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A. Problem Description
In a small book recommendation system, the user
collection is{ u0, u1, u2,..., u
5}
, the resource collection is
{ i0, i1, i2,..., i
5}
, i 0
, i 1
, i 2
are classified literature books,
i3, i4,
i
5
are classified mathematics books. The system
use the evaluate-preference way to score the books, the
rating interval is [1, 5]. The system needs to predict u
0
’s
score to book i 5
based on the other users’ score. And the
system decides whether to recommend i 5
tou 0
.
The score matrix table is shown as follows:
TABLE I.
SCORE MATRIX TABLE
Res.
User i i i i i i
0 1 2 3 4 5
u
0 4 4 5 5 4
u
1 5 3 4 1 2 1
u
2 3 4 5 2 2 1
u
3 4 3 3 3 1 1
B. Problem Analysis
The reason is that this method ignores the individuals'
preference differences in various aspects of interest, and
the obtained similarity is vague and not accurate. both
like literature books and mathematics books, but under
the user-based similarity model, the users that the system
recommended are only interested in literature books.
Therefore, using their preference degree of mathematics
books to predict the preference degree of is not accurate.
In other words, using traditional user-based similarity
model to obtain the similar users is incomplete and does
not cover the user's preferences.
III. USER-BASED SIMILARITY MODEL
Tags are a special kind of meta-data (metadata), and
they are one of the necessary functions of Web2.0 sites
[5]. They are from the tag-maker's subjective experience
on resources, while they are also used to describe
resources and classify resources by user. Tag information
directly stands for the user's interest and preference, and
it describes the relationship between users and resources
and has a high potential value. The use of tag information
can provides a useful supplement for collaborative
filtering system, and it can improve the recommendation
accuracy.
u
4 1 2 1 4 5 5
u
5 1 1 2 5 4 5
Using the user-based similarity model collaborative
filtering, each row of the score matrix is every user’s
preference vector, for example, preference( u
0) = {4,4,5,5,4} .
Using Eq. (1), calculate the preference vectors
similarity between user u 0
and user u1...
u
5
, and the
results are sorted from high to low.
su (
0, u
2)
= 0.94;
su (
0, u
3)
= 0.94;
su (
0, u
1)
= 0.89;
su (
0, u
5)
= 0.87;
su (
0, u
4)
= 0.84;
Suppose N=3, and select the top 3 preference similar
users u1, u2,
u
3
to predict u
0
’s score to book i 5
. Using
Eq. (2):
ru (
0, i
5)
= 2.73
Therefore u
0
doesn’t like booki 5
.
This conclusion is clearly at odds with reality.
Seeing fromu 0
’s score to booki 3
, i 4
, u
0
is also very
favorite to mathematics books. Therefore, u
0
is likely to
also show a preference for booki 5
.
Figure 1. Traditional user-based similarity model
Figure 2. User-based similarity model
Tags classify the resources, and reflect the user's
preferences. Through the user’s preference tag to
recommend resources, the users can get a more accurate
recommendation. The methods are as follows:
The tags collections for user u and user v are T
u
and
T
v
. The intersection T u
∩ T v
is the two users’ common
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