香港科技大学：Finding Competitive Price（寻找竞争价格）

Finding Competitive Price Yu Peng(hong Kong University of Science and Technology Raymond Chi-Wing Wong(Hong Kong University of Science and Technology Presented by Ted Prepared by Raymond Chi-Wing Wong

1 Finding Competitive Price Yu Peng (Hong Kong University of Science and Technology) Raymond Chi-Wing Wong (Hong Kong University of Science and Technology) Presented by Ted Prepared by Raymond Chi-Wing Wong

Outline 1 Introduction 2. Problem definition 3. Algorithm Spatial approach 4. Discussion 5. Empirical Study 6. Related work 7. Conclusion

Outline 1. Introduction 2. Problem Definition 3. Algorithm ◼ Spatial Approach 4. Discussion 5. Empirical Study 6. Related Work 7. Conclusion 2

1 Introduction Consider that a customer looks for a hotel near to sea World hotels H= thi, h2, h3, h4 A={a1} attraction-site(e. g. Sea World) Spatial layout Price Decision-Making table Hotel Distance-to- Price() Hotel Prices) SeaWorld (km) 100 3.0 250 1.0 250 h3200h3 4.0 200 220 2.5 220 According to the spatial layout and the price information we can generate a decision table

1. Introduction Hotel Price ($) h1 100 h2 250 h3 200 h4 220 3 H = {h1 , h2 , h3 , h4} A = {a1} hotels attraction-site (e.g., Sea World) h4 a1 h2 h3 h1 Spatial Layout Price Hotel Distance-to￾SeaWorld (km) Price ($) h1 3.0 100 h2 1.0 250 h3 4.0 200 h4 2.5 220 Decision-Making Table According to the spatial layout and the price information, we can generate a decision table. Consider that a customer looks for a hotel near to Sea World 3

1 Introduction Consider that a customer looks for a hotel near to sea World hotels H= thi, h2, h3, h4 A={a1} attraction-site(e. g. Sea World) Spatial Layout Price Decision-Making table Hotel Distance-to- Price() Hotel Price (S h SeaWorld (km) 100 3.0 100 250 1.0 250 a 200 4.0 200 220 2.5 220 h, dominates h3(since h, is better than h in terms of distance-to SeaWorld and Price)

1. Introduction Hotel Price ($) h1 100 h2 250 h3 200 h4 220 4 H = {h1 , h2 , h3 , h4} A = {a1} hotels attraction-site (e.g., Sea World) h4 a1 h2 h3 h1 Spatial Layout Price Hotel Distance-to￾SeaWorld (km) Price ($) h1 3.0 100 h2 1.0 250 h3 4.0 200 h4 2.5 220 Decision-Making Table Consider that a customer looks for a hotel near to Sea World h1 dominates h3 (since h1 is better than h3 in terms of Distance-to￾SeaWorld and Price)

1 Introduction Consider that a customer looks for a hotel near to sea World hotels H= thi, h2, h3, h4 A={a1} attraction-site(e. g. Sea World) Spatial Layout Price Decision-Making table Hotel Distance-to- Price() Hotel Price (S h SeaWorld (km) 100 3.0 100 50 1.0 250》 a 200 h3 4.0 200 220 2.5 220 h, does not dominate h3(since h has a shorter distance to-SeaWorld than h3 but h 2 has a higher price than h3) 5

1. Introduction Hotel Price ($) h1 100 h2 250 h3 200 h4 220 5 H = {h1 , h2 , h3 , h4} A = {a1} hotels attraction-site (e.g., Sea World) h4 a1 h2 h3 h1 Spatial Layout Price Hotel Distance-to￾SeaWorld (km) Price ($) h1 3.0 100 h2 1.0 250 h3 4.0 200 h4 2.5 220 Decision-Making Table Consider that a customer looks for a hotel near to Sea World h2 does not dominate h3 (since h2 has a shorter Distance￾to-SeaWorld than h3 but h2 has a higher price than h3 .)

1 Introduction Consider that a customer looks for a hotel near to sea World hotels H= thi, h2, h3, h4 A={a1} attraction-site(e. g. Sea World) Spatial Layout Price Decision-Making table Hotel Distance-to- Price() Hotel Price (S h SeaWorld (km) 100 3.0 h3 is dominated by h1 1.0 250 00 a set of all"best"possible hotels 220 2.5 220 skyline: a set of hotels which are not Skyline thy, h2, h4l dominated by other hotels

1. Introduction Hotel Price ($) h1 100 h2 250 h3 200 h4 220 6 H = {h1 , h2 , h3 , h4} A = {a1} hotels attraction-site (e.g., Sea World) h4 a1 h2 h3 h1 Spatial Layout Price Hotel Distance-to￾SeaWorld (km) Price ($) h1 3.0 100 h2 1.0 250 h3 4.0 200 h4 2.5 220 Decision-Making Table Consider that a customer looks for a hotel near to Sea World Skyline: a set of hotels which are not dominated by other hotels Skyline = {h1 , h2 , h4} h3 is dominated by h1 A set of all “best” possible hotels

1 Introduction Consider that a new company wants to open a new hotel h hotels H=th1, h2,h3, h, h) A={a1} attraction-site(e. g. Sea World) Spatial layout Price Decision-Making table Hotel Distance-to- Price() Hotel Prices) SeaWorld(km) 100 3.0 250 1.0 250 200 hb4h 4.0 200 220 2.5 220 ? 2.0 How can we set the price of hf?

1. Introduction Hotel Price ($) h1 100 h2 250 h3 200 h4 220 7 H = {h1 , h2 , h3 , h4} A = {a1} hotels attraction-site (e.g., Sea World) h4 a1 h2 h3 h1 Spatial Layout Price Hotel Distance-to￾SeaWorld (km) Price ($) h1 3.0 100 h2 1.0 250 h3 4.0 200 h4 2.5 220 Decision-Making Table Consider that a new company wants to open a new hotel hf hf , hf} hf ? hf 2.0 ? How can we set the price of hf?

1 Introduction Consider that a new company wants to open a new hotel h hotels H=th1, h2,h3, h, h) A={a1} attraction-site(e. g. Sea World) Spatial layout Price Decision-Making table Hotel Distance-to- Price() Hotel Prices) SeaWorld (km) 100 30 100 250 1.0 250 200 h 4.0 200 220 2.5 220 h 2.0 300 hf is dominated by h2 $300 is not a competitive price. 8

1. Introduction Hotel Price ($) h1 100 h2 250 h3 200 h4 220 8 H = {h1 , h2 , h3 , h4} A = {a1} hotels attraction-site (e.g., Sea World) h4 a1 h2 h3 h1 Spatial Layout Price Hotel Distance-to￾SeaWorld (km) Price ($) h1 3.0 100 h2 1.0 250 h3 4.0 200 h4 2.5 220 Decision-Making Table Consider that a new company wants to open a new hotel hf hf , hf} hf ? hf 2.0 ? 300 hf is dominated by h2 . $300 is not a competitive price

Problem(Finding Simple Competitive Price): Given a set of existing hotels and a new hotel h what greatest possible price can we set for hf such that h is in the skyline? 1 Introduction Consider that a new company wants to open a new hotel h hotels A={a1} attraction-site(e. g. Sea World) Spatial layout Price Decision-Making table Hotel Distance-to- Price() Hotel Prices) SeaWorld (km) 100 3.0 250 1.0 250 200 4.0 200 220 2.5 220 ? 2. 230 hf is not dominated by any hotel $230 is a competitive price. 9

1. Introduction Hotel Price ($) h1 100 h2 250 h3 200 h4 220 9 H = {h1 , h2 , h3 , h4} A = {a1} hotels attraction-site (e.g., Sea World) h4 a1 h2 h3 h1 Spatial Layout Price Hotel Distance-to￾SeaWorld (km) Price ($) h1 3.0 100 h2 1.0 250 h3 4.0 200 h4 2.5 220 Decision-Making Table Consider that a new company wants to open a new hotel hf hf , hf} hf ? hf 2.0 ? 230 hf is not dominated by any hotel. $230 is a competitive price. Problem (Finding Simple Competitive Price): Given a set of existing hotels and a new hotel hf , what greatest possible price can we set for hf such that hf is in the skyline?

Problem(Finding Simple Competitive Price): Given a set of existing hotels and a new hotel h what greatest possible price can we set for hf such that h is in the skyline? 1 Introduction Consider that a new company wants to open a new hotel h hotels H=th1, h2,h3, h, h) A={a1} attraction-site(e. g. Sea World) Spatial layout Price Decision-Making table In order to make sure that h is chosen by Hotel Distance-to- Price(s) customers with a higher probability SeaWorld(km) we would like to set the price of hf such that 3.0 1. h is in the skyline 1.0 250 2. h dominates at least K hotels where K is a 4.0 200 user parameter. 2.5 220 ? 2.0 230 hf does not dominate any hotel 10

1. Introduction Hotel Price ($) h1 100 h2 250 h3 200 h4 220 10 H = {h1 , h2 , h3 , h4} A = {a1} hotels attraction-site (e.g., Sea World) h4 a1 h2 h3 h1 Spatial Layout Price Hotel Distance-to￾SeaWorld (km) Price ($) h1 3.0 100 h2 1.0 250 h3 4.0 200 h4 2.5 220 Decision-Making Table Consider that a new company wants to open a new hotel hf hf , hf} hf ? hf 2.0 ? 230 hf does not dominate any hotel. In order to make sure that hf is chosen by customers with a higher probability, we would like to set the price of hf such that 1. hf is in the skyline 2. hf dominates at least K hotels where K is a user parameter. Problem (Finding Simple Competitive Price): Given a set of existing hotels and a new hotel hf , what greatest possible price can we set for hf such that hf is in the skyline?