Third, we have shown someinteresting but preliminary visualizations of time trends among ratings and reviews. Additional work is required to understand thesetrends. We are happy to share our rich dataset8 with the community
for future work.

Our findings show that exogenous factors affect user behavior, andhighlight the importance of exogenous factors in recommendation
systems. This could be a foundation for understanding how people
recommend businesses on online communities, and what factors
influence these recommendations.
In ths work, we take a holistic view of online recommendationsof restaurants by considering several endogenous and exogenous
factors that can play a role. We showed that several endogenous
factors related to restaurants influence how users review and rate
the restaurants online. We found that the price bracket in which a
restaurant operates, the type of meal it offers, and the nature of service have a significant correlation with how strongly the restaurant
is recommended (or not) online.
Our work is the first to show that exogenous factors such as
demographics of a restaurant’s neighborhood and weather conditions at the time of the review, can play an important role in online
recommendations for that restaurant. We found that restaurants located in neighborhoods with higher education levels are much more
likely to receive reviews. We also found that weather conditions
when a user reviews a restaurant can play a role in the user’s online
recommendation. We saw that reviews written in warm days are
more likely to be higher rated relative to more extreme temperature
conditions. We found that reviews written on non-rainy days andnon-snowy days are higher rated than those written on rainy andsnowy days. We controlled for all restaurant and demographicsrelated factors in our analysis.
We also showed that certain endogenous and exogenous factors
are inter-related. For example, service-related factors such as delivery and carryout are inter-related with population density; and theirimpact is better understood when the neighborhood demographics
are taken into account.
We believe that this is only an initial step, and that there is a richlandscape of research directions and open questions in this area.
Our findings have implications for the design of recommendation
systems. Recommendation sites can account for, and correct bias,that is systematically related to demographics and weather. For example, a restaurant with N reviews in New York City may not describe the same popularity as one with N reviews in a small suburban area.
Limitations and Future Work. Our work is purely quantitativeand based on observations we had from data. Our approach is useful
in describing what factors affect restaurant reviews and ratings, but
without a corresponding qualitative approach, we can only speculate on why these factors matter. Further, the statistical methods
we used examine only a small segment of what is available on thereview sites (for example, a large-scale analysis of review text cancomplement our work).
Future work can establish models to further understand impact.
First, we note that our analysis does not necessarily establish causality between all of these dimensions and reviews or ratings. Second,modeling dynamics of interaction on social recommendation sites
may highlight aspects of how particular restaurants become popular on recommendation sites. Third, what-if and how-to models areuseful to predict online reviews and ratings in new scenarios and tohelp business owners understand how to improve (or correct) ratings of their restaurants. Finally, models similar to the ones in ourstudy can be built for other review sites or online communities. For
example, the CityGrid database indexes data about shopping.
Open questions. There are some interesting questions that can be
answered with the dataset. First, we have not looked at the effect of culture on online ratings of restaurants. Can we validate
our models for online recommendations in other geographic areas?
Second, a fine-grained demographics analysis of ratings is neces-

8.

CONCLUSION

Online recommendation sites are important sources of information for people to choose restaurants. In this work, we take a firstlook at online restaurant recommendation sites to study what endogenous (i.e., related to either the community, its members orentities being reviewed) and exogenous factors influence people’s
participation and their recommendations. Online participation andrecommendations have been modeled in general as functions ofendogenous factors. Using models constructed from a corpus of
840K restaurants and their 1.1M associated reviews, we find thatwhile endogenous factors such as restaurant attributes (e.g., priceand service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education)
and weather (temperature, precipitation, snow, season) also exert asignificant effect on recommendations. Many of these online effects can be explained using (offline) theories from experimental
psychology. Our work has implications for online recommendation
systems design; for example how online communities could calibrate recommendations or project reviews for new restaurants withfew reviews.

9.

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