Do you wonder what Airlines use to recommend you travel destinations? Knowledge graphs, Deep Learning, Embeddings, etc. We present through these slides a state of the art approach to provide you personalized travel destinations where you would like to go to.

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Predicting Your Next Trip:
A Knowledge Graph-Based
Multi-task Learning Approach
for Travel Destination Recommendation
Amine Dadoun, Raphael Troncy,
Michael Defoin-Platel,
Gerardo Ayala Solano
RecTour @ RecSys 2021, 26 Sept 2021

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Motivation

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Searches Books Boards Departs
Inspire
Traveler Journey flow
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Destination Recommendation Systems
Travelers want to get inspired !
Inspire Flyer proposes
generic travel inspirations and
practical trip information
https://www.inspireflyer.com/

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Destination Recommendation Systems
Need for more personalization and contextualization
EasyJet question-based
destinations search
Emirates map-based destinations search
Audley interest-
based Trip finder
EasyJet books flights
using screenshots of
Instagram posts
https://www.audleytravel.com/

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Page 6
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Problem Formulation
Where to
go next?
Ticket price
Travel dates
Transport alternatives
Geographical location
Travel context
POIs
Preferences: Traveler’s History

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Hypothesis
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• Represent all information in one unique data structure
• Semantic enrichment of the data
• A set of different Knowledge Graph-based Recommender System algorithms
Knowledge graph (as) a vehicle for representing heterogeneous information

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Hypothesis: Literals requires special treatments
- Numerical Values:
- Prices, Number of passengers, etc.
- Dates:
- Departure Date, Arrival Date, etc.
- Quantity (Duration, measure):
- Flight distance, stay duration, advanced purchase, etc.
- Texts:
- Ancillary description, entity labels, etc.
Literature not mature enough to consider literals for Knowledge Graph Embedding:
- Models fail to capture the semantics of the data types/units of (numeric) literal values
- Drawback lies in the fact that text and numeric literals are treated in the same way
Several types of information

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Objective & RQs
Research Questions:
• RQ 1: What is the benefit of using a knowledge graph as a common data structure containing all the
input information of the recommender system?
• RQ 2: Given the heterogeneous nature of the information included in the knowledge graph
(numerical values, dates, texts, etc.), what is the best performing approach for travel destination
recommendation?
Provide leisure travelers with travel destinations that they have never visited before
Objective:

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Data & Knowledge Graph

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A 360 View of travelers journey

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Data sources
From historical trips to semantic data
November 2018 December 2019
Historical Trips
• Trip 1: London
• Trip 2: Venice
• Trip 3: Maldives
Historical Trips
• Trip 1: London
• Trip 2: Venice
• Trip 3: Maldives
Wikipedia, STD
Traveler’s Demographics
43 years old,
Malaysian,
Male,
Frequent Flyer
Collaborative
Information
Knowledge
Information
Contextual
Information
Demographic
Information
&
Booking Context Shopping Context Travelers’ preferences Travelers’ demographics Travelers’ purchase history
Semantic Trails Dataset

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Some statistics of the dataset
#Travels #Travelers #Destinations* Sparsity 𝝆
486 807 40 965 136 91.26%

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Airline Travel Knowledge graph
Illustration & Some statistics
<http://data.amadeus.com/rloc/PJ936>
<http://data.amadeus.com/rloc/PJ936/journey/1>
ams:hasJourney
<http://data.amadeus.com/airport/KUL>
<http://data.amadeus.com/airport/MEL>
schema:reservedTicket
<http://data.amadeus.com/rloc/PJ936/airticket/21563>
“One Way”
<http://data.amadeus.com/TripReservation>
“$325”
ams:departureDate
“2020-05-06"^^xsd:date
<http://www.wikidata.org/wiki/Q334>
"1994-03-27"^^xsd:date
schema:underName
2
<http://data.amadeus.com/traveller/T21652>
<http://www.wikidata.org/wiki/Q500253>
<http://data.amadeus.com/formOfPayment/CreditCard>
<http://data.amadeus.com/city/Melbourne>
<http://www.wikidata.org/entity/Q3141>
“4529500"^^xsd:integer <http://www.wikidata.org/entity/Q515>
"0.932"^^xsd:float
“719.1"^^xsd:float
dbo:continent
<https://www.wikidata.org/wiki/Q48>
travelto
<https://en.wikipedia.org/wiki/Melbourne>
owl:sameAs
#nodes #edges #properties #trip reservations
634 254 ∼ 13.7 M 48 423 427
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Approach & Baselines

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KGMTL4Rec
𝑎𝑖, 𝑎𝑗
Numerical
Attributes
Embedding Layer
𝑣𝑗 ∈ [0,1]
𝑣𝑖 ∈ [0,1]
𝑒𝑖, 𝑟𝑘, 𝑒𝑗
Entity Embedding
Layer
Relation
Embedding Layer
StructNet
Tanh
𝑡 ∈ {0,1}
𝑑𝑖, 𝑑𝑗
ConvNet
MSE
Regression Binary classification Multi-label classification
𝑤𝑒𝑖
+ 𝑤𝑒𝑗
+ 𝑤𝑟𝑘
[𝑤𝑒𝑖
; 𝑤𝑎𝑖
] [𝑤𝑒𝑗
; 𝑤𝑎𝑗
]
Tanh
Tanh
[𝑤𝑑𝑖
𝐶𝑁𝑁
; 𝑤𝑒𝑖
]
Tanh
[𝑤𝑑𝑗
𝐶𝑁𝑁
; 𝑤𝑒𝑗
]
Tanh
𝑣𝑗
′
𝜎
𝑣𝑖
′
𝜎
𝑡′
= 𝑝𝑒𝑖,𝑟𝑘,𝑒𝑗
𝜎
Log-loss
𝑠𝑗
′
𝑠𝑖
′
DescNet
CrossEntropy
AttrNet
𝑠𝑖 ∈ 𝑅|𝐷| 𝑠𝑗 ∈ 𝑅|𝐷|
𝑆𝑜𝑓𝑡𝑚𝑎𝑥 𝑆𝑜𝑓𝑡𝑚𝑎𝑥
A multi-task learning algorithm (to deal with multi typed data)
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Experiments

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Evaluation Metrics
Objective: Build a model that predicts probability that a traveler book a suggested travel
destination
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Hitrate: Mean Reciprocal Rank:

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Leave-last-out Protocol: For each user, we remove the last destination the traveler went to, and consider it in the test set.
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Evaluation Protocol
Training Procedure
Time
Training Data
Test Data
Recommender
System
Non Existing
Traveler-Destination
pair
Recommender
System trained
Ranked list
of Destinations
Prediction
…
1.
2.
4.
3.
 Hitrate@K [3]
 MRR@K [7]
Adelaide
Osaka
Phuket
Brunei

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Recommendation performance
Model Hit@10 MRR@10
Item-Pop 0.5168 0.2634
Item-KNN [28] 0.3223 0.1367
MF(BPRMF) [26] 0.5698 0.3036
NCF [13] 0.5132 0.2994
FM [25] 0.5986 0.3401
WDL [6] 0.6301 0.3472
DKFM [8]
0.6619 0.3901
Recommendation performance of Collaborative Filtering,
hybrid and context-aware recommender systems.
Model Hit@10 MRR@10
NTN [30] 0.3096 0.1511
SME [3] 0.3746 0.1992
TransE [4] 0.4548 0.2268
TransR [17] 0.4031 0.1883
ER-MLP [9] 0.6218 0.3559
CKE [39] 0.6493 0.3865
KGMTL4Rec
0.7109 0.4254
Recommendation performance of
knowledge graph-based recommender systems.
Convergence of the 4 main models with respect to the number of iterations.

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Data contribution w.r.t the recommendation performance
KGMTL4Rec vs other models
Collaborative
Information
(Past
bookings)
Content
Information
(metadata)
All
Information*
Model HR@
10
MRR@
10
BPRMF 0.5462 0.3020
KGMTL4Rec 0.5623 0.3153
WDL 0.6001 0.3472
KGMTL4Rec 0.6508 0.4061
DKFM 0.6464 0.3856
KGMTL4Rec 0.6907 0.4189
*Past bookings, metadata + context

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Ablation Study
KGMTL4Rec with different type of information
Better
Numerical
literals
STD KG Wikipedia SubNetworks HR@
10
MRR@
10
{S} 0.5884 0.3264
{S, A} 0.6508 0.4061
{S, A} 0.6781 0.4119
{S, A, D} 0.6907 0.4189
{S: StructNet, A: AttrNet, D: DescNet}

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Experiments
Influence of travel history and algorithm convergence
DKFM is more affected by the number of historical
travels than KGMTL4Rec
KGMTL4Rec convergence w.r.t Multi-task learning
strategy

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Hyper-parameters Influence
- Optimal embedding size is 128
- Best learning rate is 3 × 10−4

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KG-based Recommender System for travel destination recommendation
Conclusions & Future works
Outlook:
• Use multi-task learning for different recommendation tasks (destinations, ancillaries, etc.)
• Explore other source of information (e.g. images)
• Go live
Conclusion:
• Outperform the current in-production Rule-based approach
• Using KG simplify the use of different type of data
• Using KG-based RS enables to have better results and it is less painful (DKFM)
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Open-source code: https://gitlab.eurecom.fr/amadeus/KGMTL4Rec

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Thank you!
This presentation
https://bit.ly/3lOjmrK
@AmineDadoun
amine.dadoun@eurecom.fr
Thank you!
Q&A