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tags. Union Tu
∪ Tv
is the two users’ all tags. s
uv ,
is the
preference similarity of u and v .
s
| Tu
∩Tv
|
=
| T ∪T
|
uv ,
(3)
u v
Compare the preference similarity between the users.
When s
uv ,
=1, the two users use the same tags. Although
the similarity is high, there is no value to recommend
between the two users. So remove the users of this
similarity firstly.
Then, get the top N users of preference similarity for
target user, and the top N users are the tag-recommenders
for target user. score( u, t)
is the possibility of the tag t
recommended to target user. Sort the score( u, t ) from
highest to lowest, and select the top K tags recommended
to target user. The collection of top K tags is marked as
recommendTag( u ).
score( u, t)
=
1
uv ,
| Su
Ut
| v ∈
∑ (4)
∩ Su ∩ Ut
S
u
is the collection of top N users, and U t
is the
collection of the users who used the tag t .
Then, calculate the relevance of resources and tag
t ∈ recommendTag( u)
. Through the frequency that
all users marked the resource using this tag, the relevance
between the resource and different tags can be measured.
countTagging(,)
t i
relate(, i t)
= (5)
∑ countTagging( k, i)
k∈T i
T
i
is the tag collection of resource i , and
countTagging(,)
t i is the frequency that all users
marked the resource i using tag t . The more
countTagging(,)
t i is, the more relevant between
resource i and tag t .
Remove the resources with a low relevance, and obtain
I which is used to predict. In
the resource collection
ut ,
I
ut ,
, use Eq. (1) and Eq. (2) to recommend resources
under each tag t ∈ recommendTag( u)
.
IV. EXPERIMENT AND RESULT ANALYSIS
A. The experiment based on Movielens data set
The experiment is based on Movielens 10M100K data
set, and select the one-tenth of the data as the
experimental data sets. Randomly select 20% user data as
test data, and the remaining 80% as training data. Repeat
the experiment five times.
B. Evaluation Standard-MAE
Mean Absolute Error (MAE) is the absolute of average
difference between predicting preferences and actual
s
preferences [6]. It reflects the accuracy degree of the
recommendation. If a recommended method obtained a
lower MAE value, the average prediction error is lower.
The method of preference prediction is more accurate and
has better performance.
N
∑ | pRate |
i 1 i
− rRate
=
i
MAE =
N
(6)
rRate
i
is the user’s actual preference of selected
resources in testing data, and pRate
i
is the predicted
preference.
C. Experiment results and analysis
The MAE results of two kinds of algorithm in
Movielens data set in Figure 3:
Figure 3. MAE result of two kinds of algorithm
If the MAE is a lower value, the effect of
recommendation method is better. As the chart shows, the
tag-based collaborative filtering method proposed in this
paper is better than the traditional user-based
collaborative filtering method in aspect of the
recommendation accuracy.
V. SUMMARY
This paper presents an improvement of traditional
collaborative filtering method, and introduces the tagging
system for similarity analysis. The new method reduces
the scarcity of score matrix and is effective to classify the
resources for recommendation. Use Movielens data sets
and MAE indicators to prove the effectiveness of the
recommendation with tags. In addition, the user-tag
relevance and trust degree between users can also be
introduced, and the more accurate recommendation can
be obtained.
REFERENCES
[1] JiaweiHan,MichelineKamber. Data Mining Concepts and
Techniques, Second Edition. 2007
[2] Goldberg, D., Nichols, D., Oki, B., & Terry, D. (1992).
Using collaborative filtering to weave an information
tapestry. Communications of the ACM, 35(12), 61–70.
[3] P. Resnick, N. Iakovou, M. Sushak, P. Bergstrom, and J.
Riedl, “GroupLens: An Open Architecture for
Collaborative Filtering of Netnews,” Proc. 1994 Computer
Supported Cooperative Work Conf, 1994.
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