travelers have Bcon=Pool=0.5. In other words, they have 2117 randomly selected hotels over the United States. The the same preferences regarding a pool and conference center. On transactions covered the period from November 2008 until Jan- the other hand, for business travelers, their preference towards uary 2009. Based on the given transactions, we were able to "conference center"is much higher than towards "pool, writh compute the market shares of each hotel in each local market BPonf =0.9 and Bpo This estimation result can be further interpreted with mone- Consumer demographics: To measure the demographics tary meanings. For instance, we can infer that a business trip of consumers in each market, we used data from the Trip Advisor traveler is willing to pay $54 for the conference center and $6 web site: The consumers that write reviews about hotels on Tri- whereas a family trip traveler is willing to pay pAdvisor also identify their travel purpose(business, romance, equally $30 for each of the two feature family, friend, other) and their age group(13-17, 18-24, 25-34, 35-49, 50-64, 65+). Based on the data, we were able to identify 3. SURPLUS-BASED RANKING the demographic distribution of travelers for each destination. So far. we have described the economic model used for Hotel location characteristics: We used search tools(in particular the Bing Maps API) and social geo- ferring the preferences of consumers using a utility model and tags(from geonames.org)to identify the external amenities" aggregate demand data. This model uses the concept of surplus ainly as a conceptual tool to infer consumer preferences to- (such as shops, restaurants, etc)and available public trans- wards different product characteristics In our work, the concept portation in the area around the hotel. We also used image of surplus is directly used to find the product that is the best there is a nearby beach, a nearby lake, a downtown area, and value for money "for a given consumer. whether the hotel is close to a highway. We extracted these We define Consumer Surplus for consumer i from product j characteristics within an area of 0.25-mile, 0.5 mile, 1-mile, and as the"normalized utility surplus the surplus US, divided 2-mile radius by the mean marginal utility of money a Hotel service characteristics: We extracted the service. ased cha CS,=Normalized_ US, (4) review provides a general rating of the hotel, plus provides seven individual ratings on the following service characteristics e thereby use the estimated surplus for each product and value, Room, Location, Cleanliness, Service, Check-in, and rank the products in decreasing order of surplus. Therefore, Business Service. We computed the average ratings of each at the top we will have the products that are the"best value" hotel across these seven characteristics and used them in our for consumers, for a given price. Furthermore, we extend our data set, together with the general review rating. We also used anking to include a personalization component. To compute the hotel description information from Travelocity, Orbitz, and the personalized surplus, we ask the consumer to give the Expedia, to identify the"internal amenities"of the hotels(e. g appropriate demographic characteristics and purchase context pool, spa (e. g, 25-34 years old, male, S100K income, business traveler) Stylistic characteristics of online reviews: Finally, and then use the corresponding deviation matrices Br and aI. extracted indicators that measure not the polarity of the re- It is then easy to compute the personalized"value for money" views but rather some stylistic characteristics of the available for this consumer, and rank products accordingly. Notice that reviews. We examined two text-style features: "subjectivity the consumer has the incentive to reveal demographics in this and"readability"of reviews 5. Also, since prior research sug- gested that disclosure of identity information is associated with changes in subsequent online product sales [4, we measured EXAMPLE 2. For better understanding, let's re-consider the the percentage of reviewers for each hotel who reveal their real setting of the two hotels Al and A2 for city A fro name or location information on their profile web pages Examples 1. Suppose that two consumers are traveling to city A with an income $50,000-100,000, and C2, a 35-64 years 4.2 An Example: Personalized Hotel Search old family traveler, with an income less than $50, 000 Using the data described above, we are able to construct Since these two travelers belong to different demographic group our economic model and create a system that generates hotel and travel with different purposes, their preferences towards rankings. We estimate the mean and variance of the weights "conference center"and"pool"are different. Thus, the surplus that consumers assign to each hotel characteristic. Using these they obtain from Al and A2 varies. For example, the business estimates, we can derive the consumer surplus from each hotel aveler gets higher utility from Al due to the specialized confer ence center services, whereas the family traveler find A2 more We developed a prototype hotel search and ranking syster valuable due to the pool and price. and deployed it on Google App Engine. It consists of three basic components: a user search interface, a ary result page with the ranked hotels, and a(set of) explanatory web pages 4. A DEMO SEARCH ENGINE FOR HOTELS with details of each individual hotel listed in the results. First,a le instantiated our product search framework using as target customer is required to select the location of the trip destination application the area of hotel search. The demo is accessible at the type of the trip(e. g, business, family, romance, friend. ),and http://nyuhotels.appspot.com his/her income level via the search interface. Given the input 4.1 Data search criteria and the demographic information, the system computes the personalized consumer surplus for each hotel in First, to simulate the online search environment, we created the specified location and ranks the search results in descend one exhaustive data set using multiple data sources. order of consumer surplus (i. e, best value on top). The customer Demand data: Travelocity, a large hotel booking system, can review the list of search results and can click on the hotel to provided us with the set of all hotel booking transactions, get more information. In the detailed explanatory page of eachtravelers have β F conf = β F pool = 0.5. In other words, they have the same preferences regarding a pool and conference center. On the other hand, for business travelers, their preference towards “conference center” is much higher than towards “pool,” with β P conf = 0.9 and β F pool = 0.1, respectively. This estimation result can be further interpreted with mone￾tary meanings. For instance, we can infer that a business trip traveler is willing to pay $54 for the conference center and $6 for the pool, whereas a family trip traveler is willing to pay equally $30 for each of the two features. 3. SURPLUS-BASED RANKING So far, we have described the economic model used for in￾ferring the preferences of consumers using a utility model and aggregate demand data. This model uses the concept of surplus mainly as a conceptual tool to infer consumer preferences to￾wards different product characteristics. In our work, the concept of surplus is directly used to find the product that is the “best value for money” for a given consumer. We define Consumer Surplus for consumer i from product j as the “normalized utility surplus,” the surplus US¯ (i) j divided by the mean marginal utility of money ¯α. CSj = Normalized USj = X t 1 α¯ US¯ (i) j . (4) We thereby use the estimated surplus for each product and rank the products in decreasing order of surplus. Therefore, at the top we will have the products that are the “best value” for consumers, for a given price. Furthermore, we extend our ranking to include a personalization component. To compute the personalized surplus, we ask the consumer to give the appropriate demographic characteristics and purchase context (e.g., 25-34 years old, male, $100K income, business traveler) and then use the corresponding deviation matrices βT and αI . It is then easy to compute the personalized “value for money” for this consumer, and rank products accordingly. Notice that the consumer has the incentive to reveal demographics in this scenario. Example 2. For better understanding, let’s re-consider the previous setting of the two hotels A1 and A2 for city A from Examples 1. Suppose that two consumers are traveling to city A on the same day: C1, a 25-34 years old business traveler, with an income $50,000-100,000, and C2, a 35-64 years old family traveler, with an income less than $50,000. Since these two travelers belong to different demographic groups and travel with different purposes, their preferences towards “conference center” and “pool” are different. Thus, the surplus they obtain from A1 and A2 varies. For example, the business traveler gets higher utility from A1 due to the specialized confer￾ence center services, whereas the family traveler find A2 more valuable due to the pool and price. 4. A DEMO SEARCH ENGINE FOR HOTELS We instantiated our product search framework using as target application the area of hotel search. The demo is accessible at http://nyuhotels.appspot.com/. 4.1 Data First, to simulate the online search environment, we created one exhaustive data set using multiple data sources. Demand data: Travelocity, a large hotel booking system, provided us with the set of all hotel booking transactions, for 2117 randomly selected hotels over the United States. The transactions covered the period from November 2008 until Jan￾uary 2009. Based on the given transactions, we were able to compute the market shares of each hotel in each local market (i.e., metropolitan area), for each day. Consumer demographics: To measure the demographics of consumers in each market, we used data from the TripAdvisor web site: The consumers that write reviews about hotels on Tri￾pAdvisor also identify their travel purpose (business, romance, family, friend, other ) and their age group (13-17, 18-24, 25-34, 35-49, 50-64, 65+). Based on the data, we were able to identify the demographic distribution of travelers for each destination. Hotel location characteristics: We used geo-mapping search tools (in particular the Bing Maps API) and social geo￾tags (from geonames.org) to identify the “external amenities” (such as shops, restaurants, etc) and available public trans￾portation in the area around the hotel. We also used image classification together with Mechanical Turk to examine whether there is a nearby beach, a nearby lake, a downtown area, and whether the hotel is close to a highway. We extracted these characteristics within an area of 0.25-mile, 0.5 mile, 1-mile, and 2-mile radius. Hotel service characteristics: We extracted the service￾based characteristics from the reviews on TripAdvisor. Each review provides a general rating of the hotel, plus provides seven individual ratings on the following service characteristics: Value, Room, Location, Cleanliness, Service, Check-in, and Business Service. We computed the average ratings of each hotel across these seven characteristics and used them in our data set, together with the general review rating. We also used the hotel description information from Travelocity, Orbitz, and Expedia, to identify the “internal amenities” of the hotels (e.g., pool, spa.) Stylistic characteristics of online reviews: Finally, we extracted indicators that measure not the polarity of the re￾views but rather some stylistic characteristics of the available reviews. We examined two text-style features: “subjectivity” and “readability” of reviews [5]. Also, since prior research sug￾gested that disclosure of identity information is associated with changes in subsequent online product sales [4], we measured the percentage of reviewers for each hotel who reveal their real name or location information on their profile web pages. 4.2 An Example: Personalized Hotel Search Using the data described above, we are able to construct our economic model and create a system that generates hotel rankings. We estimate the mean and variance of the weights that consumers assign to each hotel characteristic. Using these estimates, we can derive the consumer surplus from each hotel, for a given customer. We developed a prototype hotel search and ranking system and deployed it on Google App Engine. It consists of three basic components: a user search interface, a summary result page with the ranked hotels, and a (set of) explanatory web pages with details of each individual hotel listed in the results. First, a customer is required to select the location of the trip destination, the type of the trip (e.g., business, family, romance, friend.), and his/her income level via the search interface. Given the input search criteria and the demographic information, the system computes the personalized consumer surplus for each hotel in the specified location and ranks the search results in descending order of consumer surplus (i.e., best value on top). The customer can review the list of search results and can click on the hotel to get more information. In the detailed explanatory page of each