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Hospitality Management 26 (2007) 1005–1018

Language representation of restaurants: Implicationsfor developing online recommender systems

Zheng Xiang a, , Sang-Eun Kim b , Clark Hu c, Daniel R. Fesenmaier d

a School of Merchandising and Hospitality Management, University of North Texas,

P.O. Box 311100, Denton, Texas 76203, USAb National Laboratory for Tourism & eCommerce, School of Tourism & Hospitality Management,Temple University, 1700 N. Broad Street, Suite 201F, Philadelphia, PA 19122-0843, USA

cNational Laboratory for Tourism & eCommerce, The Fox School of Business and Management, School of Tourism& Hospitality Management, Temple University, 201, 1700 N. Broad Street, Philadelphia, PA 19122-0840, USA

d Director, National Laboratory for Tourism & eCommerce, School of Tourism and Hospitality Management,Temple University, 1700 N. Broad Street, Suite 201C, Philadelphia, PA 19122, USA

Abstract

As a marketing tool recommender systems have the potential to provide relevant and highlypersonalized information to consumers. However, developing effective recommender systemsrequires a substantive understanding of consumers’ preferences as well as meaningful ways torepresent hospitality and travel products. This paper argues that language holds the key tounderstanding consumer preferences and therefore developing effective online recommender systems.Specically, it explores the nature of the language used by consumers to describe their diningexperiences in contrast to the language used by restaurant websites. The ndings indicate thatconsumers use substantially different vocabularies from restaurant websites to describe diningexperiences. This study provides implications for developing online recommender systems for

restaurants as well as general hospitality and travel products.r 2007 Elsevier Ltd. All rights reserved.

Keywords: Restaurant; Consumer language; Recommender systems; Consumer mapping; Text analysis

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0278-4319/$ - see front matter r 2007 Elsevier Ltd. All rights reserved.doi: 10.1016/j.ijhm.2006.12.007

Corresponding author. Tel.: +19405652436; fax: +1 9405654348.

E-mail addresses: [email protected] (Z. Xiang) , [email protected] (S.-E. Kim) , [email protected](C. Hu) , [email protected] (D.R. Fesenmaier) .

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1. Introduction

As the Internet continues to become an essential part of everyday life, how to deliverrelevant and highly personalized information to both potential and existing customers hasbecome an important task for hospitality and tourism businesses. Numerous hospitalityand tourism-related businesses and marketing organizations have employed a variety of approaches to provide travelers online information about their products and services withthe goal of facilitating trip planning. One common approach is to provide recommenda-tions to travelers with respect to accommodation, activities, and even destinations ( Burke,2002 ; Fesenmaier et al., 2006; Riedl et al., 2002 ). In the hospitality sector, online businesseshave also extensively adopted this approach by offering consumers options to customizetheir dining experiences. For example, online restaurant advisory websites such asZagat.com, Restaurants.com, and others not only allow customers to search for desiredrestaurants, but also explicitly recommend certain restaurants to customers.

One of the limitations of these websites is that restaurants are often recommendedsimply based upon a limited number of functional attributes such as price, cuisine, andlocation and thus there is a lack of adequate representation of the holistic experience of dining. Restaurants, and travel products in general, are arguably ‘‘experience goods’’ inthat full information on certain attributes cannot be known without the direct experience(Klein, 1998 ). That is, when looking for information about travel products, a consumer’sinformation search behavior is driven not only by functional and utilitarian needs but alsoby many social and hedonic needs ( Vogt and Fesenmaier, 1998 ). Considering dining out isoftentimes a social behavior involving explicit or implicit identity construction and image

building ( Finkelstein, 1989 ), a fundamental difculty in making online recommendationslies in understanding what consumers really want in order to identify the products that canpossibly match their preferences. Thus, developing effective and meaningful solutions toonline recommendation of restaurants remains a challenging task.

This paper argues that understanding the language representation of restaurants canprovide a useful basis for developing effective online recommender systems. The success of online recommendations hinges on a substantive understanding of, and the ability tocapture, consumer preferences ( Schafer et al., 2001 ). Given the experiential nature of dining, consumers’ language can be used as the key to understanding their perceptions of restaurant attributes and making inferences about their preferences. Recent research on

online travel information search has revealed that the information provided by travelservice providers can be substantially different from what travelers are looking for ( Panand Fesenmaier, 2006 ). This suggests that there exists a great potential to improve theonline representation of travel-related products. Thus, the goal of this paper is to explorethe nature of the language consumers use to describe their dining-out experience incontrast to what is portrayed by restaurants’ online descriptions. Text analysis techniqueswere used to examine the similarities and differences between the languages used byconsumers and restaurant owners. The ndings of this research provide useful implicationsfor the development of effective online restaurant recommender systems.

2. Literature review

A recommender system in the online environment represents a value-added processbecause it helps solve problems related to ‘‘information overload’’ in consumers’

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information search process ( Furner, 2002 ; Schafer et al., 2001 ). However, the challengesfor online recommender systems lie in that consumer preferences are highly dependentupon the nature of the product. An effective online recommender system must be basedupon an understanding of consumer preferences and successfully mapping potentialproducts onto the consumer’s preferences ( Adomavicius and Tuzhilin, 2005 ). Followingfrom Gretzel et al. (2004) and Pan and Fesenmaier (2006) , it is argued that this can beachieved through the understanding of how consumers describe in their own language aproduct, a place, and the experience when consuming the product or the place.

2.1. Modeling consumers’ restaurant preferences

Traditionally, choosing a restaurant has been considered as rational behavior whereby anumber of attributes contribute to the overall utility of a restaurant ( Auty, 1992 ; Kivelaet al., 1999 ). For example, food type, food and service quality, image and atmosphere of therestaurant, and availability of information about a restaurant, play an important role atdifferent stages in consumer’s choice-making ( Auty, 1992 ; Kivela, 1997 ). While food qualityand food type have long been perceived as the most important variables for consumers’restaurant selection, ‘‘soft’’ attributes such as situational and contextual factors have beenfound to be important determinants ( Baker, 1986 ; Bell and Meiselman, 1995 ; Bruner, 1990 ;Kivela, 1997 ). It is considered that the relative importance of traditional attributes of restaurant product (i.e., price, food type, etc) changes with the restaurant situational andcontextual factors ( Auty, 1992 ). Based upon these results, Kivela (1997) identied fourdistinct types of restaurants, i.e., ne dining/gourmet, theme/atmosphere, family/popular,

and convenience/fast-food. Auty (1992) identied four types of dining-out occasions, namelycelebration, social occasion, convenience/quick meal, and business meal.The difculty in modeling restaurant choice is caused by the fact that dining-out

experience often involves many socio-psychological aspects that go beyond the simplefunctional characteristics of restaurants or food services. According to some researchers(e.g., Caplan, 1997 ; Finkelstein, 1989 ), dining-out is essentially a social experience wherebyindividuals seek to construct, reify, or reinforce their socio-cultural identities. The oldproverb ‘‘we are what we eat’’ underscores the importance of dining in the construction of different cultures and self-image. Eating is no longer a simple biological activity for humanexistence, and the consumption of food fundamentally structures social values and personal

identities. Indeed, recent research in restaurant choice modeling has indicated that ahigh-quality meal and outstanding service are no longer considered the main factors evokingpleasant and memorable dining experiences ( Hanefors and Mossberg, 2003 ; Pine andGilmore, 1999 ). Caplan’s research (1997) clearly shows that social and personal identitiesand perceptions of health risk can substantially inuence consumers’ restaurant choices.

2.2. Approaches to online recommendations

Online recommender systems can provide efcient and effective ways to matchconsumers’ preferences over certain products. Generally, the main goal of a recommendersystem can be described as the selection of an item from a the set of all possible items suchthat it maximizes a specic user’s total utility ( Adomavicius and Tuzhilin, 2005 ; Furner,2002 ). Many approaches to making recommendations have been developed as e-commerceapplications, ranging from very simple retrieval or ltering applications (e.g., providing a

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list of the top 10 restaurants) to extremely complex ones which rely on some form (indirector direct) of elicitation of users’ preferences ( Burke, 2002 ; Furner, 2002 ; Riedl et al., 2002 ).

Collaborative ltering is a commonly used indirect approach whereby preferences areconstructed through modeling various online user behaviors. It focuses on identifyingindividuals’ preferences and recommends items that people with similar preferences havepurchased in the past ( Goldberg et al., 1992 ; Riedl et al., 2002 ). A well-known example of systems based on collaborative ltering is Amazon.com, which suggests products tocustomers by hinting that ‘‘customers who bought this item also bought y ’’ (www.amazon.com ). Recommender systems can also use a content-based approach, which learnsa prole of the user’s interests based on the features present in items the user has rated(Belkin and Croft, 1992 ). Basically, it relies on the customer’s past history to infer his/herpreferences and thus the customer will be recommended with items similar to those he/shepurchased in the past. Other approaches such as case-based reasoning are usually hybridsthat leverage both individual user preferences and collaborative similarities (c.f., Burke,2002 for a comprehensive review). For restaurants, Burke (2000) developed a system called‘‘Entre é’’ which uses a ‘‘knowledge-based’’ approach to make recommendations by ndingrestaurants in a new city similar to the restaurants the user knows and prefers. It enablesthe user to interact with the system by stating his/her preferences of a given restaurant andrening search criteria by critiquing the system’s suggestions. In addition to these modelingapproaches, Schafer et al. (2001) suggested that other factors such as the type of user input,the existence and type of community, the presentation of the recommendation, and thedegree of personalization, are important in making effective online recommendations.

Making recommendations for travel and hospitality products in an online system is

considered a more complex task than general consumer goods ( Fesenmaier et al., 2006 ;Ricci, 2002 ). First, travel involves bundling of a large number of heterogeneous productsand services and thus, applying the content-based approach requires extensive domainknowledge to be built for the particular application. Second, because the consumption of travel products is individual-based and context-specic, it cannot be assumed that twoidentical trips will result in the same experience, even if the two travelers go to the samedestination and visit the same attractions. In the case of restaurant services, it is difcult togeneralize an individual’s dining-out experience to others’ because his/her experience hasbeen shaped by many contextual and situational factors. As such, the uniqueness of traveland hospitality products poses a challenge for developing recommender systems. To

address this issue, a commonly used approach focuses on developing effectivecommunicative interactions between a user and a system. For example, Gretzel et al.(2004) proposed an approach based upon the elicitation of individual’s personality traits;Ricci et al. (2005) proposed a method called ‘‘recommendation by proposing’’ aiming attravelers by presenting images and simple descriptions of products to users.

Among these approaches, language representation of travel products has beenincreasingly considered an important issue in developing travel recommender systems,and online hospitality and tourism marketing in general. This recognition is rooted in theunderstanding that travel and tourism has a discourse of its own which includes acts of promotion as well as accounts of travelers ( Dann, 1997 ). The language used to targettourists and the language used by tourists form the basis of interpreting travel productsand tourist experiences ( Gretzel et al., 2006 ). Recent research on online travel informationsearch (e.g., Pan and Fesenmaier, 2006 ) has found that online tourism information usessignicantly different languages. That is, a large amount of marketing-oriented content is


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focused on the selling of travel products, while travelers use more subjective andexperiential language to describe their perceptions and expectations when searching fortravel products. The gap between the promotion of travel products and travel informationsearch indicates that marketers need to utilize different forms of communication to enabletravelers to express their need for information that is framed within their personal context.From the system design point of view, Gretzel and Fesenmaier (2002) argued that theexisting approaches in tourism information systems, which generally rely on numeric data,cannot capture the holistic experiential aspects of travel. They suggested that narrative(story-telling) logic should be incorporated into online recommender systems to assist tripplanners to make sense of the world. According to them, narratives can better support thegeneration of mental imageries and thus, provide guidance in terms of interpreting searchresults and evoking imagination. More recently, Loh et al. (2003) proposed an approach to‘‘mine’’ customers’ textual messages when interacting with an online travel agent in orderto understand their preferences. From these studies, it is clear that the understanding of how consumers perceive and describe travel products can help develop useful ways for therepresentation of restaurant services in recommender systems.

3. Research questions

Based upon the literature reviewed, it is argued that online restaurant recommendersystems must be able to ‘‘speak the same language’’ as restaurant customers. Thus, the goalof this study was to explore the nature of the language consumers use to represent their

preferences of restaurants as opposed to the language used by restaurant owners andmarketers on their websites. Specically, this study aimed to achieve two objectives: (1) toidentify and compare the vocabularies used by both consumers and restaurant websites indescribing the dining experience; and, (2) to understand the effect of situational factorssuch as restaurant type and dining-out occasion on the language used by consumers todescribe their dining experiences. Hence, two research questions were formulated:

Q1 . What is the nature of the languages used by consumers and restaurant websites indescribing a dining experience? How are they similar to and/or different from eachother?

Q2 . To what extent does the nature of the restaurant type and the dining-outoccasion effect consumers’ language representation of their dining experience?

4. Method

This study was conducted in three stages. First, a pilot study was conducted to identifythe specic restaurants to be included in the study. Second, two types of language datawere collected: (1) consumers’ verbal representation of their dining experience at therestaurants identied in the rst step; and, (2) descriptive text included on the websitesowned by the restaurants identied above. Third, text analysis as well as multivariateanalysis techniques were employed to compare the languages used by consumers andrestaurants and to assess the effect of dining-out occasion and restaurant type on consumerlanguages ( Hair et al., 1998 ; Woelfel and Woelfel, 1997 ).


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4.1. Pilot study

Twenty three college students in a northeastern metropolitan area in the United Stateswere asked to identify 10 restaurants where they had previous dining experience or aboutwhich they had second-hand information. In order to obtain descriptions of the diningexperiences at these restaurants, respondents were asked to indicate the type of restaurantfor each restaurant they provided using one of the three types of labels: ‘casual’, ‘family’,and ‘ne dining’. The results of the pilot study indicated that most respondents labeled therestaurants as either ‘casual’ or ‘ne dining’. The label ‘family’ was dropped because only avery small number of respondents actually applied it to the restaurants they identied. Theresults also revealed that a large number of restaurants were labeled as both ‘casual’ and‘ne dining’ by different respondents, suggesting that there existed a ‘fuzzy’ set amongthese restaurants. Thus, a new label called ‘overlapping’ was added as a third restauranttype, which represents the blurring that may occur between casual dining and ne diningcategories ( Goldman, 1993 ). Based on these preliminary results, nine restaurantsrepresenting the three types were included in the subsequent data collection procedurewith each type containing three restaurants that were most frequently and distinctlymentioned by respondents.

4.2. Collection of language data

Two types of language data describing the restaurants were collected. First, aquestionnaire was used to elicit consumers’ description of the nine restaurants identiedin the pilot study. One hundred and nine students from the public university located in thesame metropolitan area participated in the study in exchange for course credit in late 2004.They were asked to describe their dining experience at these restaurants using up to vesimple phrases or sentences for each restaurant. In addition, each student was randomlyassigned to one of three different dining-out occasions (namely entertainment, family-dining, and romantic dinner), which were considered the most common occasions fordining-out through the literature review earlier. Second, the textual contents (except thenavigational and functional elements) from the websites owned by the nine restaurantswere extracted. These textual contents were then used as the language representation of therestaurants by the restaurant owners. Two text les were generated with one containingconsumers’ language data and the other the descriptive texts from the restaurant websites.In the consumers’ data le, each case, i.e., the ve phrases or sentences used by onerespondent, was associated with two additional identiers, namely restaurant type(i.e., ‘casual’, ‘ne dining’, and ‘overlapping’) and dining-out occasion (i.e., ‘entertain-ment’, ‘family-dining’, and ‘romantic dinner’).

4.3. Language data analysis

Before performing the text analysis, the two text les were pre-processed using thefollowing three steps: (1) removed ‘stop’ words such as ‘a’, ‘the’, ‘and’, and ‘we’ in order toinclude only meaningful words in the analysis; (2) replaced plurals with singles and pasttense with present tense so that those words with the same linguistic roots (‘drinks’,‘drank’, and ‘drink’/ ‘eats’, ‘ate’, and ‘eat’) can be treated as the same; (3) used a single


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token to represent all synonyms and proper names, e.g., ‘waitstaff’ for synonyms such as‘server’, ‘waiter’, ‘waitress’, and ‘staff’, and ‘Olive Garden’ to ‘OliveGarden’.

CATPAC ( Woelfel and Woelfel, 1997 ) was then used to explore the nature of, and thedifferences between, the languages used by consumers and restaurant websites indescribing a dining experience by extracting the unique words used in both text les.Descriptive statistics were obtained to provide a comparison between consumers’ languageand restaurant websites’ language based upon word frequencies. In particular, the mostfrequently used unique words in both textual les were extracted and presented side-by-side to provide a comparative view of the commonalities and differences in the languagerepresentations of restaurant attributes. Correspondence analysis was then employed toexamine the association between the unique words used and different dining-out scenarios.Each scenario was a combination of a specic restaurant type and a dining-out occasionand thus, nine scenarios were generated (i.e., 3 restaurant types 3 dining-out occasions).The analysis used the frequency matrix for the top 160 most frequently used unique words(rows) for each scenario (columns). A correspondence map was then constructed tovisualize the associative relationships in a two-dimensional space.

5. Results

Table 1 provides the basic descriptive statistics of the language data collected from bothconsumers and restaurant websites. As can be seen, the original text le from the studentrespondents consisted of a total of 9924 words and the nine restaurant websites consistedof a total of 7219 words. After pre-processing (e.g., tokenization), there were 9893 words in

the consumer data le and 7211words in the restaurant website data le, respectively.There were 1216 unique words in the consumer data le, representing 12% of the totalnumber of words; in contrast, there were 1904 unique words in the website data le,representing 26% of the total number of words. The percentage of unique words wasobtained as the ratio between the number of unique words and the total number of wordsin the original data le. This result indicates that, overall, restaurant websites usedsubstantially more diverse vocabularies than consumers in describing the restaurantproducts and dining experience.

CATPAC was used to deconstruct the two text les by identifying the unique wordsalong with their frequencies in the two types of language data. The consumer languagedata was further differentiated by different types of restaurants, i.e., casual, ne dining,and overlapping. As shown in Table 2 , consumers and restaurant websites share a total of 428 words, while consumers used 788 words that were not used by restaurant websites and

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Table 1Descriptive statistics of the two language datasets

Type of data Number of words

Consumer Restaurant

Original data 9924 7219Pre-processed data 9893 7211Unique keywords (percentage*) 1216 (12%*) 1904 (26%*)

*The number in the parenthesis indicates the percentage of unique keywords contained in the original data.

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restaurant websites used 1476 words that were not used by consumers. This observationsupports the previous nding that restaurants use a much richer language than consumers.

Interestingly, the nature of the language used varied substantially among different typesof restaurants. As shown in Table 2 , while consumers used roughly the same number of unique words to describe restaurant attributes and dining experiences (604, 588, and 582for casual, ne dining, and overlapping, respectively), there existed a substantial differencein the language used by restaurant websites for difference types of restaurants. The numberof unique words used for casual and overlapping restaurants are 385 and 648, respectively.However, the websites of ne dining restaurants used 1167 unique words to describe their

products. In addition, the numbers of words that were used only by consumers were veryclose for the three types of restaurants (503, 380, and 422 for casual, ne dining, andoverlapping, respectively). However, websites of ne dining restaurants used asubstantially larger number of words (959) that were not used by consumers, as opposedto the other two types of restaurants (284 for casual and 488 for overlapping). This ndingindicates that websites of ne dining restaurants use more ‘‘elaborate’’ approaches indescribing their products than the other types of restaurants.

To further analyze the commonalities and differences between the two types of language,the unique words that were commonly shared by consumers and restaurant websites aswell as those used exclusively by either consumers or restaurant website, were extracted

and presented side-by-side to provide a comparative view (see Fig. 1 ). Due to the limitationof space, only the top 50 unique words are listed: (1) those used by both the consumers andrestaurant websites (column 1); (2) those used only by consumers (column 2); and, (3)those used only by the restaurant websites (column 3). The rows of the table represent thethree types of restaurants included in the study: ‘casual’, ‘ne dining’, and ‘overlapping’.All of the common and different words are rank ordered by their frequencies in thedatasets. As shown in Fig. 1 , the words most commonly shared by consumers andrestaurant owners were words such as ‘food’, ‘atmosphere’, ‘service’, ‘Italian’, and‘restaurant’, which represent the general attributes of restaurant products. Consumersand restaurant websites used a large number of distinctively different keywords, most of which were related to the restaurant attributes perceived as important by either party. Forexample, regardless of restaurant type, consumers were concerned about attributes such asthe value for money and service quality of the restaurants (represented by words such as‘price’, ‘expensive’, ‘inexpensive’, ‘excellent’, and ‘nice’). However, restaurant websites

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Table 2A comparative view of the frequencies of unique words in the language representations by both consumers andrestaurant websites

Restaurant type Number of unique words Number of common words Number of different words (%)

Consumer Restaurant Consumer Restaurant

Casual 604 385 101 503 (83.2%) 284 (73.8%)Fine dining 588 1167 208 380 (64.6%) 959 (82.2%)Overlapping 582 648 160 422 (72.5%) 488 (75.3%)Total 1216* 1904* 428* 788* (64.8%*) 1476* (77.5%*)

*Note that the total number does not match the summated value of all three categories, since the number of duplicated words among the categories were subtracted.

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focused more on the types of products and the services available (represented by wordssuch as ‘platter’ and ‘offer’) or the features that distinguish one restaurant from others(represented by words such as ‘collection’, ‘history’, ‘chef’, and ‘Havana’). Interestingly,

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Fig. 1. A comparative view of the top 50 words in the language representations of both consumers and restaurantwebsites.


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consumers used more adjectives and nouns, while restaurant owners used more verbs suchas ‘offer’, ‘continue’, ‘learn’, ‘try’, and ‘help’, which represents an active tone of persuasion.

Table 3 provides the summary results of the correspondence analysis and indicates thatthere were eight non-trivial dimensions which could be used to fully explain the variance inthe data. The eigenvalue (0.208) of the rst dimension explained only 39.7% of the totalvariance, while the second dimension accounted for an additional 10.8% of the totalvariance. The rst two signicant dimensions were used to construct the correspondencemap to facilitate further discussion.

Fig. 2 provides the correspondence map to represent the associations between the top160 frequently used words and the nine dining-out scenarios in a two-dimensional space. Itshows that three distinct groups exist among the nine dining-out scenarios: Casualrestaurants (represented by labels ‘CE’, ‘CR’, and ‘CM’) are close to one another than toother types of restaurants (i.e., ne dining, labeled as ‘FE’, ‘FR’, and ‘FM’; and,overlapping, labeled as ‘OE’, ‘OR’, and ‘OM’), and vice versa. This suggests that thedining-out occasions, namely ‘entertainment’, ‘romantic’, and ‘family dining’, did notgenerate substantially different language representations of restaurant attributes. Whenholistically examining the correspondence map, the displayed 160 unique words seemed toform four clusters, including: (1) a set of words (enclosed in the central eclipse) were

commonly shared by consumers’ descriptions of the three types of restaurants, including‘food’, ‘menu’, ‘nice’, ‘delicious’, ‘dinner’, ‘dining’, ‘cuisine’, ‘good’, ‘bar’, ‘wine’, and soforth; and, (2) other words were closely associated with each type of restaurant, indicatinga degree of uniqueness when consumers chose the words to describe the restaurantproducts. For example, words such as ‘casual’, ‘affordable’, and ‘variety’ are closelyassociated with the ‘casual’ restaurant type; words such as ‘steak’, ‘wonderful’, and‘upscale’ appear to be associated with the ‘ne dining’ restaurant type; last, words such as‘tasty’, ‘international’, ‘portions’, and ‘entertainment’ are associated with the ‘overlapping’restaurant type.

The correspondence map also shows there are words that fall in between two clusters of restaurants. For instance, words such as ‘italian’, ‘location’, ‘need’, ‘city’, and ‘scene’ (inQuadrant I) are approximately equally distant from ‘ne dining’ and ‘overlapping’restaurants; words such as ‘amazing’, ‘theme’, ‘family’, ‘beer’, and ‘asian’ (in Quadrant II)are equally associated with ‘overlapping’ and ‘casual’ restaurants. These words are used by

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Table 3Summary of the correspondence analysis

Dimension Eigenvalue Proportion of inertia

Accounted for (%) Cumulative (%)

1 0.208 39.708 39.7082 0.088 16.815 56.5233 0.066 12.598 69.1214 0.056 10.754 79.8755 0.037 7.087 86.9626 0.029 5.603 92.5667 0.020 3.888 96.4548 0.019 3.546 100.000

w2: 3211.753 (observed), 1397.379 (critical) with 1312 degrees of freedom; one-tail po 0:0001.

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consumers to describe both types of restaurants and thus, represent the ‘‘common space’’in consumers’ perceptions of two kinds of dining experiences. Interestingly, ‘ne dining’and ‘overlapping’ restaurants share very few words in common, suggesting that these twotypes of restaurants are perceived distinctively by consumers. In addition, there are wordsthat are distributed far from the center of the map, e.g., ‘clean’ and ‘crowded’ in QuadrantII, ‘spanish’, ‘grilled’, and ‘money’ in Quadrant III, and ‘trendy’, ‘dressup’, and ‘ambience’

in Quadrant IV. These words are less frequently used by consumers; however, they aremore closely associated with a specic restaurant type than others. These words appear torepresent the idiosyncrasies of restaurant type perceived by consumers. For instance,words such as ‘trendy’, ‘dress-up’, and ‘ambience’ reect consumers’ perceptions of nedining restaurants.

6. Discussion and conclusions

As an online marketing tool recommender systems guide behavior by suggestingproducts or information relevant to consumers ( Fesenmaier et al., 2006 ; Riedl et al., 2002 ).This paper argues that understanding the language consumers use to describe restaurantsis essential to developing effective online recommendations. The ndings indicate thatconsumers and restaurant websites, indeed, use very different languages to describe thesame restaurant products. In addition, the effect of different dining-out scenarios on the

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Fig. 2. Correspondence map for the top 160 key words represented with nine dining-out scenarios.


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consumer language was also examined, revealing that, while there are commonalitiesamong dining-out occasions, substantial differences exist between restaurant types.Through investigating the nature of consumer language, particularly the uniqueness andidiosyncrasies in the ways consumers represent their dining experiences, the ndings offerseveral important implications for developing online recommendations.

First, most of the existing online recommender systems have been built based upon theinformation provided by producers. Indeed, the results of this study show onlinerestaurant websites, which are provided by restaurant owners or advertisers, largely ignorethe axiom in system design and content provision; that is, to ‘‘speak the same language’’ asconsumers ( Dann, 1997 ). A direct negative consequence of this failure is that consumerswill nd it difcult to locate the restaurant they are looking for ( Nielsen, 2006 ).

Second, variety seeking is especially pronounced in hospitality and travel. In the case of restaurant service, consumers can be novelty or sensation seeking when looking forrestaurants. Consumers not only spend their time and money on eating, but also will usethe dining-out occasion to fulll heterogeneous needs. From a recommendation point of view, it is important to provide messages relevant to the specic needs of the consumer insearch of an experience. Therefore, restaurant owners or online marketers should addressconsumers’ experiential needs by delivering highly relevant and even personalized cues.For example, using the vocabularies identied in consumers language, a restaurant websitecould provide messages similar to the following conjectural narratives to mimic aconsumer’s way to express his/her preferences when searching for a restaurant ( note: thewords in bold font type were identied as exclusively used by consumers in this study):

I’d like to have good and nice food with my family in a casual restaurant that isinexpensive . Also, I hope I can receive fast services from their friendly staff

for a casual restaurant; or,

This restaurant is upscale with high quality food and a great atmosphere. I bet peopledining there are trendy and classy and they are probably very similar to us.for a ne dining restaurant.

The stories told by consumers, which span a variety of media and emerge in differentcontexts of consumption, form the basis for interpreting travel experiences andconstructing meaning ( Gretzel et al., 2006 ). Indeed, the evolution in communication on

the Internet offers profound implications for both service providers and consumers. Withthe emergence of new forms of communication such as personal blogs, social networkingtools, and collaborative tagging, numerous opportunities exist for hospitality and travelbusinesses to tap into the consumer knowledge readily available online in abundance intextual format. As illustrated by this study, ‘‘mining’’ consumer knowledge throughanalysis of language can reveal consumers’ preferences when selecting restaurants. As such,it is important for hospitality and travel businesses to recognize these opportunities forimproving their understanding of consumers. It seems to be a promising sign that manyservice providers (e.g., www.sheraton.com ) are including their guests’ opinions on theirwebsites in different forms (e.g., stories and reviews).

There are two important limitations of this study. First, consumers’ languagerepresentation of restaurants was collected from college students. Future research shouldextend the current study to collect the language used by the general public to improve theexternal validity as well as to examine the differences across heterogeneous social


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demographic segments. The sampling of consumer language data can be expanded toinclude different sources, e.g., real-time search terms used in online search functions,discussions in online communities, and personal blogs, which can be used to validate thenature of consumer language as identied in this study. Second, while this study shows thatmarketers and consumers use considerably different languages, the effect of exploitingconsumers’ language in the development of a recommendation system was not tested.Therefore, future research should aim to empirically validate these ndings through thedevelopment of language-sensitive recommender systems.

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