Recommender systems for dynamic packaging of tourism … · 2015. 4. 21. · Michael Schumacher and...

Michael Schumacher and Jean‐Pierre [email protected]

Institute of Business Information SystemsUniversity of Applied Sciences Western Switzerland

(HES‐SO), CH‐3960 Sierre

Recommender systems for dynamic packaging of

tourism services

Bio Express

• Jean‐Pierre Rey– http://iig.hevs.ch/switzerland/jean‐pierre.html– Software Engineering & Business processes– eTourism– Sustainable Development

• Michael Schumacher– http://iig.hevs.ch/switzerland/michael‐schumacher.html– Intelligent agents– E‐Health

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Presentation’s goal

• Present some reflexions about recommender systems on a particular and well‐defined context

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Agenda

• Context• Recommander systems in this context

– For individual services• Collaborative filtering• Ontological filterint

– For packages of services• Association rules• Content based

• Conclusion4

Recommender system: why ?

• Two major point of views– Guide the consumer (improve user experience)– Sell more and better (improve business)

• Propose to the consumer the best products for him

• Such a system help to match users with items

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Global context of this analysis

• An applied research project with Valais Tourism for helping them to design a global marketplace based on the use of (new) IT

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24.08.2012 7

The (existing) situation

• 16 organisations/destinations are using each its own reservation system– With various results– Sometimes , resorts have no tools– A lot of different systems and often no compatibility between systems

• It is very difficult to combine various services (dynamic packaging)

24.08.2012 8

Observations (facts)• Marketing and distribution weaknesses• Too much invidualism (operational and development)

• Lack of common platform at the cantonal level (cross sale Valais)

• Difficulty or unwillingness of providers to provide products / quotas (contingents)

Conclusion : the eCommerce Valais solution want to solve the problem of dispersed forces and marketing weakness!

• One point of sale with a maximum of services

• Optimized distribution• Cross selling innovation

– A single selling network– Every service provider is becoming a seller interested by the addition of other prestations

– Go beyond individuals and fragmentedstructures

– One click = one selling opportunity

eCommerce Valais: Vision

• Connect• Integrate

• Systems• Various services

• Local solution• Part of a broader vision(Valais 2.0)

eCommerce Valais: goals

24.08.2012 11

e‐commerce Valais

Web shop

Integrator (integration layer of multiple various products)

Accommoda‐tion Activities Services

New

Existing • Hotels

• Appartments• Tourism Offices using Tomas, Deskline

• Ski (cable cars, schools, and so on)• Guides and escorts• Leisure, recreations• Baths and wellness •Culture• ...

• Sports shops• Transports• Shop online• ….

e‐check in +

CRM

Distribution (channel

management)

booking.com HRS expedia etc.

Centralizedmarketing

Case study (eComTour)

WEBSHOP VALAIS TOURISMEWEBSHOP VALAIS TOURISMECITI

Deskline

Interhom

e

Skidata

Tomas

… …

Hotels, real estate agencies, resorts, sport shops, …

Inventory of tourism products in Valais

Dynamic packaging

• Business goal:– Electronic system that guides the consumer (or the travel agent) through the design, the booking and the payment of their holiday or trip, according to their needs or desires.

• Real time touristic service composition:– Dynamically assemble the different components of their choices

– and then complete the transaction in real time.

Service platforms integration

• Dynamic packaging requires:– Architectural issues

• Unique electronic window that combines offers in Valais.

• Integrator layer: Web Service integration of individual service

– Financial challenge: money flows– Ability to put together people and ideas

• More than a technical problem

Agenda

• Context• Recommandation system in this context

– For individual services• Collaborative filtering• Ontological filterint

– For packages of services• Association rules• Content based

• Conclusion16

Personalized package recommendation

• Opportunity:– As soon as an integrated platform for dynamic packaging exists, it can be enhanced for each user with …

– This paper is an analysis of which recommender systems can be used for dynamic package recommendations

Personalized package recommendations

What is a recommender system?

• u is a utility function that measures if an item sis useful for a user c:

• Goal: choose for every user c of C the best item s’ of S that maximizes the utility for the user:

Users (possibly described

with a profile)

Items/products that can be recommended, i.e. hotel bookings, ski rentals (possibly described with features)

ordered set (e.g. real values)

Two identified characteristics of dynamic packaging platforms

1. Users are NOT regular visitors of the Web site:– their profile is not known in advance; – they have no purchase history;– they have probably never rated any other items.

2. In a package, recommendations can be made– Either for each individual service of the package: step by step, recommendations are made for every single service.

• E.g. I recommend step by step an accommodation, then an event, then a wellness service.

– Or for a whole package• E.g. I recommend “Package 534: 3 days in Zermatt hotel with 1 Fondue night and one wellness park entry for 430 EUR”

Recommendation of individual service versus service package

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Activity Hotel Wellness Transport

Recommend alpine ski service

Recommend alpine ski service

Recommend 3* Family Hotel

Recommend 3* Family Hotel

Recommend family friendly

swimming pool

Recommend family friendly

swimming pool

Recommend specific bus company

Recommend specific bus company

Recommend package {alpine ski, 3* family, family‐friendly swimming pool}

Recommend package {alpine ski, 3* family, family‐friendly swimming pool}

Rec. of individual services

Rec. of service packages

Recommendation of individual services: solution 1

• Item‐based Collaborative Filtering

« Tell me what’s popular among my peers »« Use the wisdom of the crowd »

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Collaborative filtering (CF)

• Most popular recommendation method• 2 hypotheses:

– users rate items/products; – users have similar behaviour that does not change significantly.

• 2 main families of CF methods:– Memory‐based CF: directly uses the item rating matrix to make recommendations, i.e. runtime analysis.

– Model‐based CF: offline‐based method that learns a model using rating matrices. During runtime, this model is then used to make recommendations.

Item‐based CF (IbCF)

• One of the most efficient memory‐based CF• Uses similarity between items (and not users) to make predictions.

• To define the utility of an item i for a user u, IbCF searches for all similar items and uses the ratings by u for this subset of items to predict the utility of i.

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• Use item‐based CF– This methods needs

• Ratings of individual items (touristic services)• Thus, the user must be asked to rate certain offers beforehand

DISCUSSION:• Almost impossible to ask a very occasional user to rate other offers

• Furthermore, problem of cold start: new introduced items (services) are not yet rated by users.

Recommendation of service packages: solution 3

• Association Rules

« What goes with what ? »

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Association rules

• Popular model‐based method, based on transactions in a shop

• Defines “what goes with what”. Example:– Transaction {golf, 4*hotel, wellness} could produce rules such as:

• “If client purchases golf, then also a 4* hotel and a wellness service”, • Or: “If client purchases a wellness service, then also golf and 4*hotel”

– Goal is to find strong rules in two steps:• Generate all possible associations (with apriori algorithm)

• Choose only the rules with strong confidence26


• Use Association rules:– This methods needs

• Large history of composed packages purchase• No ratings of individual services or packages are needed

DISCUSSION:• Easy to implement• Rules can be calculated offline (e.g. every night)• No start problem: a dynamic packages platform can be run for a while to collect transactions, before the association rules are created to produce recommendations.


• Preference‐Based Recommendation System

« Show me more of the same what I’ve liked »

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Content‐based recommender systems (CBRS)

• Memory‐based & model‐based methods use ratings of items or transaction information.

• However, content‐based RS use:– Information about items– and information on user profiles (preferences)

• User preferences have to be learned so that items can be recommended that are similar to the user’s preferences.

• Calculates the utility u(c,s) of an item s for a user cusing the utilities u(c,si) that this same user c has attributed to the items si of S that are similar to s. 29

CBRS: Preference‐based RS• Creation of recommendations is considered a constraint satisfaction problem (X, D, C, I) :– X : attributes {x1, ,xp} that describe all items;

e.g. X={type, numberOfRooms, surface, ratePerWeek};

– D : authorised domain values {D1, ,Dp), where every Direpresents the set of possible values for xi;

e.g. DType = {chalet, apartment}, DNumberOfRooms = [1,8], DSurface = [10,300]m2, DRatePerWeek = [0,10’000]CHF;

– C : constraints {c1,… ,cp}, where every ci is a constraint function that describes the values that a subset of X can have;

e.g. CType,Size: if type = chalet then surface > 70m2;

– I : set of items that will be recommended, cartesian product D = D1 x D2 x … x Dp.

e.g. {chalet, 7, 220m2, 2’500}. 30


• Use Preference‐based RS– This method needs:

• User preferences must first be defined (expressed as strong and weak constraints)

• Based on this declarative description, a CSP solver will find a set of values for the attributes (variables) that fulfil the preferences (constraints).

DISCUSSION:• CSP solvers are well‐studied and very efficient.• Takes a complete view of the preferences• Big disadvantage: need to obtain the user preferences before making the recommendations 31

Knowledge‐based recommender systems.

• Use technologies based on the representation of knowledge of items and users

• For our problem, two techniques are useful:– Conversational RS:

• Bases on case‐based reasoning

– Ontological filtering:• Bases on ontology technologies

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• Use Conversational RS– The aim is to resemble a conversation with a salesperson, in two steps:

• asks the user about his/her preferences • new preferences are then implicitly constructed through critiques of the recommendations (e.g. this recommended hotel room is too expensive for me).

DISCUSSION:• Advantage: does not require much user feedback, i.e. it can immediately be used (no cold start issues)

• Disadvantage: User must be ready to give at least some basic feedback and to interact in a conversation 33



• Use ontological filtering:– This method needs:

• An ontology to describe item catalogue and possible preferences

DISCUSSION:• Advantage:

– can construct automatically ontologies for describing item catalogues

– and can infer preferences from votes

Conclusion (1/3)

• Implementing RS into a dynamic touristic service platform–Work in three steps:

• Develop the packaging platform: integrate web services and record transactions

• Analyse thoroughly the data

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Conclusion (2/3)

• Realize a feasibility study with different RS methods, and take into account:

– precision and utility of the recommendation, – cost for implementation –maintenance of the system– ...

• From a very practical point of view:– Users are NOT regular visitors – Users are NOT ready to spend a lot of time setting personal preferences

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Conclusion (3/3)

• Our analysis: most useful RS for dynamic packaging:– Association rules (easy to implement and no user interaction)

– Conversational RS (do not require much feedback, but long conversation may be not welcomed)

– Preferenced‐based RS (combined with conversational RS, may offer optimal recommendations, with disadvantages to acquire preferences).

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Questions ?

Advices ?Similar experiences ?

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