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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
2
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
3
Recommendation approaches
4
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
5
Content-based approaches
• Definition– This approach is based on the intuition that each user ex-
hibits a particular behavior under a given set of circum-stances.
– 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 rec-ommendation based on the features they exhibit.
•Disadvantage– The behavior of users is predicted from their past behavior– Over-specialization– A poor quality
Recommendation approaches
6
• Definition– The approach is based on the idea that people within a par-
ticular group tend to behave alike under similar circum-stances.
• 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
7
• 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-Neigh-
borhood 를 구하여 , 추천에 활용함
Collaborative filtering approaches (2)
Recommendation approaches
8
• 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 predic-tions.
• Memory-based is the most popular prediction technique
Collaborative filtering approaches (3)
9
• Definition– This approach aim at categorizing users based on their per-
sonal attributes as belonging to stereotypical classes.
• User profile– A user profile is a list of demographic features that repre-
sent a class of users.
• Advantage– 사용자에 대한 적은 정보만을 이용하여 효과적으로 사용자의 프로파일을
만들 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
10
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
입력 받는 것이 쉽지 않음⇒ 암묵적으로 사용자의 선호를 추출하는 기법에 대해 연구를 진행해야 함 .
customers
Travel Agency ApplicationProfiles
TourPackages
Agent
Builds profiles
Purchases, Complaints
Observes
Observes
Asks for recommendations, validates suggestions
Makes recommendations, prepares reports
Manages
Suggestions and offers
<Fig. 1. Traveller’s overview>
Traveller’s overview
11
Traveller’s overview
12
Purchase
Com-plaints
Pur-chases
Customer Preferences in the form of association rules
Details of complaints
Ratings for tours taken
Personal InformationCustomer
Tour and reasonFor complaining
Tour purchased
Personal Data
Content-based Profile
Collaborative Profile
Demographic Profile
<Fig. 2. Components of a hybrid user profile>
Traveller’s combined recommendation technique
• Agent combines the information contained in the dif-ferent profiles
• A hybrid method– Three approaches are combined
13
- tj : tour ui : user
- Importance of each term : α= 0.3, β= 0.5, γ= 0.2
- cont_pred (tj ,ui) : content-based recommendations- cf_pred (tj ,ui) : collaborative filtering- dem_pred (tj ,ui) : demographic profile
14
Traveller’s combined recommendation tech-nique 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 = {i1, …,in} : 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
15
Traveller’s combined recommendation tech-nique 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 con-tain 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]
16
Traveller’s combined recommendation tech-nique 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
17
Traveller’s combined recommendation tech-nique Content-based recommendations (4)
• The term cont_pred – Association rule– User’s complaints
Traveller’s combined recommendation tech-nique Collaborative filtering (1)
• Goal– Prediction about the score for an item Ij of user Ur
• U = {u1, u2,…,um} : Users
• I = {i1,i2,…,in} : Items• Matrix M (m × n)• Mrj : a user ur rating on item Ij
• Memory-based approach– Nearest neighbor algorithms
• Determine a set of neighboring users who have rated items similarly
– Neighbors’ preferences
• Similarity computation
– : 0.8 , : 0.2
18
Traveller’s combined recommendation tech-nique Collaborative filtering (2)
19
20
Traveller’s combined recommendation tech-nique Demographic profile
• The dem-pred term– The expert agent compares the characteristics of the
tour against the demographic user profile.
21
Experimental results (1)
• Condition– We compared the prediction values generated for differ-
ent 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 : 3.652
. Content-Based : 5.24
. Hybrid : 6.3
22
Experimental results (2)
<Fig. 6. Recommendation values for differ-ent tours>
(b) An economic family holiday in Bariloche in January
. Collaborative : 0.2
. Content-Based : 5.156
. Hybrid : 5.22
(c) An economic tour to Rio de Janeiro in January
. Collaborative : 4.824
. Content-Based : 5.16
. Hybrid : 7.46
23
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 rec-ommendations than the other approaches used in an iso-lated way.
24
Conclusions and future work
• Traveller– An expert agent in the tourism
• A variety of approachesHybrid approaches = Collaborative filtering + Content-based user profiles + Demographic information⇒ overcome the difficulties of each method used in isola-
tion⇒ the precision of the recommendations made was higher
for the hybrid technique than with each method used separately.
• Future workGroup profiles = individual preferences + preferences of
the group