Building an expert travel agent as a software agent Silvia Schiaffino *, Analia Amandi 소프트컴퓨팅연구실황주원

Overview Introduction Recommendation approaches Traveller’s overview Traveller’s combined recommendation technique - Content-based recommendations - Collaborative filtering - Demographic profile Experimental results Conclusions and future work 1

Introduction Traveller –An expert agent in the tourism and travel domain –Goal Suggestion package holidays and tours –Method Hybrid approach : combination of a variety of approaches A variety of approaches –Content-based approaches –Collaborative filtering approaches –Demographic approaches –Hybrid approaches 2

Recommendation approaches 3 1. Content-based approaches 2. Collaborative filtering approaches –Memory-based collaborative filtering –Model-based collaborative filtering 3. Demographic approaches 4. Knowledge-based approaches

Recommendation approaches 4 Content-based approaches Definition –This approach is based on the intuition that each user exhibits a particular behavior under a given set of circumstances. –This behavior is repeated under similar situations. User profile –A user profile contains those features that characterize a user interests, enabling agents to categorize items for recommendation based on the features they exhibit. Disadvantage –The behavior of users is predicted from their past behavior –Over-specialization –A poor quality

Recommendation approaches 5 Definition –The approach is based on the idea that people within a particular group tend to behave alike under similar circumstances. User profile –A user profile in this approach comprises a vector of item ratings, with the ratings being binary or real-valued. Process – 구매하거나 경험했던 아이템에 대한 평점을 줌 → user profile 로 구성 – 사용자와 취향이 비슷한 Nearest-Neighborhood 의 profile 과 비교하여 유사도를 계산 – 계산한 유사도를 바탕으로 새로운 아이템에 대한 예상 선호도를 계산 Collaborative filtering approaches (1)

Recommendation approaches 6 Advantage – 사용자가 높이 평가할 수 있는 아이템에 대한 새로운 아이템에 대한 추천가능 Disadvantage – 새로운 아이템이 추가되었을 경우 – 아이템 수에 비해 사용자 수가 적을 경우 – 사용자의 취향이 독특할 경우 Nearest-Neighborhood – 사용자 선호도 예측에 쓰이는 다른 사용자의 수는 그 수가 커질수록 처음에는 시스템 성능이 향상되지만, 어느 수 이상 늘어나면 성능이 저하됨. 따라서 사용자와 유사도가 높은 사용자를 모아 적정 크기의 Nearest- Neighborhood 를 구하여, 추천에 활용함 Collaborative filtering approaches (2)

Recommendation approaches 7 분류 –Memory-based collaborative filtering This approach uses nearest neighbor algorithms that determine a set of neighboring users who have rated items similarly This approach combines the neighbors’ preferences to obtain a prediction for the active user. –Model-based collaborative filtering This approach generalize a model of user ratings using some machine learning approach and uses this model to make predictions. Memory-based is the most popular prediction technique Collaborative filtering approaches (3)

8 Definition –This approach aim at categorizing users based on their personal attributes as belonging to stereotypical classes. User profile –A user profile is a list of demographic features that represent a class of users. Advantage – 사용자에 대한 적은 정보만을 이용하여 효과적으로 사용자의 프로파일을 만 들 수 있음. – 피드백 정보가 없이도 상품에 대한 추천이 가능함 – 시스템 초기 구축 단계나 처음 방문한 사용자에 대해서도 적용할 수 있음 Disadvantage – 구축된 인구 통계 데이터 시스템을 만들기 위해 많은 시간과 노력이 필요함 – 사용자의 관심에 관련한 아이템을 효과적으로 추천할 수 없음 Recommendation approaches Demographic approaches

9 Recommendation approaches Knowledge-based approaches Definition –This approach recommendation is based on inferences about a user’s need and preferences, which are performed using some functional knowledge Advantage – 단순하고, 효과가 좋음. – 역으로 사용자가 제외하고 싶은 아이템에 대해 적용할 수 있음. Disadvantage – 다양한 서비스나 상황에 따라 사용자에게 명시적으로 추천 받을 아이템을 입 력 받는 것이 쉽지 않음 ⇒ 암묵적으로 사용자의 선호를 추출하는 기법에 대해 연구를 진행해야 함.

customers Travel Agency Application Profiles Tour Packages Agent Builds profiles Purchases, Complaints Observes Asks for recommendations, validates suggestions Makes recommendations, prepares reports Manages Suggestions and offers Traveller’s overview 10

11 Purchase Complaints Purchases Customer Preferences in the form of association rules Details of complaints Ratings for tours taken Personal Information Customer Tour and reason For complaining Tour purchased Personal Data Content-based Profile Collaborative Profile Demographic Profile

Traveller’s combined recommendation technique Agent combines the information contained in the different profiles A hybrid method –Three approaches are combined 12 - t j : tour u i : user -Importance of each term : α= 0.3, β= 0.5, γ= 0.2 -cont_pred (t j, u i ) : content-based recommendations -cf_pred (t j, u i ) : collaborative filtering -dem_pred (t j, u i ) : demographic profile

13 Traveller’s combined recommendation technique Content-based recommendations (1) Association rules –Obtain relationships between items in a domain –In this work, Discovery about relationships between different features of tours Obtainment of knowledge about a user’s preferences Build the content-based profile Association rule mining is commonly stated as follows :. I = {i 1, …,i n } : a set of items. D : a set of data cases. X : subset of items in I. X → Y where X ⊆ I, Y ⊆ I and X∩Y = ∮ X is the antecedent of the rule Y is the consequent

14 Traveller’s combined recommendation technique Content-based recommendations (2). Minimum support (s%) : X∩Y. Minimum confidence (c%) : X or Y. Ex) “30% of transactions in a supermarket that contain beer also contain diapers; 2% of all transactions contain both items” → 30% is the confidence of the rule and 2% the support of the rule. Ex). R1 : Month=January → Place=beach, Guests=family [sup: 0.40, conf: 0.825]. R2 : Month=January, Cost=Low → Place=beach, Guests=family [sup: 0.40, conf: 0.775]

15 Traveller’s combined recommendation technique Content-based recommendations (3) Apriori algorithm (Agrawal & Srikant, 1994) –This algorithm for discovering association rules –Input file contain information about different features of holidays bought by a user. (place, cost, destination, type of hotel, guests..) –Filtering steps Elimination of redundant rules Elimination uninteresting rules Selection of interesting rules

16 Traveller’s combined recommendation technique Content-based recommendations (4) The term cont_pred –Association rule –User’s complaints

Traveller’s combined recommendation technique Collaborative filtering (1) Goal –Prediction about the score for an item I j of user U r U = {u 1, u 2,…,u m } : Users I = {i 1,i 2,…,i n } : Items Matrix M (m × n) M rj : a user u r rating on item I j Memory-based approach –Nearest neighbor algorithms Determine a set of neighboring users who have rated items similarly –Neighbors’ preferences Similarity computation – : 0.8, :

Traveller’s combined recommendation technique Collaborative filtering (2) 18

19 Traveller’s combined recommendation technique Demographic profile The dem-pred term –The expert agent compares the characteristics of the tour against the demographic user profile.

20 Experimental results (1) Condition –We compared the prediction values generated for different tours using a pure collaborative approach, a pure content-based approach and our hybrid approach. –The experiments were carried out with 25 users. (a)A family holiday in Fortaleza in January. Collaborative : Content-Based : Hybrid : 6.3

21 Experimental results (2) (b) An economic family holiday in Bariloche in January. Collaborative : 0.2. Content-Based : Hybrid : 5.22 (c) An economic tour to Rio de Janeiro in January. Collaborative : Content-Based : Hybrid : 7.46

22 Experimental results (3) The average precision –The pure content-based approach (using association rules) : 55% –The pure collaborative approach : 52% –The hybrid approach : 80% –The results obtained in the two experiments show that the hybrid approach was more accurate at making recommendations than the other approaches used in an isolated way.

23 Conclusions and future work Traveller –An expert agent in the tourism A variety of approaches Hybrid approaches = Collaborative filtering + Content-based user profiles + Demographic information ⇒ overcome the difficulties of each method used in isolation ⇒ the precision of the recommendations made was higher for the hybrid technique than with each method used separately. Future work Group profiles = individual preferences + preferences of the group