Travel Recommender Systems Francesco Ricci, eCommerce and TourismResearch Laboratory

Recommender systems are commonlydefined as applications that e-commercesites exploit to suggest products and provideconsumers with information to facilitatetheir decision-making processes.1 Theyimplicitly assume that we can map userneeds and constraints, through appropriaterecommendation algorithms, and convertthem into product selections using knowl-edge compiled into the intelligent recom-mender. Knowledge is extracted fromeither domain experts (content- or knowl-edge-based approaches) or extensive logsof previous purchases (collaborative-basedapproaches). Furthermore, the interactionprocess, which turns needs into products, ispresented to the user with a rationale that

depends on the underlying recommenda-tion technology and algorithms. For exam-ple, if the system funnels the behavior ofother users in the recommendation, itexplicitly shows reviews of the selectedproducts or quotes from a similar user.

Recommender systems are now a popularresearch area2 and are increasingly used bye-commerce sites.1 For travel and tourism,3

the two most successful recommender system technologies (see Figure 1) areTriplehop’s TripMatcher (used by www.ski-europe.com, among others) and Vaca-tionCoach’s expert advice platform, Me-Print (used by travelocity.com).

Both of these recommender systems try tomimic the interactivity observed in traditionalcounselling sessions with travel agents whenusers search for advice on a possible holidaydestination. From a technical viewpoint, theyprimarily use a content-based approach, in

which the user expresses needs, benefits,and constraints using the offered language(attributes). The system then matches theuser preferences with items in a catalog ofdestinations (described with the same lang-uage). VacationCoach exploits user profilingby explicitly asking the user to classify him-self or herself in one profile (for example, asa “culture creature,” “beach bum,” or “trailtrekker”), which induces implicit needs thatthe user doesn’t provide. The user can eveninput precise profile information by complet-ing the appropriate form.

TripleHop’s matching engine uses amore sophisticated approach to reduceuser input. It guesses importance of attri-butes that the user does not explicitly men-tion. It then combines statistics on past userqueries with a prediction computed as aweighted average of importance assignedby similar users.4

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2. H. Werthner et al., “Information SocietyTechnologies for Tourism,” Report of the Strategic Advisory Group on the 5thFramework Program on Information Soci-ety Applications for Transport and Associ-ated Services, 1997.

3. H. Werthner, “Just Business—Shouldn’tWe Have Some Fun?” Proc. 3rd Int’l Conf.Electronic Commerce & Web Technologies

(Proc. ECWEB-DEXA), Springer-Verlag,New York, 2001.

Hannes Werthner is head of the eCommerceand Tourism Research Lab at the IRST ResearchCenter, a professor at the University of Trento,and founder of the eCommerce CompetenceCenter in Vienna. His research activities coverdecision support systems, simulation, artificialintelligence, and Internet-based information

systems, especially in the field of tourism. Heearned an MS and PhD in computer sciencefrom the Technical University Vienna. He is amember of the strategic advisory board forthe European research program IST, acts as theeditor in chief of the journal InformationTechnology and Tourism, and is HonoraryPresident of the International Federation forIT and Travel/Tourism (IFITT). Contact him atwerthner@itc.it.

Figure 1. (a) Ski-Europe and (b) Travelocity destination recommendation tools.

(a) (b)

Caveats and limitationsNeither system supports the user in build-

ing a “user defined” trip, consisting of oneor more locations to visit, accommodations,and plans to visit additional attractions (amuseum, the theater, and so forth). Althoughtravel planning is a complex decision process,these systems support only the first stage—deciding the destination.

Researchers have proposed severalchoice models,5 which identify two groupsof factors that influence destination choice:personal features and travel features. Thefirst group contains both socioeconomicfactors (such as age, education, and income)and psychological and cognitive ones(experience, personality, involvement, andso forth). The second group might listtravel purpose, travel-party size, length oftravel, distance, and transportation mode.These various factors affect all stages ofthe traveller’s decision-making process,which is a complex constructive activity.

Another reason why these systems focuson destination selection relates to the filter-ing (content-based) approach. Even if wecould apply the same filtering technology toother tourism objects, such as cruises, thesystem would have to describe a catalog ofcruises—that is, build a catalog using aselected set of features (decision variables).The approach does not scale unless we pur-sue a costly knowledge-engineering activityfor each product type. So, these systemsmust have a particular catalog—in this case,a catalog of destinations—which requiresextensive domain knowledge and must bebuilt for the particular application. Currently,the focus is on destinations because they arerather stable, reusable concepts (many rec-ommender systems can exploit the same des-tinations knowledge base).

Pure collaborative filtering approachesdo not suffer from this problem, but, unfor-tunately, we cannot readily implement themin the travel domain. The major issue is thecomplexity of travel objects; we can’t sim-plify a trip to the point where two travellers’trips are the same. Surely two people havebought the same book, but it is less likelythat two people have experienced the sametrip. This points to a basic requirement ofCF approaches: one user’s purchase historymust be comparable to that of another. Thus,one user’s travel list must somehow overlapthat of another user. One approach could beto simplify the travel description to a certainpoint—for instance, representing just the

destination—but then we will discover thatthe already visited destinations are insuffi-cient to predict the next one. Additional con-text information must be included, so wemust query the user about the content of hisor her trip. Hybrid approaches that combinecontent- and collaborative-based approacheswill more likely succeed.6

Broadening the scopeGoing back to the basic recommendation

process (moving from needs to productswith explanations), this apparently linearprocess is far from being straightforward inthe real world.

Catching user needs and decision stylesRecommender systems struggle to catch

user needs, and companies have imple-mented different approaches to tackle thisissue. Amazon.com, for instance, immedi-ately recognizes the user’s identity and rec-ommends a book, without asking for anyuser input. In contrast (similar to the twotravel recommender systems mentionedearlier), www.activebuyersguide.com in-volves a user searching for a vacation in amultistage interaction. First, the site asksabout the vacation’s general characteristics(type of vacation, activities, accommoda-tion, and so forth). Second, it asks for detailsrelated to these characteristics, then fortradeoffs between characteristics. Finally,it recommends destinations. Both approacheshave drawbacks, but an adaptive approach,where questions are fine-tuned as thehuman–machine interaction unfolds, hasmore potential.

Researchers have recently argued thatrecommender systems should support mul-tiple decision styles.5 The DieToRecs rec-

ommender (a case-based travel planningsystem) supports these decision styles byletting the user enter the system throughthree main doors: iterative single-itemselection, complete travel selection, andinspiration-driven selection.

Iterative single-item selection lets themost experienced user efficiently navigate inthe potentially overwhelming informationspace. The user can select whatever productshe or she likes and in the preferred order,using the selections done up to a certainpoint (and in the past) to personalize the nextstage. For example, if the user selects a par-ticular destination, that destination is used torecommend a particular accommodation.

Complete travel selection lets the userselect a personalized travel plan that bun-dles items available in the catalog. The per-sonalized plan is constructed “reusing” thestructure of travels built by other users insimilar sessions.

Inspiration-driven selection lets the userchoose a complete trip by means of a sim-pler user interface (icon based) and an inter-action that is as short as possible. The tech-nology behind this approach is provided byintegrating case-based reasoning with inter-active query refinement. Interactive queryrefinement allows a more flexible dialoguemanagement—the system tackles failuresdue to over- or underspecified user needs,suggesting precise repair actions (constraintrelaxation or tightening, respectively). Case-based reasoning provides the framework tocast a recommendation session into a case-and similarity-based ordering of both com-plete trips and single products.6,7

Generating recommendationsThe mechanistic idea that from needs

(problems), the recommender’s intelligentalgorithm can deduce the right products(solution) is far too simple. Marketers statethat needs can be created such that productscan be sold. This motivates the suggestionpath in Figure 2. Products shown on a Website can help create needs by offering exam-ples to users who might not have enoughexperience to formulate the query as the rec-ommender system might require (see, forexample, www.activebuyersguide.com). Inother words, an effective travel recommendersystem should not only notice the user’s mainneeds or constraints in a top-down way butalso allow the exploration of the option spaceand support the active construction of userpreferences (in a bottom-up way).

56 computer.org/intelligent IEEE INTELLIGENT SYSTEMS

Recommendation path

Suggestion path

Product

Needs Rationale

Figure 2. Recommendation and suggestion paths.

Recent research has emphasized thischange of perspective, defining it as naviga-tion by proposing.8 In this approach, the sys-tem shows the user examples of products,selected from those that the initial queryretrieved. The user can choose a product asthe current best choice, which updates theinitial query and lets the recommender iden-tify a new set of suggestions. The relevancefeedback technique used in informationretrieval (for example, Rocchio’s method)has influenced this approach, which basi-cally injects new constraints or terms—extracted from the selected item or a corre-sponding cluster—into the original query. Inaddition, the approach is conversational inthat it supports either a multistage interactionor a dialog that interleaves needs elicitationwith products.9 In multistage interaction,example recommendations elicit user needsby exploiting a dialog control component,which poses only focused questions, deter-mined by the previous interaction steps.

Speaking the right languageAs I mentioned earlier, recommender sys-

tems must carefully manage the human–machine dialogue such that even a naive usercan effectively use the system. Rephrasing auser-centered design slogan: “Recommendersystems are about people, not machines.”Thus, usability issues, such as choosing theproduct description language, come to thefore. For instance, asking if the user needs a“hot shoe” or a “manual white balance” in adigital camera could be a “hard to say” ques-tion for a naive photographer.

A recommender system’s ultimate effective-ness relies on its algorithms and their ability toextract useful and novel products from the cata-log.10 However, even if the recommendationsare useful, users will struggle if the help systemis poor, the item descriptions are too terse, or thesite navigation support is confusing. Systemusability is such an important issue that even arecommendation that is not useful but correct(for example, a place already visited) canincrease a user’s trust in the system—a neces-sary condition for recommendation acceptance.

Recommender systems could becomelearning environments or simpler informa-tion presentation tools, but we must designthem to support surplus learning and userbehavioral changes; again, usability comesfirst. Furthermore, the interaction and inter-face design can deeply affect the user’sdecision-making process. Different designchoices can induce distinct decision strate-

gies and influence the user’s affective state(emotions, level of involvement, quality ofthe flow experience) in peculiar ways.

Recommender systems emerged initiallyas filtering tools, where the primary concernwas to discard, in a large database of prod-ucts, items inappropriate to user needs.Now, experiences with real recommendersystems and research prototypes show thatthe user tasks and functions supported bysuch systems are much more varied. Wethus should focus on new support functionsfor expanding the user’s horizon.

AcknowledgmentsI thank Fabio Del Misser, Elena Not, and

Hannes Werthner for comments on an earlierversion of this essay. Thanks to Daniel R. Fesen-maier and Josef Mazanec for their inspiring cri-tiques and encouragement. This work is partiallyfunded by the CARITRO foundation (underContract “eCommerce and Tourism”) and theEuropean Union’s Fifth Research and Technol-ogy Development Framework Programme(under Contract DIETORECS IST-2000-29474).

References

1. J.B. Schafer, J.A. Konstan, and J. Riedl, “E-Commerce Recommendation Applications,”Data Mining and Knowledge Discovery, vol.5, nos. 1–2, Jan.–Apr. 2001, pp. 115–153.

2. F. Ricci and B. Smyth, “Recommendation andPersonalization in eCommerce,” Proc. Adap-tive Hypermedia 2002 Workshop (AH 2002),Univ. of Malaga, Malaga, 2002.

3. H. Werthner and S. Klein, Information Tech-nology and Tourism—A Challenging Rela-tionship, Springer-Verlag, New York, 1999.

4. J. Delgado and R. Davidson, “KnowledgeBases and User Profiling in Travel and Hos-pitality Recommender Systems,” Proc. 9thInt’l Conf. Information and Comm. Tech-nologies in Tourism (ENTER 2002), K. Woe-ber, A. Frew, and M. Hitz, eds., Springer-Ver-lag, Heidelberg, Germany, 2002, pp. 1–16.

5. D. Fesenmaier et al., Tourist Decision Model,tech. report D2.2 DieToRecs IST-2000-29474, EU IST project, 2002; http://dietorecs.itc.it/PubDeliverables/D2.2-V1.0.pdf.

6. F. Ricci and H. Werthner, “Case-BasedQuerying for Travel Planning Recommenda-tion,” Information Technology and Tourism,vol. 4, nos. 3–4, 2002, pp. 215–226.

7. D.R. Fesenmaier et al., “DieToRecs: TravelAdvisory for Multiple Decision Styles,” to bepublished in Information and Communica-tion Technologies in Tourism 2003, A. Frew,ed., Springer-Verlag, New York, 2003.

8. H. Shimazu, “ExpertClerk: Navigating Shop-pers Buying Process with the Combination ofAsking and Proposing,” Proc. 17th Int’l JointConf. Artificial Intelligence (IJCAI 2001),Morgan Kaufmann, San Francisco, 2001, pp.1443–1448.

9. M. H. Göker and C. A. Thomson, “Personal-ized Conversational Case-Based Recommen-dation,” Proc. Advances in Case-Based Rea-soning: 5th European Workshop (EWCBR2000), Springer-Verlag, New York, 2000, pp.99–111.

10. K. Swearingen and R. Sinha, “Beyond Algo-rithms: An HCI Perspective on RecommenderSystems,” Proc. Recommender Systems: Papersfrom the 2001 ACM SIGIR Workshop, 2001,http://cs.oregonstate.edu/~herlock/rsw2001.

Adaptive context-awaremobility support for touristsAlexander Zipf, European Media Laboratory

As mobile devices decrease in size,weight, and price and increase in power,storage, connectivity, and positioning capa-bilities, tourists will increasingly use themas electronic personal tour guides. However,to make such mobile tourist services a suc-cess, a range of factors must work together,from technical issues (such as bandwidth,positioning availability, and supportedinteraction paradigms) to user interface andsecurity issues. We must also considerissues such as the availability of accurate,timely, and localized data, end-user costs(business models), and trust.

Location awareness for mobileusers

Resolving these issues becomes moreurgent as time-to-market gains importance.However, the danger exists of investing a lotof money into solutions that tourists willnot accept. For example, many companieshave already started developing mobile cityinformation and navigation systems tar-geted at tourists (in particular, during theUniversal Mobile Telecommunications Sys-tem (UMTS) hype in Europe). These com-panies often claim to provide personalized,location-aware solutions, but using buzz

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