A comprehensive mechanism for hotel recommendation to achieve personalized search engine

2018·0.56 MB·English

by  Ying Huang, Hong-Yu Zhang, Jian-Qiang Wang;

JournalofIntelligent&FuzzySystems35(2018)3733–3745 3733
DOI:10.3233/JIFS-18547
IOSPress
A comprehensive mechanism for hotel
recommendation to achieve personalized
search engine
Y
∗
YingHuang,Hong-YuZhang andJian-QiangWang
P
SchoolofBusiness,CentralSouthUniversity,Changsha,China
O
C
Abstract.Searchenginesareasimportantasrecommendersystemsforhotelselections.However,therecommendedlists
of search engines are usually non-personalized and low accuracy. In order to deal with these issues in search engines,
a comprehensive mechanism for hotel recommendation is prop osed. In this mechanism, we consider users’ personalized
preferences by identifying users’ attributes about interest, trusRt and consumption capacity. Meanwhile, the quantification
methodforeachattributeispresentedbyusingfuzzytheory.Moreover,thispaperimprovesthemethodtoevaluatethehotel,
whichrespectstothecriteriaprice,rating,andonlinereviewbyusingfuzzytheory.Inaddition,thisproposedapproachuses
TOPSIS,aclassicalmulti-criteriadecisionmakingmethod,Otoimprovetheaccuracyfurther.Finally,acasestudyisconducted
basedonTripadvisor.comtoillustratethevalidityoftheproposedmethodforhotelrecommendationinsearchengines.The
resultsofthecasestudyindicatethatitnotonlysolvestheproblemofnon-personalization,butalsoimprovestheaccuracy
insearchengine. H
Keywords:Hotelrecommendation,TOPSISmethod,searchengine,onlinereviews,price
T
U
1. Introduction menditemsone-commerceplatformsforconsumers.
A Both recommender systems and search engines can
The development of electronic-commerce (e- augmentthepossibilityofonlinepurchasing.
commerce) and social media has changed users’ At present, most of the researches focus on rec-
styles for booking hotels. Tourists are accustomed ommender systems, few of them study how to
to booking hotels in advance on e-commerce plat- improve search engines. However, there are some
forms,suchasHolidayInn.com,ctrip.comandsoon. obvious issues in search engines. Usually, search
However,thelargequantity,variedpricesandragged engines cannot provide personalized recommended
qualityofhotelsmakeitisdifficultforuserstofind lists. Nevertheless, the item recommendations by
satisfactoryhotels.Recommendersystemscanfilter search engines are mainly based on the ratings.
thevastamountofinformationinnetworkstoassist Besides,evenifconsumersarenotsatisfiedwiththe
consumerinmakingthebestchoices[1–6].Besides items,theystilltendtogivehighscoresduetothecon-
the recommender systems, search engines are the sumers’ rating habits or in order to avoid malicious
othercommontoolstofilterinformationandrecom- harassment caused by giving low scores. It causes
mosttheratingsofitemsareconcentratedin4or5(on
the 1–5 scale). Traditional recommendation meth-
∗Correspondingauthor.Hong-YuZhang,SchoolofBusiness,
ods usually use the average ratings to rank hotels,
CentralSouthUniversity,Changsha410083,China.Tel.:+86731
88830594;Fax:[phone];E-mail:[email protected]. and the difference of average ratings among hotels
1064-1246/18/$35.00©2018–IOSPressandtheauthors.Allrightsreserved
3734 Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine
isoftenslight.Inthecircumstances,itisdifficultto neutrosophic numbers instead. In this study, on the
distinguish the quality of hotels [7] on the basis of basis of research in [25], we will use the sentiment
average ratings, which in turn causes low accuracy analysistechnologytotransformonlinereviewsinto
of the recommendations for search engines. In this singlevaluedneutrosophicnumbers(SVNNs).
study, we will propose a new method to deal with Apartfromratingsandonlinereviews,ithasbeen
theratingsandacomprehensivehotelrecommenda- proved that price is a pivotal factor that influences
tion method to improve the performance of search consumers’ decision-making [26–30]. Few studies
engines. have applied the price in product or hotel recom-
The basic framework of consumer’s purchasing mendation.Largelybecauseofregionaleconomyand
decision-making in the context of online shopping personalincomes,differentconsumersmayhavedif-
is same with that of off-line way. The decision- ferentopinionsonthequestionwhetherthepriceof
Y
makingprocessofitemsselectionisthemostcritical ahotelisexpensiveornot.Theresultisthatitcan-
stage in the process of consumer’s online shopping notdirectlyrelyonthevalueofthepricetoevaluate
[8].Atthisstage,consumersneedtocompare,ana- or recommPend hotel. Besides, due to the impact of
lyze and evaluate the selected items according to various factors, such as social position, vanity and
the purchase criteria, so that they can make a judg- income, different consumers have different prefer-
O
ment of the quality of the items, to generate the ences for products’ prices. One point to be sure is
final evaluation. The researches of the current rec- that,forthesameconsumer,hisconsumptioncapac-
ommendation methods often focus on the demand itywillbeconsistentforalongtime.Thehigherthe
C
evokingpart,whichignoretheprocessofconsumer similarity between the hotel’s price and customer’s
decision.Withrespecttosearchengines,consumers consumptioncapacity,thegreaterthelikelihoodfor
providetheirdemandsdirectlybyenteringkeywords. thehoteltobebookedbythecustomer.
Therefore, there is a relatively low requirement on RBased on the researches outlined above, in the
identifying the demands of consumers. In addition, decision-making process of item selection, we will
decision-making process of items selection is more combinetheimpactofrating,onlinereviews,aswell
O
important in the methods for search engines than aspricetoimprovetheaccuracyofsearchengine.
in the traditional recommendation methods. In this In the hotel recommendation field, some studies
study,wewillamelioratesearchenginesbyresearch- are devoted to the excavation of hotel evaluation
H
ingdecision-makingprocessofitemsselection. rules. Yu and Chang [31] designed a hotel evalua-
Thedecision-makingprocessofitemsselectionis tionrulewithfivecriteriaincludingdistance,traveler
influenced by many factors for consumers T[9–11]. preference,roomrate,facilities,andratingthesefive
Theratingtowarditemisthemostusedcriterionfor criteria.Levietal.[32]minedhotelreviewstodeter-
item selection [12, 13]. However, most studies use minetheimportanceofeachcriterionofhotels.Some
U
onlyacomprehensivescore,whichresultsinaseri- literaturesdesignedpersonalizedhotelrecommenda-
ouslossofinformation[14,15].Inrecentyears,with tionmethodsbygivingdifferentweightsfordifferent
thedevelopmentoftextanalysistechnAology,several criteriabasedonthetargetuser’spersonalizedprefer-
researcheshavecombinedratingsandonlinereviews ence[33,34].Nevertheless,mostexistingresearches
toselectitems[16,17]. givethesameweightsforalluserswhohavecomment
As online reviews are in the form of text, which on the hotels. Actually, for a target user, the ratings
contain more information than ratings, more and andonlinereviewsofsimilarusershavemoreinflu-
more scholars have studied the impact of online encethanthoseofdissimilarusers.Therefore,inthis
reviewsandappliedthemtoimprovetheperformance study,weneedtomineusers’preferences,toidentify
of recommender systems [18–21]. Most of them similar group, to adjust the weights of groups with
utilizeonlinereviewstoidentifyusers’oritems’pref- differentsimilaritiestotargetuserintermsofratings
erences by identifying keywords in online reviews, andonlinereviews,andtodealwiththeproblemof
then use these keywords as the properties of items non-personalizationinsearchengine.
or users [22, 23]. Some other researches use online Fuzzytoolshavebeenusedtomodeluncertainand
reviewstoevaluateitems[24].Zhangetal.[25]pro- vaguepreferencesinrecommendersystems[34,35].
posedamethodstoquantifyonlinereviewsbyusing Usually, the fuzzy sets used in different studies are
neutrosophictheory.Intheirstudy,theydidnottrans- differentbecauseofthedifferentdataformsandrec-
late online reviews into the neutrosophic numbers ommendation strategies. Combining the features of
actually, but translated ratings into interval-valued the collected data and the feature of each fuzzy set,
Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine 3735
thispaperwillusetheappropriatefuzzysettoquan- 1) A+B={(cid:3)x, μ (x)+μ (x)−μ (x)μ (x),
A B A B
tifyprices,ratings,andonlinereviews.Theformof v (x)v (x)(cid:4)|x∈X},
A B
informationabouteachcriteriontoevaluatehotelsis 2) A·B={(cid:3)x,v (x)+v (x)−v (x)v (x),μ (x)
A B A B A
different,sothemethodofquantificationvariescon- μB(x)(cid:4)|(cid:2)x(cid:3)∈X}.
siderably with criteria. For this reason, aggregating 3) λ·A= x, 1−(1−μ(x))λ, 1−(1−v
(cid:4) (cid:5) A
thecriterionevaluationvaluesdirectlytosorthotels (x))(cid:2)λ(cid:3) |x∈X , (cid:4)(cid:5)
isnotefficient.Wecouldrankhotelsandrecommend 4) Aλ = x, μ λ(x), v λ(x)|x∈X .
A A
thembyusingtheTOPSIS(TechniqueforOrderPref-
erencebySimilaritytoSolution)method,tosolvethe
multi-criteriadecisionmakingproblems[36–39]. Definition 3. [41] Let A={(cid:3)x, μ (x), v
A A
Theremainderofthispaperisorganizedasfollows. (x)(cid:4)|x∈X}, B={(cid:3)x, μ (x), v (x)(cid:4)|x∈X}
B B
Y
InSection2,webrieflyreviewsomebasicconcepts are two IFSs on X, then the normalized Euclidean
relatedtothisstudy.Section3proposesacomprehen- distancebetweenAandBcanbedefinedas:
P
(cid:6)
(cid:7)
(cid:7) (cid:9)n (cid:10) (cid:11)
d (A, B)=(cid:8) 1 (μ (x)−μ (x))2+(v O(x)−v (x))2+(π (x)−π (x))2 (1)
IFs A B A B A B
2n
i=1
C
Definition4.[42]LetXbeauniverseofdiscourse,
sivemechanismforhotelrecommendation.Toverify
thensinglevaluedneutrosophicsetainXisdefined
the feasibility of the method, Section 4 conducts a
Ras: a={(cid:3)x, Ta(x), Ia(x), Fa(x)(cid:4)|x∈X}. Ta(x),
casestudyusingdatafromTripAdvisor.com.Finally,
I (x), F (x) are the degrees of truth-membership,
a a
Section5concludesthestudyandsuggestsdirections
indeterminacy-membershipandfalsity-membership,
forfutureresearch. O
respectively.ThevaluesofT (x),I (x)andF (x)lie
a a a
in[0,1],forallx∈X.Theelementinsetaiscalled
singlevaluedneutrosophicnumber(SVNN).
2. Basicconcepts H
Definition 5. [43] Let A and B be two SVNNs, for
In this section, we briefly review the defini- anyx∈X,therearesomeoperations,anddefinedas:
T
tions of the intuitionistic fuzzy set (IFS), the single
valued neutrosophic set (SVNS), and the hesitant 1) A+B=(cid:3)TA+TB−TATB, IA+IB−
fuzzy linguistic term set (HFLS), as weUll as their IAIB, FA+FB −FAFB(cid:4),
operations and distance methods. These definitions 2) A·B=(cid:3)(cid:3)TATB, IAIB, FAFB(cid:4),
will be used in the proposed hotel recommendation 3) λ·A= 1(cid:4)−(1−TA)λ, 1−(1−IA)λ, 1−
A
method. (1−F(cid:3)A)λ ,and (cid:4)
4) Aλ = T λ, I λ, F λ .
A A A
Definition 1. [40] Let X be a nonempty clas-
sical set, X={x , x ,..., x }, the intuitionistic
1 2 n
fuzzy set defined on X is represented as: A= Definition6.[44]LetAandBbetwoSVNNs,then
{(cid:3)x, μ (x), v (x)(cid:4)|x∈X}, where μ (x):X→ the Euclidean distance between A and B is defined
A A A
[0,1] and v (x):X→[0,1] are the membership as:
A
degree and non-membership degree of A, and 0≤
d (A, B)
μA(x)+vA(x)≤1. H (cid:12)
ForeveryIFSonX,π (x)=1−μ (x)−v (x) 1(cid:10) (cid:11)
A A A = (T −T )2+(I −I )2+(F −F )2 .
istheintuitionisticindexoftheelementxinA,and 3 A B A B A B
0≤π (x)≤1, x∈X.
A (2)
Definition 2. [40] Let A={(cid:3)x, μ (x),v
A A
(x)(cid:4)|x∈X},andB={(cid:3)x, μ (x), v (x)(cid:4)|x∈X} Definition7.[45]introducedasubscript-symmetric
B B
be two IFSs on X, the operations of A and B are linguistic evaluation scale, which can be defined as
definedas: follows:S ={s−τ,...,s−1, s0, s1,...sτ}.
3736 Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine
Definition 8. [45] Let s and s be two linguistic
a b
terms, then the distance measure between s and s
a b
is:
|a−b|
d(s , s )= . (3)
a b 2τ+1
Definition 9. [46] S = {s−τ, ..., s−1, s0, s1, ...
s }isalinguisticscalesetincluding2τ+1linguis-
τ
tic terms, then a set of finite ordered elements in
the set S is called a hesitant fuzzy linguistic term
set (HFLS), denoted as H : Hs={s |s ∈S, i= Y
S βi βi
1, 2,..., n}, s is a linguistic term, an element in
βi
H . βi is the subscript of s , and −τ ≤βi≤τ. n
S βi P
representsthenumberofelementsinH .
S
Definition 10. [47] Let H1 and H2 be two HFLSs, O
s s
theHammingdistancebetweenH1andH2isdefined
s s
as: Fig.1. Theframeworkoftheproposedmethod.
d(cid:13)H1,H2(cid:14)= 1 (cid:9)n (cid:15)(cid:15)αj −βj(cid:15)(cid:15), (4) C
s s n 2τ+1 isoneoftheindispensablestepstoprovideuserswith
j=1 accurate recommendations. This study will extract
whenthenumbersofelementsintwosetsarediffer- Ruserinterestattributefromuser’spurchasingrecords
ent,complementthesetwithfewerelements. andonlinereviews.
Thestepsofinterestrecognitionareasfollows.
O
(i) For user u, gather all titles of hotels user u
3. Acomprehensivemechanismforhotel
stayedanduser’sonlinereviewstoformalong
recommendation
H text,denotedasT .
u
(ii) For each long text, eliminate irrelevant words
Inthissection,weproposeacomprehensivehotel
such as stop words and so on, and carry out
recommendationmethodtoimprovetheperforTmance
wordsegmentationandwordfrequencystatis-
of search engine. The framework of the proposed
tics. Then, classify the high-frequency words.
methodisshownasFig.1. U
Because the same interest can be expressed
Thedetailsoftheproposedmethodwillbeexpati-
in different ways, it is necessary to sort out
atedintherestofthissection.
thehigh-frequencywordsfurther:classifysyn-
A
onymsintoonecategory,anduseeachcategory
3.1. Thequantificationofuserattribute
vocabularyasafeatureofuseru’sinterest,the
featureofuser’sinterestdenotingasf .
u
In a socialized business environment, users’ data
(iii) For each T , count the frequency of each
u
have increased exponentially. User’s purchasing
feature f appearing in the text T , and
u u
records, browsing records, ratings, online reviews,
denote as freq . Thus, the interest attribute
u
tags and other data, to some extent, reflect user’s
for user u can be represented by the collec-
interest, trust, consumption capacity and other tionofallthefeatures,representedasF(u)=
personalized information. How to utilize massive {f , f ,...f ..., f }, where f is the
u1 u2 ui un ui
unstructured information, to mine user’s attribute ith feature of interest for user u, freq(u)=
information accurately and effectively is one of the {freq , freq ,...freq ..., freq } is a
u1 u2 ui un
key issues in this study. In this part, we will intro-
set of the frequency corresponding to the fea-
ducethequantificationmethodsforuser’sattributes
tureF(u).
indetail.
User interest. User interest directly reflects con- User trust. At present, the number of users on e-
sumer’spurchasepreference.Miningusers’interest commerce websites is huge. Some users are false
Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine 3737
golfer326
Since Nov 2014 Jordan
50-64 year old
female
the data reflects how many items
user “golfer 326” has commented.
the data reflects how many users Y
think the reviews are useful
P
Fig.2. Themechanismtoevaluateuser’strustonTripadvisor.com.
O
users,whosepurchasingandcommentbehaviorsare
irregularanduntruthfulness.Theonlinereviewsand
ratings of these users are unconvincing. Their trust CFig.3. Keywordsrepresenteduser’sinterests.
degreesarelow,andtherecommendationsbasedon
thehistoricrecordsofthemareinaccurate.Thelower s−4=“Incomparable Cheap”, s−3=“Significantly
thetrustofuser,thelowerthereliabilityoftheuser, Cheap”, s−2=“Cheap”, s−1=“Somewhat Cheap”,
and the lower the accuracy of the recommendation Rs =“Medially”, s =“Somewhat Expensive”,
0 1
basedonhishistoricrecords.Therefore,thetrustof s =“Expensive”,s =“SignificantlyExpensive”and
2 3
userisalsoimportanttotheidentificationofsimilarOs4=“CertainlyExpensive”.
group,andtheusersinsimilargrouparewithahigh Secondly,collectthepricedistributionsofdiffer-
degree of trust. Many e-commerce platforms have ent cities, and divide the prices of hotels in each
the mechanisms to evaluate user’s trust, for exaHm- city into 9 levels corresponding to linguistic terms
ple, Fig. 2 shows the mechanism to evaluate user’s S. For the same city, the higher the price of hotel
trust on Tripadvisor.com. The more the number of user booked, the stronger the consumption capacity
“helpful votes” a single review gets, the mTore the ofuser.Finally,foreachuser,consumptioncapacity
trustworthytheuserpossesses.Bycomparison,some isdenotedasH intheformoflinguistictermsets.
u
e-commerce platforms do not have the meUchanisms
toevaluateuser’strust. 3.2. Thedefinitionofsimilargroup
Fortheformer,thereisthestandardizationofuser’s
A
trust: Inthisstudy,weidentifyconsumerswithhighsim-
t ilarity in terms of interest, trust, and consumption
trust = u , (5)
u max(t) capacity as similar group, and they tend to have a
similarpurchasingpreference.
wheretuisthetrustofuseruonthee-commerceplat- (1)Generally,themoresimilarthecharacteristics
form,max(t)isthemaximumvalueinallusers’trust. ofhotelsandtheaspectswhencommentingonhotels,
Forthelatter,thetrustquantitativemethodneedsto the higher the preference similarity between users.
befurtherstudiedinthefuture. Theinterestpreferencesimilaritybetweentargetuser
uandtheuservcanbecomputedasfollow:
User consumption capacity. Similar users have
similarconsumptioncapacities.User’sconsumption Sim int(u, v)
⎧
chaapsasctaityyedca.Innbtheirsesfltuedctye,dwbeywtihlleuptirliiczeeslionfghuoistetilcstuesrmer ⎪⎪⎨(cid:20)m (1−|frequj−freqvj|)
setFsitrostelyx,praessssuumseerst’hcaotntshuemreptiiosnacaspeatciLtieso.f nine =⎪⎪⎩j=1 max(N0u,,Nv) ,NNu =/=00aonrdNNv==/00,
linguistic terms S to depict the price, where L= u v
{s−4, s−3, s−2, s−1, s0, s1, s2, s3, s4}, where (6)
3738 Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine
whereN , N arethenumbersofinterestfeaturesfor G ={v|sim all(u, v)<θ }. (12)
u v 3 2
useruanduserv;jdenotesthejthcommoninterest
In this study, for G , G , G , the weight of each
featureforuseruanduserv;misthetotalnumberof 1 2 3
groupisdefinedasfollow:
commoninterestfeaturesforuseruanduserv,freq
uj
and freq are the frequencies of the jth common
vj sim average(u, G)
interestfeatureinuseruanduservinterestfeatures, wi = (cid:20)3 i , (13)
respectively. sim average(u, G)
i
(2)Thegreaterthevalueofuser’strust,themore l=1
the target user trusts another user, which means the
whereuseruisthetargetuser,sim average(u, G)
i
higherthetrustsimilaritydegreebetweentargetuser
istheaverageofthesimilaritiesbetweenmembersin
and the other user. In addition, the greater the trust
groupG andthetargetuser.
i Y
valueofthetargetuserhimself,thehighertherequire-
mentintrustfortheusersinthesimilargroup.The
3.3. Thequantificationofhotelevaluation
methodofcalculatingthetrustsimilarityisshownas: P
criteria
Sim trust(u, v)
(1+min(trust , trust ))×trust In oOrder to improve the accuracy of recommen-
= u v v, (7) dations, this study comprehensively considers three
1+trust(u)
factors,whicharerating,onlinereviewsandpriceas
where trustu, and trustv denote the trust degrees of hotCelevaluationcriteria.
useruanduserv,min(trustu, trustv)istheminimum Rating.Theratingreflectsthesatisfactiondegree
valueintrustu,trustv. of a user towards a hotel. For a hotel, some users
(3)Foruseruanduserv,theirconsumptioncapac- rateitwithhighvalue,whileothersgivelowratings.
R
itiesaredenotedasHuandHv,thentheconsumption Simply seeking the average rating of all users just
capacitysimilaritybetweenthemisdefinedas reflectslittleinformation.Inordertoretaintherating
Opreferences of different users for the hotel as much
sim c(H , H )=1−d(H , H ). (8)
u v u v aspossible,foreachgroup,weuseanintuitivefuzzy
Then, for user u and user v, the comprehensive numbertoexpressthesatisfactionofgrouptowards
similarityiscalculatedasfollow: H hotel.
Sim all(uv) IF(G)={(cid:3)μ(G), v(G)(cid:4)}. (14)
i i i
Sim int(u, v)+Sim trust(u, v)+Sim c(HT, H )
= u v . Inmostoftheelectroniccommerceplatforms,the
3
rating scale ranges from 1 to 5. A score of 4 or 5
U
(9)
represents that the user likes the hotel, and a score
of 1 or 2 represents that the user does not like the
According to the comprehensiveAsimilarity of hotel. Therefore, when the scale is in the range of
users,wedividetheusersintothreegroups: 1 to 5, the ratios of ratings above 3 and less than 3
inthegroupindicatethatthedegreeofmembership
• Similar group (G ): the user whose user simi-
1 andthedegreeofnon-membershiptowardshotel.G
1
larity is higher than θ is a member of similar
1 represents similar groups. G means weak similar
2
group.
groupandG meansdissimilargroup.
• Weak similar group (G ): the user whose user 3
2 Comparedtouserswithlowsimilarity,userswith
similarityislessthanθ andhigherthanθ isa
1 2 highsimilarityhaveagreaterimpactonthepurchas-
memberofweaksimilargroup.
ing decision of target user. The ratings and online
• Dissimilargroup(G ):theuserwhoseusersim-
3 reviews of similar group are more important than
ilarityislessthanθ isamemberofdissimilar
2 thoseofweaksimilargroupordissimilargroupare.
group.
Foreachgroup(G ,G ,G ),wecancalculatetheir
1 2 3
Theequationtoidentifyeachgroupisasfollows: weightsbasedonEquation(13).Bygivingdifferent
groupsdifferentweights,itnotonlymakestheresult
G1 ={v|sim all(u, v)>θ1}, (10) moreaccuratethandirectlycalculatingtheaverageof
allusers’ratingsandonlinereviews,butalsomakes
G ={v|θ <sim all(u, v)<θ }, (11) therecommendationresultpersonalized.
2 2 1
Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine 3739
Thecomprehensiveratingforalluserstowardsthe is used to express the target user’s consumption
hotelisIF(G): capacity.
(cid:9)3
IF(G)= wIF(G). (15) 3.4. Themodelofrecommendationbasedon
i i
TOPSIS
i=1
Becausetheexpressionsofthreecriteriatoevaluate
Online reviews. Because one online review may
hotelsaredifferent,criterionvaluescannotbeaggre-
havepositive,neutralandnegativeevaluationsatthe
gateddirectlybyaggregationoperators.TheTOPSIS
same time. In order to depict online reviews appro-
methodaggregatesallcriteriabasedondistancemea-
priately and reduce the loss of information, we use
sure,andsortsthealternativesaccordingtothedegree
singlevaluedneutrosophicnumberstoexpressonline Y
ofcloseness.Thereisnospecialrequirementforthe
reviews. According to the definition of SVNNs, the
data form of each criterion value. In this part, we
online review of user u on the hotel i is expressed
as R˜ =(cid:3)T , I , F (cid:4). T indicates the positive will sort hPotels by TOPSIS method and form final
ui ui ui ui
recommendedlist.Thefollowingarethesteps:
degreeofthepositiveonlinereview,andI indicates
(1)Obtainthepositiveidealsolutionandtheneg-
theneutraldegreeoftheneutralonlinereviewandF O
ativeidealsolution.
indicates the negative degree of the negative online
The positive ideal solution consists of the posi-
review.
Further, we regard all users in the same group as tiveCideal value of each criterion, and the negative
idealsolutionconsistsofthenegativeidealvalueof
onevirtualuser.Thentheonlinereviewsofusersin
each criterion. For the criterion of rating, the pos-
the same group are regarded as one online review.
itive ideal value is (cid:3)1,0(cid:4), which means all users
TheonlinereviewforgroupG isdenotedas:
i Rhave high ratings on hotel (4 or 5, in a scale of
R˜ (Gi)=(cid:3)T (Gi), I(Gi), F(Gi)(cid:4). (16) 1–5). For the criterion of online review, the posi-
tive ideal value is (cid:3)1,0,0(cid:4), which means all online
Thenallusers’onlinereviewsarecalculatedas: O
reviews contains only positive comments, in which
(cid:9)3 thepositive-membershipdegreeoftheonlinereview
R˜ (G)= wiR˜ (Gi). (17) is equal to 1. For the criterion of price, the positive
H
i=1 idealvalueis1,whichmeansthesimilaritybetween
the price of hotel and the consumption capacity of
Price.ThequantitationmethodofpriceisTsimilar useris1.Thenegativeidealvalueiscompletelyoppo-
to that of consumption capacity in Section 3.1. sitetothepositiveidealvalueundereachcriterion.
First,definethesetoflinguistictermsthatUrepresent Then,thepositiveidealsolutionA+andthenega-
the price. Assume that there is a set S of nine tiveidealsolutionA−aredescriptedasfollows:
linguistic terms to depict the price, where S =
{s−4, s−3, s−2, s−1, s0, s1, s2, s3,As4}, s−4= A+ ={(cid:3)1,0(cid:4),(cid:3)1,0,0(cid:4),1}, (19)
“Incomparable Cheap”, s−3=“Significantly
Cheap”, s−2=“Cheap”, s−1=“Somewhat Cheap”, A− ={(cid:3)0,1(cid:4),(cid:3)0,1,1(cid:4),0}. (20)
s =“Medially ”, s =“Somewhat Expensive”,
0 1
(2) Calculate the distance. For each alternative
s =“Expensive”, s =“Significantly Expensive”
2 3
hotel,calculatethedistancebetweeneachalternative
and s =“Certainly Expensive”. Then, collect the
4 +
andthepositiveidealsolutionD ,aswellasthedis-
price distribution of hotels in each city, and divide i
tancesbetweeneachalternativeandthenegativeideal
the price into 9 levels corresponding to the price
−
solution D . For the criteria rating, online reviews
linguistic term of each city. The price of each hotel i
and price, the distances can be calculated based on
canbeexpressedasalinguisticterm,anddenotedas
Equations(1,2and4).
s .Lettheevaluationofpriceforhotelibedenoted
β
(3)CalculatetheCloseIndexC.
asP: i
i
(cid:15) (cid:15)
Pi =1− (cid:15)sα−9 sβ(cid:15), (18) Ci = Di+D+i−Di−. (21)
where s is a linguistic term, α is the average Rank hotels based on Close Index. The greater
α
value of subscripts in the linguistic term set which the value of hotel’s Close Index, the higher the
3740 Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine
recommendedrankingofhotel.Thenrecommendthe Table1
hotelstothetargetuserubasedonthenewranking Alternativehotelsforrecommendation
list. Hotel R1 R2 price Pi rating
Hotel41 1 1 4274 s2 5
3.5. Theprocessofrecommendation MilestoneHotel 2 3 6978 s4 5
MontcalmHotel 3 8 2891 s2 5
HaymarketHotel 4 10 14937 s4 5
In this part, we will introduce the process of FourSeasonsHotel 5 15 7408 s4 5
mechanismforhotelrecommendationtoachieveper- TheDorchester 6 37 5783 s3 5
sonalizedsearchenginebasedontheresearcheswe TheCapitalHotel 7 42 3070 s1 5
haddiscussed.Theproposedapproachiscomprised AmpersandHotel 8 47 2795 s0 4.5
VentureHostel 9 690 241 s−4 3.5
ofthefollowingsteps: EuroQueensHotel 10 922 430 s−4 2.5
Y
Step 1: Obtain the alternative hotels. Using search
engine on e-commerce platform to filter the hotels P Table2
basedonthekeywordsenteredbythetargetuser. Asampleofusers’information
User number vote tu trustu
Step 2: Identify similar group for the target user. O
brooket986 3 1 0.3333 0.0238
According to the user’s historical records and the chickenlips 2 2 1 0.0714
similargroupidentificationmethodproposedin3.2. ZenaB 13 12 0.9231 0.0659
...C ... ...
Foreachpairofusers,calculatethesimilarityusing
RonaeF 1 0 0 0
Equations(6–9),anddivideusersintothreegroups.

Step3:Calculatetheweightofeachgroupaccording
Rsearch engine as alternative hotels and collect basic
toEquation(13).
informationabout10hotels.Foreachhotel,wecol-
lect20users’ratingsandonlinereviews.Weusethese
Step4:CalculatehotelevaluationvaluesunderthreeOdataasthetestset.Besides,wecollectuser’sinfor-
criteria: price, rating, online review based on the
mationandhistoricalcommentrecordsabouthotels
methodsproposedinSection3.3.
foreachuser,whichareusedasthetrainingset.
H
Table 1 displays the 10 hotels’ information. The
Step5:UsetheTOPSISmethodtorankthealterna-
first column is the name of hotel, the second one is
tivesaccordingtoSection3.4.BiggervaluesofClose
hotel’srankingin10hotels,thethirdcolumnishotel’s
Indexareassociatedwithhigherranking. T
rankinginallLondon’hotelsbasedonsearchengine,
Step6:Recommendthehotelsbasedontheranking the fourth one is hotel’s price, and the sixth one is
U
listinstep5tothetargetuser. hotel’soverallrating.Table2showstheuser’infor-
mation,inwhichthefirstcolumnisuser’sname,the
A secondcolumnisthenumberofhotelsthattheuser
4. AcasestudybasedonTripadvisor.com comments, the third column is the number of other
users that consider the user’s comments is helpful,
Acasestudyisconductedinordertovalidatethe thefourthandfifthcolumnsaretheoriginalandstan-
efficiency and applicability of the proposed method dardized trusts of user on Tripadvisor.com. Table 3
one-commercewebsiterecommenderproblems. isasampleaboutthedetailedrecordsofusers’com-
mentsonhotels.Sincethereistoomuchtextinonline
4.1. Dataset reviewsandhotelsdescriptions,itisnotshownhere.
In Table 3, the first column is user’s ID, the second
Inthissection,weusethedatacollectedfromTri- columnisthehoteltheusercommentedon,thethird
padvisor.com, which is a travel-sharing site where columnshowsuser’sratingtothehotelandthefourth
users can comment on hotels they have lived in, andfifthcolumnsshowthepriceandcityofhotel.
attractions they have visited and foods they have
eaten. In this case study, we assume that the tar- 4.2. Result
get user’s demand is to order a hotel in London by
searchingforthekeyword“Londonhotel”.Thenwe Firstly, we identify users’ attributes of interest,
randomlyselect10hotelsintherecommendedlistof trust and consumption capacity, respectively. The
Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine 3741
Table3 Table4
Asampleaboutthedetailrecordsofuserscommentedonhotels Apartofresultsabouttheusersimilarity
UserID hotel rating price Cityofhotel UserID S1 S2 S3 S4
3 OakleyHallHotel 4 1393 Hampshire 2 0 0.0714 0 0.02381
3 TheSavoy 5 3579 London 3 0 0.0659 0 0.021978
3 FlemingsMayfair 5 2100 London 4 0 0.1428 0 0.047619
5 KahalaHotel 5 3876 Honolulu 5 0 0.0396 0 0.013228
5 MilestoneHotel 5 3093 London
... ... ... ... ...
198 DisneylandHotel 5 1585 HongKong
Table5
198 HotelBoss 3 542 Singapore
Theresultsaboutsimilaritygroups
UserID G1 G2 G3
Y
1 2,3,9,13,14,19 5,8,11,12,16,18 4,6,7,10,15,17,20
2 1,3,9,13,14,19 5,8,11,12,16,18 4,6,7,10,15,17,20
3 2,3,5,7,14,20 6,8,9,12,13,19 4,10,11,15,16,17,18
P
O Table6
Therankingofeachhotelforusers
(cid:2)C(cid:2)Use(cid:2)rID(cid:2)(cid:2)(cid:2)Ho(cid:2)te(cid:2)l
1 2 3 4 5 6 7 8 9 10
1 4 7 1 8 6 5 3 2 9 10
2 4 7 1 8 6 5 3 2 9 10
3 4 8 2 7 6 5 1 3 9 10
R
4 4 7 1 8 6 5 3 2 9 10
5 1 5 4 6 8 2 3 7 9 10
28 5 2 7 3 4 1 6 8 9 10
O43 4 7 1.5 6 8 5 3 1.5 9 10
65 4 7 3 8 6 5 1 2 9 10
Fig.4. Thehotel’spricedistribution.
97 3 4 7 2 5 1 6 8 9 10
H 101 1 5 8 6 4 2 3 7 9 10
trustattributehasbeenstandardizedaccordingtothe 121 4 6 1 7 8 5 3 2 9 10
third column in Table 2 and the standardized trust 141 4 8 1 7 6 5 3 2 9 10
161 6 8 1 4 10 7 5 3 2 9
for user is shown in the fourth column in TTable 2.
181 5 10 3 8 9 7 6 4 2 1
Basedontheonlinereviewsandhotelsdescriptions,
thekeywordsusedtorepresenttheusers’interestsare
U
displayedinFig.3,thelargerthesizeofaword,the Then, for a target user, for each item, the users
higherthefrequency. who have evaluated the hotel can be divided into
The consumption capacity of theAuser can be three groups. A part of the results is displayed in
obtainedfromthedatainthefourthcolumninTable3. Table 5. Similarly, the first column is user ID, the
We compare the hotel’s price distribution in several second to the fourth column are the users belongs
cities, as Fig. 4. It is obvious that the price distri- to similar group, weak similar group and dissimilar
butions in different cities are evidently distinct, the group,respectively.
linguistictermscorrespondingtothesamepricemay ThenbasedonthemethodproposedinSection3.4,
bedifferent. eachusercanobtainapersonalizedrecommendedlist
Thenforeachuser,wecalculatethesimilaritywith forhotel.ThedetailsaredisplayinTable6.InTable6,
otherusersbasedonEquations(6–9)proposedinSec- rows 2 to 6 are 10 hotels’ rankings for users 1 to 5
tion3.3.ApartoftheresultsisshowninTable4.The whobookedthe41hotel.Foreachoftheremaining
first column is the users’ ID, the second to fourth ninehotels,weselectoneuserwhohavecommented
columns are these users’ similarities with user 1 in on the hotel, and display the 10 hotels’ ranking for
interests, trust, and consumption capacity. The fifth these users as rows 7 to 15. In search engine, the
column is the overall similarity. Because user 1 has rankingofthese10hotelsis{1,2,3,4,5,6,7,8,9
nohistoricalrecords,thesimilaritiesbetweenhimand 10}forallusers.TheresultinTable6indicatesthat
theothersininterestsandconsumptioncapacityare therankinglistsofthese10hotelsformostusersare
zero. different.
3742 Y.Huangetal./Acomprehensivemechanismforhotelrecommendationtoachievepersonalizedsearchengine
n
o
ecisi
Pr
1 2 3 4 5 6 7
n
model 1 model 2
Y
Fig.5. ThePrecisionwiththechangeofn.
Fig.6. Theprecisionaftereliminatingtheuserswhoorderedthe
4.3. Discussion top-(cid:2)hotels.P
From Tables 4 to 6, for all users, the proposed
O
method has the ability to calculate their similarities The hotel’s ranking in search engine will remain
withotherusers,toidentifytheirsimilargroupsand thesameforalongperiod.Fortheuserwecollected,
to provide them a personalized recommended list. theCaccuracy of model 1 for users who booked top
The feasibility of proposed hotel recommendation hotels is always higher than that for the user who
methodhasbeenillustrated.What’smore,theresults booked bottom hotels. For instance, for users who
inTable6illustratethattheproposedmechanismused bookedhotel1,thePrecisionofmodel2isequalto1,
for search engine can provide personalized list for RwhilethePrecisionofmodel2foruserswhobooked
users. hotel 10 is equal to 0. The Precision is extremely
In the next step, we will discuss the accuracy of unstableforuserswithdifferentpreferencesonbook-
O
theproposedmethod.Inthisstudy,weusetheindex ing hotels. Therefore, we analyze the Precision for
of Precision to measure the accuracy. Precision is different users with different preferences. Figure 6
the ratio of the items the user liked to all the rHec- showsthePrecisionaftereliminatingtheuserswho
ommended items in the recommended items [48]. orderedthetop-(cid:2)hotels(where(cid:2)isathreshold).For
Because in the test set, only one hotel per user has example,when(cid:2)=1,itmeansthatwejustcalculate
beenordered,thereisafine-turningforthedeTfinition thePrecisionsforuserswhobookedhotels2tohotel
of Precision. In this study, we consider the recom- 10. The results of Model 1(a) and model 2(a) show
mendation is successful when the rankinUg of hotel thePrecisionsformodel1andmodel2respectively
userorderedistop-n(wherenisthethreshold)among when the value ofn is 5. The results of Model 1(b)
allhotels.Therefore,thePrecisionisthepercentage andmodel2(b)showthePrecisionsformodel1and
ofprovidingsuccessfulrecommendatioAntoallusers. model 2 respectively when the value of n is 3. The
ThecalculationofPrecisionisshownas: resultsofmodel1(c)andmodel2(c)showthePreci-
sionsformodel1andmodel2respectivelywhenthe
N(s)
Precision= , (22) valueofnis1.ConsistentwiththeresultsinFig.5,
N
the Precisions of the proposed method are always
whereN(s)isthenumberofprovidingsuccessfulrec- greaterthanthoseofsearchengineusedinTripadvi-
ommendation,andNisthetotalnumberofproviding sor.comwhateverthevalueofnor(cid:2)is.InFig.6,we
recommendation. canobservethattheprecisionsarezerofortheusers
WecalculatethePrecisionoftheproposedmethod whopreferthebottom-5hotelwhateverthevalueofn
(model1)andsearchenginecurrentlyusedinTripad- is.However,theproposedmethodhasstableperfor-
visor.com(model2).Figure5exhibitsthePrecisions mance.Theresultfurtherprovesthedeficiencyofthe
withthechangeofnfortwomethods. hotelrecommendedlistgeneratedbysearchengine,
From Fig. 5, it is obvious that the Precisions of andindicatesthattheproposedmethodcanprovide
theproposedmethodarealwaysgreaterthanthatof usersaccurateandpersonalizedhotelrecommended
model2.Thatistosay,theaccuracyoftheproposed list.
methodisimprovedcomparingtothesearchengine To go a step further, we discuss the accuracy for
currentlyusedinTripadvisor.com. cold start users. The Precisions are presented as (in