Slide 5
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Kazuki Motohashi - Konduit K.K.
実践者向けディープラーニング勉強会 第8回 - 18/November/2019
(a) 旅⾏の⽂脈を予測
- ⼦供連れか否かなど
(b) レビューを始めとするコンテンツを
集めてどれを表⽰するか
- なぜその施設が⼈気か要約
(c) 価格やオプションのトレンド
5
Ϟσϧ׆༻ͷ۩ମྫ
(a) Traveller Context Model (b) Content Curation Model
(c) Content Augmentation Model
Figure 1: Examples of Application of Machine Learning
likely is that a user is shopping for a family trip. Usually, Family
noisy and vast, making it hard to be consumed by users. Content
Curation is the process of making content accessible to humans. For
example, we have collected over 171M reviews in more than 1.5M
properties, which contain highly valuable information about the
service a particular accommodation provides and a very rich source
of selling points. A Machine Learning model "curates" reviews, con-
structing brief and representative summaries of the outstanding
aspects of an accommodation (Figure 1(b)).
2.1.6 Content Augmentation. The whole process of users brows-
ing, selecting, booking, and reviewing accommodations, puts to
our disposal implicit signals that allow us to construct deeper un-
derstanding of the services and the quality a particular property or
destination can oer. Models in this family derive attributes of a
property, destination or even specic dates, augmenting the explicit
service oer. Content Augmentation diers from Content Curation
in that curation is about making already existing content easily
accessible by users whereas augmentation is about enriching an
existing entity using data from many others. To illustrate this idea,
we give two examples:
• Great Value: Booking.com provides a wide selection of prop-
erties, oering dierent levels of value in the form of ameni-
ties, location, quality of the service and facilities, policies,
and many other dimensions. Users need to assess how the
price asked for a room relates to the value they would obtain.
Applied Data Science Track Paper KDD ’19, August 4–8, 2019, Anchorage, AK, USA