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24.What do we know about the author's dance experiences
A.She took the stage before five.
B.She tried diverse dance styles.
C.She cared little about awards.
D.She arranged a dance at nine.
25.What was the author's misunderstanding about dance
A.Its value was about processes.
B.Its focus was on self-comfort.
C.Its skills must be perfect.
D.Its appeal lay in postures.
26.Why did the author treat the young dancers kindly
A.She recalled her own childhood.
B.They explored new dance skills.
C.They had decent study habits.
D.She wanted to cheer them up.
27.What does the author learn from dance
A.It fuels positive feelings.
B.It makes people grow up.
C.It offers shy people joy.
D.It shows one's true self.
c
As consumers rarely buy items alone but shop in sets or groups,either to save on delivery
costs and time,to reach minimum purchase quantities for a discount,or simply due to habit,
keeping products in the shopping basket,whether it is online or physical,before checking out is
the common practice.Still,the existing recommendation systems have only considered product
choices at the item level and baskets level by integrating purchase patterns and history with
users'step-by-step behaviour across transactions().
"One critical factor that is often ignored is the price,says Francisco Cisternas,Senior
Lecturer of Marketing at the Chinese University of Hong Kong Business School."Price is a key
factor in consumer decision-making and it varies across consumers and product categories,so
integrating price into recommendation systems is challenging due to its intricacy."
It was because of its complexity that Dr.Cisternas and his co-authors introduced a novel
approach that integrates price sensitivity,called the basket-augmented dynamic heterogeneous
hypergraph(BDHH)."We aimed to address this gap by integrating price sensitivity and
enhancing the connections among users,items and baskets to better match recommendations
with real-world shopping behaviour,"he says.Importantly,the model doesn't just treat each
connection equally,but also factors in pricing data.By doing so,the model can accurately
understand how sensitive different shoppers are to cost,as well as how this informs what they're
likely to buy.
The team tested their model across a range of datasets and compared it to existing
recommendation systems.In nearly all cases,BDHH outperformed traditional models,
especially in situations where price sensitivity played a major role.That means it was not only
better at predicting what someone might buy next,but also when it comes to understanding why
they might buy it.
In practical terms,the system can understand that a particular user often buys a particular
cereal()when it's on sale.This ability to recognize deeper purchasing patterns enables
smarter basket predictions that matches individual budgets and tastes,which could then
translate into an enhanced shopping experience for users.
28.What do consumers usually do before checking out
A.Store selected goods.
B.Compare goods prices.
C.Ask for low delivery fees.
D.Refer to discount policies.
29.What does the underlined word "intricacy"in paragraph 2 mean
A.Availability.
B.Complexity.
C.Management.
D.Judgement.
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