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A Multi Agent Architecture for Tourism Recommendation

A Multi Agent Architecture for Tourism Recommendation. Inma García Universidad Politécnica de Valencia. The e- Tourism Multi-Agent System . Web- based rec ommender system that computes a user-adapted tourist plan for a single user .

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A Multi Agent Architecture for Tourism Recommendation

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  1. A MultiAgentArchitectureforTourismRecommendation Inma García Universidad Politécnica de Valencia

  2. Thee-TourismMulti-Agent System • Web-basedrecommender system that computes a user-adapted tourist plan for a single user. • Recommends a list of activities to perform in a city (Valencia, Spain). • Agenda of activities: time schedule for the list of activities taking into account: • Distances between places • Opening hours, etc.

  3. Thee-TourismMulti-Agent System • e-Tourism integrates agents that cooperate to: • Dynamically capturethe user profile. • Obtain a list of activities for the user. • Computes the planned agenda.

  4. Thee-TourismMulti-Agent System • The e-Tourism requires a flexible architecture : • To implement multiple users: • New users should be able to enter the system at any time. • Existing users should be able to leave the system. • Tourism activities and information need to be updated. • Recommendations and planning techniques: • Different planning and recommendation techniques. • New ones should be easily integrated. • Cooperation scenarios should be created on demand depending on the tourism preferences of the user and the recommendation provided.

  5. Thee-TourismMulti-Agent System • The MAS architecture provides: • Flexibility • Openness • Adaptability • Scalability • to a tourism recommender and planning system. • We focus on the system components and its functional behaviour.

  6. RecommenderSystems • Information filtering technique that attempts to presentinformationitemsthat are likely of interest to the user. • Widely used in the internet for suggesting products, activities, … • Give a recommendation for a user considering his/her interestsand tastes. • Infers the recommended items by analyzing the available user data and information about the environment. • Howmucha particular user likes an item is represented by a rating. • Recommends to the user the items with the highestestimated ratings.

  7. e-Tourism MAS Architecture: AgentRoles

  8. e-Tourism MAS Architecture: Use Cases The four roles are in charge of six use cases: • Register User: When a user first enters the system, the first step is to register and enter his personal details and general preferences. • Request Visit: Each time the user enters the system for a new visit he will be requested to introduce his specific preferences for the current visit (date, time schedule, …).

  9. e-Tourism MAS Architecture: Use Cases • Recommend Activities: When a user requests a visit, the GRSK is in charge of generating a list of activities that are likely of interest to the user. • Plan Tourist Agenda: From the list of recommended activities, the user selects those he is really interested in and discards those he does not want to be included in the final plan. The planning system schedules the activities according to the time restrictions of the user and the environment.

  10. e-Tourism MAS Architecture: Use Cases • Update User Profile: When the user logs again in the system, he is asked to rate the activities in the last recommended plan. These ratings are used to improve the user profile. • Update Tourism Info: The Finder role is in charge of keeping updated tourism information and activities in the system.

  11. e-Tourism MAS Architecture: Ontology Features Itemstorecommend • Preference: (feature, d_interest) feature in the ontology and the value that represent the degree of interest of the user in the feature. • Items are associated a value ACi (acceptance counter): • Represent how popular the item is among users. • Indicates how many times the item has been accepted when recommended Degree of interest of theitemunderthefeature

  12. UserAgent • The User role is played and implemented by one or more User Agents. • This agent represents a userof e-Tourism. • In charge of: • Store and handle the user profile. • Obtain the general preference model. • Obtain the visit data. • Obtain the list of recommended items. • Obtain the visit agenda. • Obtain the items rating (feedback).

  13. UserAgent: 1. UserProfile

  14. UserAgent: 1. UserProfile • Tasksinvolving the user profile: • Initialized: SetProfile • Modified: ChangeProfile • Consulted: GetProfile • Use cases: • Register User • Update User Profile

  15. UserAgent: 2. General PreferencesModel • Description of the types of items (preferences) the user is interested in. • Use case: Register User. • Tasks: • Setpreferences. • Changepreferences. • Informpreferences. • Getpreferences.

  16. UserAgent: 3. VisitData • Each time the user enters the system for a new visit will be requested to introduce: • Specific preferences for the current visit (Visit Preferences), which may differ from his general preferences. • For example, unlike other user trips, he might not be traveling with children in the current visit. • User current location, which is stored in the Current Status. • Maximum number N of recommendationshe desires. • Task: Create Visit • Use case: Request Visit

  17. UserAgent: 4. Listof RecommendedItems • The GRSK provides the User Agent the list of recommended items. • The list is stored as the Current Recommendation. • Use case: Recommend Activities

  18. UserAgent: 5.Agenda • The user markseach activityin the list of recommendations as: • Accepted • Discarded • Indifferent • Task: Select Recommended Item.

  19. UserAgent: 5.Agenda • The Planner Agent construct the Current Plan • Using the list of selected and indifferent items • Use case: Plan Tourism Agenda. • Current Plan: a list of activities joint with an specific start time and a duration (agenda).

  20. UserAgent: 6. ItemsRating (Feedback) • When the user logs again in the system: • Specify which activities he has performed and the degree of satisfaction with the recommendation. • Ratesthe items recommended in the previous interaction. • Information crucial to improve future recommendations. • Stored as Previous Visits. • Use case: Update User Profile.

  21. GRSKAgent • Generate the list of recommended items • Distributed architecture: • Every recommendation technique is encapsulated into an agent. • New techniques can be easily added: • By means of a new agents compliant with the interaction protocol. • Acts as response to a User Agentrequest.

  22. GRSKAgent • Generalist: • Independent of the current catalog of items to recommend. • As long as the data of the domain are defined through a taxonomyrepresentation. • The ontology represent the user‘s likes and the items to recommend. • Items are semanticallydescribed through an ontology. • The recommendations are based on the semantic matching between the user preferences and the item descriptions.

  23. GRSKAgent: RecommendationTechniques

  24. GRSKAgent: RecommendationTechniques

  25. GRSKAgent: RecommendationProcess • The RS agents derive a set of positive and negative constraints: • Positive constraints CP: preferences that the recommended items must meet. • Negative constraints CN: preferences that the recommended items must not fulfill. • The itemsthat match these constraints are recommended to the User Agent.

  26. GRSKAgent: RecommendationProcess

  27. GRSKAgent: RecommendationProcess

  28. GRSKAgent: RecommendationProcess

  29. GRSKAgent: RecommendationProcess

  30. GRSKAgent: RecommendationProcess

  31. PlannerAgent Computes a planfrom the list of activities recommended by the GRSK Agent and then filtered by the user.

  32. PlannerAgent Manages three groups of data: • User Planning Preferences: the visit date, the user available time, the current geographical location of the user, ... • Activity Data: information about each activity: opening hours of each activity, address of the place where the activity takes place, duration of activity, … • City Data:information about the city map.

  33. PlannerAgent • Select which activitiesinclude in the plan: • The scheduling will depend on the user available time, his temporal constraints and the time restrictions of the environment (i.e. opening hours of places). • Partial Satisfaction Planning (PSP) problem. • In PSP problems the solution plan is not required to achieve all the goals but instead achieve the best subset of goals given the resource limitations. • Goal: is to obtain a plan • With the most satisfactory activities (as possible). • Trying to minimize the time spent on going from one place to another.

  34. Conclusions • e-Tourism: multi-agent system that generates personalized recommendations about tourist tours in the city of Valencia (Spain). • Computes an agendaof recommended activities: • Reflect the user's tastes • Takes into account the geographical distance between places and the opening hours of these places.

  35. Conclusions The main component is the GRSK Agent a GeneralistRecommenderSystemKernel: • RS based on the semantic description of the domain • Allows the system to work with any domain defined through an ontologyrepresentation. • Basic RecommendationTechniques • Demographic • Content-based • General preferences filtering • Current preferences filtering • Recommendations based on the user's tastes, his demographic classification, the places visited by the user in former trips and, finally, his current visit preferences.

  36. FurtherWork • Extension of e-Tourism to group recommendation: • Calculating the list of activities according to the global or particular constraints rather than in terms of the group preferences. • Innovative techniques to compute the group profile (Incremental Intersection Technique or Incremental Collaborative Intersection). • Apply agreement technologies for group recommendation, in order to increase the reliability of electronic communities by introducing human social control mechanisms.

  37. Thankyou Inma García Universidad Politécnica de Valencia (Spain) ingarcia@dsic.upv.es

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