recommender systems for dynamic packaging of tourism … · 2015. 4. 21. · michael schumacher and...
TRANSCRIPT
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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
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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
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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)
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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!
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• 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
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• Connect• Integrate
• Systems• Various services
• Local solution• Part of a broader vision(Valais 2.0)
eCommerce Valais: goals
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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
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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
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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.
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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
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Agenda
• Context• Recommandation system in this context
– For individual services• Collaborative filtering• Ontological filterint
– For packages of services• Association rules• Content based
• Conclusion16
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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
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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)
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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”
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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
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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.
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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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Recommendation of individual services: solution 1
• 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.
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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
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Recommendation of service packages: solution 3
• 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.
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Recommendation of service packages: solution 5
• 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
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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
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Recommendation of service packages: solution 5
• 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
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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
Recommendation of service packages: solution 4
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Recommendation of individual services: solution 2
• 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
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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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