Restaurant Choice Model
08. UTokyo bin A Team
Aiko Kondo, Kana Masuhashi, Yosuke Mochizuki, Kiri Fukutani,
Daichi Ogawa, Ryo Kato, Isamu Okuta, Kasun Thalgaskotuwa

Background
2軒⽬⾏こうぜ！
どこに⼊れば満⾜できるかなぁ
Which izakaya will
satisfy me?

1Motivation

Bar-hopping
behavior

Pure Curiosity

How do we hop from a restaurant to another ?

Open Data

SearchModel

linked toratings

choicebehavior
in a city

Deep learning& image recognition

text

image
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Framework※ Open Data
Monzennaka-Cho2021

Data
Proccessing

Estimation&SimulationEstimation model

based on search theory

PP Data
MapMatching𝜷parameter

Path Data

Network Data

Data on link transition

Links obtained fromOpen Street Map＝Behavior

Tied tothe closest link

Budget(dinner): ¥2,500
Genre: BarRating: ★★★☆☆ =3.2

CNN

more
search...

Simulation

Logit model & search algorithml Randomly generated
O by building polygons
l How do we repeat thedismissal and admissionof restaurant choicedecision?

Adopt it!
more
search...

Impression Score★★★★☆ =4.1

Data about restaurant
from Gourmet Review Sitepicture3Formulation
Formulation and estimation method of proposed model
買い⼿がたまたま⾒かけた飲⾷店の価格が𝑝のとき、買い⼿の状態の価値（ = 満⾜度）𝑉はWhen a buyer happens to see a restaurant at a price of 𝑝, thebuyer's state value 𝑉 is𝑣 : impression score
𝑝 : average budget of the store
𝑥! :the link distance%1
𝑉 = max {𝑣 − 𝛽! 𝑝, −𝛽" 𝑥" + 1 𝑣# − 𝛽! 𝑝# }𝑛

サーチを停⽌し、この飲⾷店に⼊店したときの余剰
Surplus when the search is stopped.

#$"

コスト + そのエリアのお店に⼊店したときの期待効⽤
Cost + expected utility of entering a store in the area%

1
𝑉 & = 𝑣 − 𝛽! 𝑝 − −𝛽" 𝑥" + 1 𝑣# − 𝛽! 𝑝#𝑛#$"+𝜀Error term (normal distribution)↓Binary probit model
Maximum Likelihood Estimation

4Simulation
Search Theory

Sequential Logit

外観、予算、距離をもとに⼊店する/しないを判断

各交差点で、沿道の店舗をもとに選択
Route choice based on the restaurant along the streets

Input•Network•OD

Generate
Agents

経路選択

Route Choice

特定のジャンルへの選好(e.g, Love French cuisine!)
Prefer specific cuisine

Decide based on exterior, budget, and distanceto the next candidate restaurant

Identify
candidaterestaurant

just pass byEnter?

YesNoSatisfied?

YesEnd!

No
Seek for next

該当リンクに複数店舗がある場合、店舗外観により確率的に⼊店候補を割り付け
One candidate restaurant is chosen for each linkbased on exterior appearance

Logit Form Probability

累積効⽤値が閾値を超えるか？
Does the cumulativeutility exceed thethreshold?

5Simulation
Search Theory

Sequential Logit

外観、予算、距離をもとに⼊店する/しないを判断

各交差点で、沿道の店舗をもとに選択
Route choice based on the restaurant along the streets

Input•Network•OD

GenerateAgents

経路選択

Route Choice

特定のジャンルへの選好(e.g, Love French cuisine!)
Prefer specific cuisine

Decide based on exterior, budget, and distanceto the next candidate restaurant

Identify
candidaterestaurant

just pass byEnter?

YesNoSatisfied?

YesEnd!

No
Seek for next

該当リンクに複数店舗がある場合、店舗外観により確率的に⼊店候補を割り付け
One candidate restaurant is chosen for each linkbased on exterior appearance

Logit Form Probability

累積効⽤値が閾値を超えるか？
Does the cumulative
utility exceed thethreshold?

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Simulation Result
サーチパラメータ（探索⽅針）を変更した時の期待効⽤
Expected utility of changing search parameters (search policy)
期待効⽤の成分ごとの変化

Variation in Expected Benefits by Component

最適⽅針と実際の⽅針のずれ
Misalignment between optimal policy and actual policyGAP!!

探索⽅針の最適値

実際の探索⽅針
期待効⽤

𝑉 & = 𝑣 − 𝛽! 𝑝 − 𝛽" 𝑥"
店の満⾜度

⽀払額

総探索コストl 実現されている探索⽅針は最適解に乖離
The realized search policy is misaligned with the optimal solution.

※ サーチパラメタを変化させている。
※ 同時に、推定されたパラメータは個々のエージェントの認知を表す項として採⽤した。
The search parameters were varied.
At the same time, the estimated parameters were employed as terms representing the individual agent's cognition.

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