SEKE 2012 Proceedings - Knowledge Systems Institute

such as one week and gradually deteriorate as statistical
period goes long, this may indicate that users’ preference
varies fast, the most popular products this week will be different
from those of next week. Item-to-item-CF performs
better when time goes by because it really needs some time
to collect users’ visiting or purchasing data for computing
item similarity.
5. Acknowledgment
Figure 2. Top-5 precision of different time period
Content-based method.
User-based-CF: user-based-CF performs much worse
than item-to-item-CF mainly because the key point of this
algorithm is accurately computation of the user similarity.
As mentioned in Data Set section, on average every user
visits only 5 products in the site, so it is severely lack of
co-visited products of users to compute user similarity. Another
factor of user-based-CF’s bad performance is the same
as content-based method, we have no ratings to know users’
preference to items. To get rating information needs users’
extra effort. Because the number of user is much large
than that of item,computing user similarity costs more than
computing item similarity, that is why User-based-CF costs
more than Item-to-item-CF.
Most-popular: This simple benchmark does a good job
in our experiment because of its high cost performance.
This method needs not much information such as item feature
information, user rating and calculation cost is also low.
It only needs the statical information of users’ visiting or
purchasing information.
Cheapest&Newest: These two methods derived from
common business sense do not surprise us, but we still recommend
to have a try on these simplest methods because
sometimes simple does not mean ineffective. To make the
recommendation process complex and obscure is not the
objective of recommendation.
What we also want to discuss is the time value of the
data. We choose item-to-item-CF and most popular which
get relatively better performance to test how time factor affect
results. Figure 2 shows the result of these two methods
in different time period.
We can figure out data from different time period will
affect result a lot, and also the effect to different methods
is different. Most-popular perform better in a short period
The work described in this article was partially supported
by the Humanities and Social Sciences Foundation
of Ministry of Education of China (10YJC870020,
10YJC630283), the National Natural Science Foundation
of China(11171148, 61003024). The authors would like to
thank the industrial partner for sharing data.
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