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

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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

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Recommendation approaches

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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

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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

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• 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

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• 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

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• 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)

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• 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

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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

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Traveller’s overview

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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

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- 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

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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

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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]

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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

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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

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Traveller’s combined recommendation tech-nique Collaborative filtering (2)

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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.

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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

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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

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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.

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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