Traveling Route Generation Algorithm Based On LDA and Collaborative Filtering

Peng Cui; Yuming Wang; Chunmei Li

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Traveling Route Generation Algorithm Based On LDA and Collaborative Filtering

Peng Cui

Yuming Wang

oraz

Chunmei Li

| 14 paź 2019

International Journal of Advanced Network, Monitoring and Controls

Tom 3 (2018): Zeszyt 4 (January 2018)

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Data publikacji: 14 paź 2019

Zakres stron: 47 - 62

DOI: https://doi.org/10.21307/ijanmc-2019-021

Słowa kluczowe
LDA Model, Collaborative Filtering, KDE Algorithm, Recommended Algorithm, Machine Learning

© 2018 Peng Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

LDA travel route recommendation algorithm based on KDE and classification

Collaborative filtering travel route recommendation algorithm based on KDE and Classification

The experimental results of different values of hyperparameter α

The experimental results of different values of hyperparameter β

The experimental results of different number of topic K

The experimental results of different number of iterations n

The route correlation rate of LDA travel route recommendation algorithm based on KDE and classification

The route correlation rate of collaborative filtering travel route recommendation algorithm based on KDE and classification

ROUTE BASIC ATTRIBUTE TABLE

	id	name	plan_id	type	hours	daysep
meaning	City id	City name	Route id	Route type	Playing time	The flag of end of day
Value type	string	string	string	string	list	bool
example	‘263’	‘Osaka’	‘3799’	‘place’	[4.0,8.0]	true

INPUT AND OUTPUT OF TRAVEL CITY TOPIC MODEL BASED ON LDA

input: preprocessed and classified travel route text set (one route for one line) The number of topic K, hyperparameters α and β
output:1. Topic number assigned to each word of each text2. Topic probability distribution θ for each text3. Characteristic city probability distribution for each topic4. Word id mapping table in the program5. Top-N feature city words sorted from top to bottom for each topic

THE EXPERIMENTAL RESULTS OF DIFFERENT VALUES OF HYPERPARAMETER A

α	5	10	15	20	25	30	35	40	45	50
log	4.72	4.16	4.02	3.38	3.21	3.16	3.82	4.12	4.68	5.16
p(e)	0.282	0.254	0.236	0.192	0.166	0.171	0.179	0.216	0.249	0.288

THE EXPERIMENTAL RESULTS OF DIFFERENT NUMBER OF ITERATIONS N

n	300	400	500	600	700	800	900	1000	1100	1200
log	6.15	5.86	4.02	3.98	3.82	3.64	3.12	3.31	3.41	3.53
p(e)	0.332	0.308	0.275	0.262	0.236	0.214	0.161	0.172	0.181	0.194

THE EXPERIMENTAL RESULTS OF DIFFERENT VALUES OF HYPERPARAMETER B

β	0.01	0.05	0.10	0.15	0.20	0.25	0.30	0.35	0.40	0.50
log	5.62	4.42	4.02	3.32	4.23	5.10	5.82	5.92	6.58	7.21
p(e)	0.282	0.254	0.236	0.172	0.198	0.216	0.232	0.299	0.328	0.356

THE OUTPUT RESULTS OF DIFFERENT ALGORITHM

The input of algorithm	total days of travel	7
The input of algorithm	cities that user wants to go	Osaka, Nagoya
The output of algorithm	No improved LDA recommended algorithm	[Naoshima: 2.5, Yamanashi: 1.8, Osaka: 56.4, Nagoya: 29.8]
	No improved collaborative filtering recommendation algorithm	[Yakushima: 12.5, Naoshima: 8.6, Osaka: 42.8, Nagoya: 26.2]
	LDA travel route recommendation algorithm based on KDE and classification	[Kyoto: 42.4, Nakafurano-cho: 3.9, Osaka: 15.5, Nagoya: 16.5]
	collaborative filtering travel route recommendation algorithm based on KDE and classification	[Kyoto: 24.2, Tokyo: 20.3, Osaka: 15.5, Nagoya: 16.5]

THE EXPERIMENTAL RESULTS OF DIFFERENT NUMBER OF TOPIC K

k	4	6	8	10	12	14	16	18	20	22
log	5.62	4.42	4.02	3.32	3.26	5.10	5.82	5.92	6.58	7.21
p(e)	0.223	0.214	0.205	0.196	0.182	0.226	0.265	0.314	0.408	0.516

eISSN:: 2470-8038
Język:: Angielski

Częstotliwość wydawania:: 4 razy w roku
Dziedziny czasopisma:: Computer Sciences, other

Kanał RSS czasopisma

Traveling Route Generation Algorithm Based On LDA and Collaborative Filtering

Data publikacji: 14 paź 2019

Zakres stron: 47 - 62

DOI: https://doi.org/10.21307/ijanmc-2019-021

Słowa kluczowe
LDA Model, Collaborative Filtering, KDE Algorithm, Recommended Algorithm, Machine Learning

© 2018 Peng Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

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Figure 4.

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Figure 8.

Figure 9.

ROUTE BASIC ATTRIBUTE TABLE

INPUT AND OUTPUT OF TRAVEL CITY TOPIC MODEL BASED ON LDA

THE EXPERIMENTAL RESULTS OF DIFFERENT VALUES OF HYPERPARAMETER A

THE EXPERIMENTAL RESULTS OF DIFFERENT NUMBER OF ITERATIONS N

THE EXPERIMENTAL RESULTS OF DIFFERENT VALUES OF HYPERPARAMETER B

THE OUTPUT RESULTS OF DIFFERENT ALGORITHM

THE EXPERIMENTAL RESULTS OF DIFFERENT NUMBER OF TOPIC K

Traveling Route Generation Algorithm Based On LDA and Collaborative Filtering

Data publikacji: 14 paź 2019

Zakres stron: 47 - 62

DOI: https://doi.org/10.21307/ijanmc-2019-021

Słowa kluczoweLDA Model, Collaborative Filtering, KDE Algorithm, Recommended Algorithm, Machine Learning

© 2018 Peng Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

ROUTE BASIC ATTRIBUTE TABLE

INPUT AND OUTPUT OF TRAVEL CITY TOPIC MODEL BASED ON LDA

THE EXPERIMENTAL RESULTS OF DIFFERENT VALUES OF HYPERPARAMETER A

THE EXPERIMENTAL RESULTS OF DIFFERENT NUMBER OF ITERATIONS N

THE EXPERIMENTAL RESULTS OF DIFFERENT VALUES OF HYPERPARAMETER B

THE OUTPUT RESULTS OF DIFFERENT ALGORITHM

THE EXPERIMENTAL RESULTS OF DIFFERENT NUMBER OF TOPIC K

Słowa kluczowe
LDA Model, Collaborative Filtering, KDE Algorithm, Recommended Algorithm, Machine Learning