hotel systems

7/29/2019 Hotel Systems

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Personalized Intelligent Hotel Recommendation

System For Online Reservation--a perspective of product and user characteristics

Xiong Yu-ningSchool of Economics and Trade

Xihua University

Chengdu, Sichuan, P.R. China, 610039

[email protected]

Geng Li-xiaoSchool of Management

Hebei University of Technology

Tianjin P.R.China, 300130

[email protected]

AbstractOnline hotel reservation makes travel easier, But a large

number of online hotel information also makes people become

unable to follow. Online hotel reservation websites need try to gain

exposure to customers eyes and recommend a personalized hotel

list. Nowadays, Personalized Intelligent Hotel Recommendation

Systems have been researched widely, but rarely in online hotel

reservation. The paper summarizes representative online hotelreservation websites personalized recommendation system

situation, such as qunar, kuxun, ctrip and elong etc. personalized

recommendation have poor performance. This research firstly

extracts Hotel Characteristic factor , attempts to analyze customers

browsing and purchasing behaviors and secondly constructs a

personalized online hotel marketing recommendation system

polymerization model for Multi-level customer ,at last presents an

achieve Matlab procedure implementing the core arithmetic of the

personalized recommendation.

Keywords-intelligient recommendation; personalized

preference; product characteristic; cluster; Matlab

I. INTRODUCTIONThe most successful e-business application is online travel

market. IResearch Consulting statistics show, China's onlinetravel reservation market income reaches 37.4 billion Yuan2009. Online travel reservation market still has grown 13.2%.the data indicates that with the development of online travelcompanies influence, as well as the convenience and pricetransparency, it brings consumers a better reservationexperience, more and more users choose to book travel

products online.

Online reservation hotels have its low cost advantage,lacking of purchasing guide providing users with productinformation and advice, it makes buyers need to spend more

time to find the product. At present, although travel site hasalso introduced a variety of recommended services, such as hot

product recommendation, promotion recommendation and soon, trying to achieve a similar effect of procurement assistant,

but with the requirement of personalized information serviceenvironment, the traditional recommendation system model of"to a class of products recommended to all" can not meetcustomers satisfaction. Users hope the recommendation

provided by tour e-commerce sites is "tailor-made", welltargeted, initiative and even intelligent, which reflect the userneeds of personalized recommendation system. Studies have

shown that with the help of personalized recommendationsystem, sales can be increased by 2% -8%.

According to annual industry report in 2009 on the onlinereservation market, some domestic representative travelwebsites which accounted for a larger share of the online hotel

reservation market, such as qunar, kuxun, ctrip and elong etc,have few personalized recommendation service. It is difficult tobring the user a satisfied personalized experience.

TABLE I. COMPARISON CHART OF DOMESTIC HOTEL RESERVATIONWEBSITE PERSONALIZED RECOMMENDATION SYSTEM

Recommendation A B C D E F G H

Hot product

Promotion

Destinations

New Product

Offline Correspondence

Others comments travel notes

Position

Persona-lizedRecom-

mendati

-on

Stayed in thehotel, but also

lived in

Interestingforecast

Recommend to

friends

Multi-recommendation

Browse HistoryCollection

A: Ctrip hotels.ctrip.com B: Elong www.elong.comC: Qunar www.qunar.com D: Kuxun www.kuxun.cn

E: Chinahotel www.chinahotel.com

F: Mangocity www.mangocity.comG: Aoyou www.aoyou.com H: Chunqiu www.china-sss.com

Table.1 shows that compared with the recommendationfunction. Qunar has the most personalized characteristic,followed by Ctrip and Elong. From the perspective of

personalized recommendations, only a few sites havepersonalized recommend service to customers. Someresearches show that hotel product recommendation is risky.

978-1-4244-5326-9/10/$26.00 2010 IEEE


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We believe that recommendation is a useful attempt to increasecustomers viscosity and upgrade the intelligence of travel site,it is a useful attempt.

II. RESEARCH BACKGROUNDA. Hotel product characteristic extraction

This research is based on product characteristics. Throughcustomers browse and transaction records, their preferencestoward specific product characteristics can be learned. Thoughdifferent people have their preferences, but the hotel productcharacteristic is stable and as objective as possible. Our aim isto find the stable and objective characteristic. Through a surveyof online hotel booking factors which users interested in , Thefollowing factors were selected. The characteristics areobtained from product database.

TABLE II. HOTEL PRODUCT CHARACTERISTIC

Term value in database

1Maximum price

2 Discount D=(8-Discount+1)/(3+1)

3 Class Value=12345

4

CuisineProviding cuisine of

characteristic value is 1,otherwise, the value is 0.

5

Free BreakfastProviding free breakfast of

characteristic value is1,otherwise, the value is 0.

6 Room amenities(domestic direct-dial,

International direct-dial

Providing Room amenitieof characteristic value is 1,

otherwise, the value is 0.7

BroadbandBy the fast and slow speedto give 5-point scale score

results

8

Pool/billiards roomHaving Pool/billiards roomof characteristic value is 1,otherwise, the value is 0.

9

Fitness facilitieshaving fitness facilities of

characteristic value is1,otherwise, the value is 0.

K-

1

Credit cards accepted by

hotel(China UnionPayVisa, Master)

China UnionPay is 1 ,

Visa, Master of value is 2,all of them is 3, otherwise

is 0.K

comments5-point scale

comprehensive scoreresults

The product characteristic can be expressed in Matrix S informula (1) :

S(m)=(S11,S12,S1j,S21, S2j,.,Sij)

i = 1,,L j=1,..k (1)

In formula (1),i stands for total number of products ,kstands for total number of products characteristic. Also, Sij isthe value of the ith products jth characteristics.

B. Customer profile moduleIn this section, a new customer profile model is proposed

reflecting product characteristics. It is necessary to understandcustomer interests and preferences and then provide suitablehotel products or services at an adequate time. A goodrecommendation system must be relied on in order torecommend products or services of interests. Lee and Yang(2003) pointed out that the most important issue in

personalization research is to construct a computational modelfor each individual user to predict his preferences for incominginformation.

For travel Web site, customers type is often different. Websites for different types of customers should provide different

personalized recommendation. Customers are divided intothree categories: primary users, Intermediate users and

advanced users.Primary users are usually the first time to visit the site,

when products meet their demand, they would like to purchasethe hotel, so no purchase records and comment records; TheCustomer profile module characteristics are obtained fromusers browse history. For Intermediate users, they have

purchase and comment record. Customer profile module dataare on the composition of their purchase and comment data.For advanced user, they have been booked hotel for manyyears. Customer profile module data not only are on thecomposition of their purchase and comment data, but also thetime record.

TABLE III. CUSTOMER PROFILE PARAMETER MATRIX

Primary user Intermediate user Advanced user

SNi=( SN1SN2SNk (i=1

2..k)

SIi= (SI1SI2

SIk), (i=1

2..k)

SLi= (SL1SL2

SLk,T), (i=1

2..k)

III. RESEARCH METHODOLOGIESIn this section, we describe our method in detail by

presenting a conceptual framework to carry out personalizedhotel recommendation.


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A. Model framework

The research structure is shown in Fig.1. For findingcustomer interests, the procedures are divided into product

profile module and customer profile module. In the

recommendation modules, products not yet purchased or rarelypurchased can still be recommended to customers with browsehistory. Product profile module mainly finds productcharacteristics in the product database, where characteristics of

products constitute the profile. In customer profile module,product characteristics from customers past browse andpurchase records are drawn for customer profile. Thencustomer profile and product profile are later analyzed forsimilarity, and candidate products of recommendation are thusgenerated. This mainly attributes to the research based on

products purchased in the past, thus the recommendations arenot likely to miss products out.

B. Recommended model for the primary usersPrimary users are often to visit the site in unregistered

capacity, no purchase records and comment records, acollection of resources which the current user has access to orresource requirements submitted online can de treated as his

browse trace, to analyze the customers preferences based onproduct characteristics, because the similarity between resourceis nature and more stable, the similarity can are calculated off-line store and regularly updated, the algorithm can solve thescalability problem. Then select similar hotels which havent

been visit from product to recommend to customer. Primaryusers Model consists of the following three steps.

Step 1: Define the product characteristics data S(m), matrixS(m) can be gained in product database as formula (1 ). Forexample, a product matrix S constructed for a booking site; thenumber of the product L is 21, and the number ofcharacteristics is chosen from Table.2, the number of thecharacteristics k is 11.

=

43.113.210131266

4.21003.300120.25-108

3.71003.300120.25-88

5000000010.25-68

4.4311510150.51318

4.63113.811150.25760

4.8311510140.25-412

3.5210311140433

4101300120.25-167

3.7101211030.25-285

4000201120.25-233

2.7311410131288

3.71003.300120177

5000000010.25-98

4.93114.510150.51118

4.6311411150.25760

4.8311510140.25-420

3.4211311140467

5101400120.25-198

4.8101211030.25-303

4000201120.25200

S

Step 2: To compute an individuals interests, this modelanalyzes the customer browse histories that included clicks,

basket insertions fields. Users browsing records can beselected, for example, the user the has browsed the 1st, 8th andthe 9th record, the record number l is 3.Construct customer

browse histories matrix SN as bellow,

=

3.71003.300120177

5000000010.25-98

4000201120.25200

SN

Step 3: Assumes that the user browsing history is based onthe case of his near preferences. Such as the user wants to finda hotel can provide free breakfast or a hotel has a fast speed

broadband service. So the difference of at least onecharacteristic is small. Calculate the mean value of each

characteristic of the product, get X as following:

=

===

,

.....,........., 112

1

1

l

SN

l

SN

l

SN

SA

l

i

ki

l

i

i

l

i

i

(2)It is calculates by Matlab, get the result of X:

SA=[ 158.3333 0 1.6667 0.6667 0.3333 0 1.7667 0 00.3333 4.2333]

Step 4: To analyze the users interesting, by matrix SA, theusers average characteristic preference is indicated. SA joinsSN to a new matrix X.

X=[SN:SA] (3 )

The value of SA can be treated as the 22nd record. Byformula (3), calculated X as following:

Feedbac

m

ealgorithm

Long-

termusersprofilemodul

Similarity

Usersprofile

Productcharacteristic

Recommendation

Cluster profile

Sorting

Customer

Users rowseand transaction

Figure 1. Research model framework


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=

4.23330.3333001.766700.33330.66671.66670158.3333

43.113.210131266

4.21003.300120.25-108

3.71003.300120.25-88

5000000010.25-68

4.4311510150.51318

4.63113.811150.25760

4.8311510140.25-412

3.5210311140433

4101300120.25-167

3.7101211030.25-2854000201120.25-233

2.7311410131288

3.71003.300120177

5000000010.25-98

4.93114.510150.51118

4.6311411150.25760

4.8311510140.25-420

3.4211311140467

5101400120.25-198

4.8101211030.25-303

4000201120.25200

X

Step 5: Standardized X, calculate the average and theStandard deviation of every characteristic, and calculate everystandardized data.

)(1

1 1

1

+

=+=

L

i

kik xL

x (4)

+

=

+

=

1

1

2)(1

1 L

i

kkikxx

Ls

(5)

Then get the standard data'

kix

can be expressed as formula

(6):

k

kki

s

xxx

ki

=

' (6)

After calculating, standardized data BX, results are shownas following:

=

0.00731.0584-0.8919-1.1742-0.9133-1.0703-0.0936-0.3462-0.9961-0.1142-0.6619-

0.3679-1.15921.07030.81290.10430.89190.7798-0.50010.01142.39730.3381-

0.0463-0.5040-0.8919-1.1742-0.17531.0703-0.7798-0.50010.7442-0.7420-0.8133-

0.8502-0.5040-0.8919-1.1742-0.17531.0703-0.7798-0.50010.7442-0.7420-0.8735-

1.24001.3357-0.8919-1.1742-2.1676-1.0703-0.7798-2.0389-1.4999-0.7420-0.9336-

0.27531.15921.07030.81291.38230.89190.7798-0.50011.52281.14152.8258

0.59691.15921.07030.81290.53030.89191.27890.50011.52280.51371.1476

0.91841.15921.07030.81291.38230.89190.7798-0.50010.76710.7420-0.1010

1.1718-0.32761.07031.1742-0.0377-0.89191.27890.50010.76710.1142-0.1641

0.3679-0.5040-0.8919-0.81290.0377-1.0703-0.7798-0.50010.7442-0.7420-0.6359-

0.8502-0.5040-0.8919-0.81290.7476-0.89191.27892.0389-0.01140.7420-0.2810-

0.3679-1.3357-0.8919-1.1742-0.7476-1.0703-1.27890.50010.7442-0.7420-0.4374-

2.4581-1.15921.07030.81290.67230.89190.7798-0.50010.01142.39730.2720-

0.8502-0.5040-0.8919-1.1742-0.17531.0703-0.7798-0.50010.7442-0.1142-0.6058-

1.24001.3357-0.8919-1.1742-2.1676-1.0703-0.7798-2.0389-1.4999-0.7420-0.8434-

1.07921.15921.07030.81291.02730.89190.7798-0.50011.52281.14152.2243

0.59691.15921.07030.81290.67230.89191.27890.50011.52280.51371.1476

0.91841.15921.07030.81291.38230.89190.7798-0.50010.76710.7420-0.1250

1.3326-0.32761.07030.81290.0377-0.89191.27890.50010.76710.1142-0.2664

1.24000.5040-0.8919-0.81290.67231.0703-0.7798-0.50010.7442-0.7420-0.5426-

0.91840.5040-0.8919-0.81290.7476-0.89191.27892.0389-0.01140.7420-0.2268-

0.3679-1.3357-0.8919-1.1742-0.7476-1.0703-1.27890.50010.7442-0.51370.5366-

BX

Step 6: To find which hotel has the similar characteristicwith the browse history, we use cluster analysis method to

judge which hotel is the same group with browse history. Socalculate the similarity of product and SA, Euclidean distancemethod has been used. The similarity can be expressed by Rij

0Rij1i,j=1,2,3,4,5,L+1and get the Fuzzy matrix Y

+

=

=

+

=

)()(1

11

)(1

1

1

2'' jixxL

ji

R L

k

kjki

ij

(7)

Step 7: Given threshold , use shortest distance method toclassify the product

hotel systems

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