pressure on the local environment and
public services, especially for transportation. Take the China National Day (an
annual weeklong holiday beginning October 1) for example: In 2015, visitors to
Huangshan mountain spent nine hours
on average waiting in line. In 2016, more
than 25 million tourists visited Chongqing city—a western metropolitan city
whose residential population is 30 million. In 2017, the traffic congestion on
the Hukun expressway was accumulated to maximally 49.73km. Such overcrowded populations, as we know, lead
to problems like pollution, congestion,
and loss of open spaces, and causes inconvenience and negative experiences
for both tourists and local residents.
As the largest online travel agency in
China, Ctrip discovered an insight from
its big data—that is, there is an imbalanced situation between the distribution
of tourists and the collective capacity of
attractions. The agency envisioned that
a good recommendation system could
help divert tourists to less-crowded attractions to resolve the problem. The
basic idea is to build a tourist prediction
component, and once an attraction is
predicted as overcrowded, a recommendation component will be triggered to try
to divert tourists to other places.
However, online tourism products
are very different from regular commodities due to several factors, including:
holiday travel is a low-frequency event,
most people travel only 1–2 times per
year; and, numerous travel packages generate different combinations of transport means, restaurants, and hotels.
Thus, most travel products have very few
or even zero customers, and it is very difficult to simply apply traditional recommendation algorithms to this scenario.
Ctrip’s solution for recommendation is twofold: user-profiling based on
its big tourism data accumulated over
the last 18 years, and developing a hybrid collaborative filtering model that
specifically targets the sparse data and
Figure 5 is the user preference tree
built from historical travel data. The
short-term profile has the same structure of the long-term one, but is limited
to the latest 30 days’ data. The system
can quickly iterate the tree and generate
a preference vector for a user, as the input for the recommendation system.
The key to the enhanced recommen-
dation algorithm is the so-called Addi-
tional Stacked Denoising Autoencoder
(aSDAE), 3 which employs the deep learn-
ing model to learn the latent variables of
users and products, and combine it with
the classic matrix factorization. The la-
tent variables learned from the two mod-
els are used to fit the product-scoring ta-
ble that is initialized by users’ feedbacks.
Moreover, the overall loss function is a
linear combination of two models’ loss
functions. Lastly, a text-generation AI
component will creatively generate po-
etry to characterize the recommended
attraction and push to users. The test
has shown the algorithm performs bet-
ter than traditional ones for the sparse
data and cold start scenarios.
The system has been deployed to
governments such as Henan province,
Guiyang City, and many others. In the
Henan province, for example, the recommendation system was deployed last
March. According to Ctrip’s online travel booking data, during the Labor Day
holiday (a period of three days starting
around May 1), the total number of tourists in the Henan province reached 2.04
million, which is a 41.5% increase from
Labor Day in 2017. To evaluate the effect
of its recommendation system, the tourist distribution over 18 areas throughout
the province is calculated. The standard deviation (SD) of the distribution
is 234,355 in 2017 and 202,208 in 2018.
The SD decrease suggests a more balanced experience visiting the province’s
many attractions, which benefits both
tourists and local residents.
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Wanli Min is Chief Data Scientist and Senior Director at
Alibaba Cloud Computing in Hangzhou.
Liang Yu is Senior Data Scientist at Alibaba Cloud
Computing in Hangzhou.
Lei Yu is head of the AI Department at Ctrip in Shanghai.
Shubo He is manager of the AI Department at Ctrip in
© Copyright 2018 ACM 0001-0782/18/11 $15.00.
Figure 5. User preference tree built from Ctrip’s big tourism data.
POI Tourism theme