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ISBN 978-952-5726-09-1 (Print)
Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10)
Jinggangshan, P. R. China, 2-4, April. 2010, pp. 182-185
Research on Tag-based Collaborative Filtering
Strategy
Shuo Wan 1 , and Huizhong Qiu 2
1 School of Software, University of
Electronic Science and Technology of China, Chengdu, P.R.China
Email: wanshuo168@gmail.com
2 School of Computer Science and Engineering, University of
Electronic Science and Technology of China, Chengdu, P.R.China
Email: hzqiu@uestc.edu.cn
Abstract—Recommendation technology is designed to take
the initiative to recommend using the user's history
behavior information, without requiring users to explicitly
specify the query case information. Collaborative filtering is
the most widely recommended technique. However, some
problems of the traditional collaborative filtering
recommendation system still exist, and these problems
significantly affect the recommended results. Tag system as
the essential functions of Web2.0 websites in recent years
has been very widely used. This article will combine the tag
information with the collaborative filtering recommendation,
and recommend resources by recommending tags. By
analyzing a problem of the traditional collaborative filtering
strategy, this experiment proves tag-based recommendation
strategy can effectively solve these problems and improve
the accuracy of recommendation.
Index Terms—tag, collaborative filtering, recommendation,
similarity, preference
I. TRADITIONAL USER-BASED SIMILARITY
MODEL ALGORITHM
Collaborative filtering assumes that a user in the
system and his similar groups both have the similar
preferences on the system resources, and the users have
the similar preference on the similar resources. Thus
mining the collective wisdom embedded in massive data,
and predicting the individuals with the similar groups.
The traditional user-based similarity collaborative
filtering algorithm is mainly in two steps [6]:
1. To calculate the user's similarity with other users,
and receive the top N preference similar users.
2. Based on the similar user's preferences to predict the
user's preferences, and recommend resources.
Formal description is as follows:
U = { u1, u2,..., u m
} stands for m users collection,
I = { i1, i2,..., i n
} stands for n resources collection, the
preferences of the user on the resources can make a
matrix R (shown as follows), r
ui ,
in matrix R stands
for the preferences of user u on resource i , r
ui ,
= 0
stands for the user u have not evaluated preferences on
resource i .
⎛r11 K r1
n ⎞
⎜
⎟
R = M O M
⎜r
r ⎟
⎝ L ⎠
m1
mn
1. The user’s similarity calculation is based on the row
vector of the matrix R . Calculate cosine similarity of the
vector.
s
uv ,
=
i∈I
i∈I
∑
∑
uv ,
r
r
⋅ r
ui , vi ,
∑
2 2
ui , vi ,
i∈I
uv , uv ,
I
uv , is the intersection of user u and v ’s preference
resources.
2. Select the top N preference similar users. Predict the
user’s preference.
rui ,
= ru +∂ ∑ suv ,
⋅( rvi ,
−rv)
(2)
v∈S
u
∩U
S
u
is the similarity of the top-N users. i
i
r
(1)
U is the
users’ evaluate preference on resource i ,
∂= ∑ v∈ Su ∩ Ui.
1/ s v uv ,
II. THE PROBLEM EXISTED IN USER-BASED
SIMILARITY MODEL
The traditional user-based similarity model is based on
two underlying assumptions:
1. There is only one user's preferences similar model,
and using the only value generated by this similarity
model will be able to determine the similarity of the
user's preferences.
2. The preference similarity on one type of the
resources produced by this model can also be applied to
other types.
But there are some problems in these two assumptions.
The article will illustrate the unreasonableness with the
following examples.
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