Welcome to my blog, this is my
first post about the course Social Networking; I will not cover all the
concepts of the 1st and 2nd classes but focus on the recommendation
system of social media.
In general, there are two
types of recommendation systems: Direct Social Recommendations and Derived
Social Recommendations.
The former one is easy to
understand, the system will ask users to recommend the things they bought or
watched to others they know. For instance, when you bought goods from
Taobao.com, the system will ask you to recommend the goods to your friends or
share it on social media. Actually, these systems are almost everywhere on the
internet.
While the latter is much more
complicate. Distinct from the Direct Social Recommendations, the Derived Social
Recommendations always have well-designed algorisms to predict the users’
favors. Take Youku as an example, whenever you finish watching a video, the
system will find a list of recommended video to you.
And the prediction algorisms are divided
into two ways: Collaborative filtering and Content-based filtering. I will focus
on the Collaborative filtering because it is more widely-used.
The
definition of Collaborative filtering from Wikipedia is: Collaborative
filtering methods are based on collecting and analyzing a large amount of
information on users’ behaviors, activities or preferences and predicting what
users will like based on their similarity to other users. These filtering
methods also have two types, one is User-based filtering, and another is
Item-based filtering. In my opinion, the major difference between the two
methods is, the User-based filtering take users into different groups while the
Item-based filtering group the items. Let’s take a simple example to make it
easier to understand.
We
assume that Item1 and Item3 are correlated and User A and B have similar
tastes, in the example upon, both the two users like Item1 and user A also like
Item2, so in the User –based algorithm, Item2 is recommended to User B because
the two users are in the same “group”. But when it turns to Item –based
algorithm, the system recommends Item3 instead of Item2 to the two users for the
reason that Item1 and item3 are in the same “group”.
I
think it is really hard to give an absolute result about which one is better
because the User –based algorithm seems more progressive
while the Item –based one is more conservative and they give different ways
of user experience. Maybe in most cases, Item –based
algorithm is more accurate.
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to comment on my blog.
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