When you step into SASA and ask the sales
for a foundation make-up, it is quite possible for the sales to suggest you
with another make-up product, for instance, the blusher or BB cream. This is a
very typical recommender case in our daily life.
With the development of internet services, all
kinds of e-commerce platforms could not wait to introduce the recommender
system into their online shops. On one hand, many statistic reports have shown
that the recommender can be really helpful in raising the sales volume. On the
other hand, as an average customer exposed to over hundreds or even thousands
of choices, the recommender system is a time-saving tool for me when shopping.
Since in lecture 9&10 of social media
analytics, many algorithms for recommender have been introduced, in my fourth
post (the last post of this course), I am trying to compare the differences
between user-based collaborative filtering (UserCF) and item-based
collaborative filtering (ItemCF).
The main thought of UserCF is to find out
the users with the similar opinion or behaviors towards the same items first. The
items liked by the user group will be recommended to this user next. For
example, user A is interested in product X1, X2 and Y1 while user B is
interested in X1, X3 and Y2. According to UserCF algorithm, user A and user B
are regarded to have similar preferences, therefore the system may recommend Y1
to user B while recommend Y2 to user A. Figure-1 could be an application of
UserCF with a large possibility.
Figure-1
On contrary, the idea of ItemCF is to find
out the similar items first. When the user is selecting one item, the similar
item can be recommended. The judgment criterion is of the similarity is mainly
by the amount of users who hold the same attitude towards the two items. For
instance, if the item C and item D are liked by a large amount of users, the
system then will regards them as similar items. When user is looking at item C,
item D will be recommended to him or her. The related recommending (相关推荐) could be
implemented by ItemCF from my understanding.
Figure-2
So, will you agree or disagree with me?
