=Paper=
{{Paper
|id=Vol-497/paper-4
|storemode=property
|title=Social Tag Prediction Base on Supervised Ranking Model
|pdfUrl=https://ceur-ws.org/Vol-497/paper_03.pdf
|volume=Vol-497
|dblpUrl=https://dblp.org/rec/conf/pkdd/CaoXXLTH09
}}
==Social Tag Prediction Base on Supervised Ranking Model==
https://ceur-ws.org/Vol-497/paper_03.pdf
Social Tag Prediction Base on Supervised
Ranking Model
Hao Cao, Maoqiang Xie, Lian Xue, Chunhua Liu, Fei Teng, and Yalou Huang
College of Software, Nankai University, Tianjin, P.R.China
{caohao, xuelianlotus, hytjfxk, nktengfei}@mail.nankai.edu.cn
{xiemq,huangyl }@nankai.edu.cn
Abstract. Recently, social tag recommendation has gained more at-
tention in web research, and many approaches were proposed, which
can be classified into two types: rule-based and classification-based ap-
proaches. However, too much expert experience and manual work are
needed in rule-based approaches, and its generalization is limited. Ad-
ditionally, there are some essential barriers in classification-based ap-
proaches, since tag recommendation is transformed into a multi-classes
classification problem, such as tag collection is not fixed. Different from
them, ranking model is more suitable, in which supervised learning can
be used. In additions, the whole tag recommendation task can be divided
into 4 subtasks according to the existence of users and resources. In dif-
ferent subtasks, different features are constructed, in order that existed
information can be used sufficiently. The experimental results show that
the proposed supervised ranking model performs well on the training and
test dataset of RSDC 2008 recovered by ourselves.
1 Introduction
Tag is a new form to index web resources, which help users to categorize and
share the resources, and later search them. Also, the tags assigned by specified
user reveal the user’s interests, therefore, according to the tags user have already
tagged, someone can find other users who have the similar interests, as well as
similar interesting resources. Therefore, it is widely used in social network such
as Bibsonmy, Del.icio.us, Last.fm , etc.
A tag recommendation system can suggest someone a few tags to specified
web resource, thus it can save the user time and effort when them mark up re-
sources. Further, the recommended tags and existing tags can be used to predict
the profile of the user and the interesting to the web resource, for example, to
predict what they like and dislike. The research of tag recommendation is also
very suggestive for other applications, such as online advertisement. In the field
of online advertisement, we can predict what advertisement the browser might
be interested in with the help of the surrounding text and his browsing history.
Recently, social tag recommendation has gained more attention in web re-
search. It has been a hot issue for both industry and research area. For example,
tag recommendation is one of the tasks in ECML RSDC’s 08. Now, in ECML
PKDD 09, tag recommendation has become the exclusive task. However, the
performance of tag recommendation is not good enough to be widely used, more
research work is needed and progress is essential for the practical use of tag rec-
ommendation in commercial system. In this paper, supervise ranking model is
applied to tackle tag recommendation problem, and good result is achieved on
test data.
The rest of paper is organized as follows: Section 2 lists the previous work on
tag recommendation. Section 3 gives a description of supervised ranking model.
Section 4 lists our experiment settings, experiment procedure and our analysis of
the results on recovered 08’s dataset. The model’s performance on 09’s dataset
is presented in Section 5. Section 6 summarizes our work.
2 Previous Work
Much research work has been done for tag recommendation, most of which
can be categorized into two types, one is rule-based, the other is classification-
based.
Rule based approach is used by many researchers. Lipczak [1] proposed a
three-step tag recommendation system in their paper : Basic tags are extracted
from the resource title. In the next step, the set of potential recommendations is
extended by related tags proposed by a lexicon based on co-occurrences of tags
within resource’s posts. Finally, tags are filtered by the user’s personomy - a set
of tags previously used by the user. Tatu, et al. [2]used document and user models
derived from the textual content associated with URLs and publications by social
bookmarking tool users, the textual information includes information present in
a URL’s title, a user’s description of a document, or a bibtex field associated
with a scientific publication, they used natural language understanding approach
for producing tag recommendations, such as extraction of concepts, extraction
of conflated tags which group tags to semantically related groups. However, too
much expert experience and manual work are needed in rule-based approaches,
and its generalization is limited.
Classification-based approach is also used for the tag recommendation task.
Katakis et al. [3] tried to model the automated tag suggestion problem as a
multilabel text classification task. Heymann et al. [4] predicted tags based on
page text, anchor text, surrounding hosts, and other tags applied to the URL.
They found an entropy-based metric which captures the generality of a particu-
lar tag and informs an analysis of how well that tag can be predicted. They also
found that tag-based association rules can produce very high-precision predic-
tions as well as giving deeper understanding into the relationships between tags.
Their results have implications for both the study of tagging systems as poten-
tial information retrieval tools, and for the design of such systems. However ,
the application of classification does not suggest a good solution to the tag pre-
diction problem: first, the tag space is fixed , all the resource can be categorized
to the existed tags only, also, the amount of tags number could be very large,
the traditional classification model would be rather low efficient.
Collaborative filtering is a commonly used technical for user-oriented task.
Many researchers tried collaborative filtering in tag recommendation. Gilad
Mishne [5] used collaborative approach to automated tag assignment for weblog
posts. Robert Jaschke, et al [6] evaluated and compared user-based collaborative
filtering and graph-based recommender, the result shows that both of these two
methods provide better results than non-personalized baseline method, especially
the graph-based recommender outperforms existing methods considerably.
Adriana Budura et al. [7] used neighborhood based tag recommendation,
which make use of content similarity. Principle and simple score approach is used
to select the candidate tags, however, in our paper, machine learning method
is used, a ranking model is learned automatically, then the candidate tags are
ranked and top-ranked tags are suggested as recommending tags.
3 Supervised Ranking Model for Tag Recommendation
3.1 Problem Statement
The tag recommendation problem can be described as follows:
For a given post P whose user is U and resource is R, a set of tags are
suggested as tags for the post. Here we denote post as P, tag as T, resource as
R, user as U.
A possible and most nature way to solve the tag recommendation problem
is as follows: First, a set of candidate tags are selected for the post, and then
tags which are most likely to be the tags for the post are selected as recom-
mending tags. The commonly used approach to choose the tags is rule-based
and classification-based methods, but both of them have defects: rule-based ap-
proach relies on expert experience and manual efforts to set up the rules and
tuning the parameters; classification-based is restrict to the fix of tag space and
is inefficient when it is treated as a multi-label problem. In this paper, tag recom-
mendation is conveyed to a problem of ranking candidate tags. A ranking model
is constructed to ensure tags that are most likely to be post’s tags rank higher
than tags that are not. Supervised learning model is used to construct the rank-
ing model satisfying the restriction. Ranking-SVM model is the most frequently
used supervised ranking model and is proofed to be a successful model, so it is
used as our supervised ranking model in the experiments. All the candidate tags
for one post are grouped as a ranking group and the top-ranked candidate tags
are selected as recommendation tags.
3.2 Introduction to Ranking SVM
Here we briefly describe the Ranking Support Vector Machine(Ranking SVM)
model for tag recommendation.
Assume that X ∈ ℜm is the input feature space which represents feature of
a candidate tag given a user and resource, and m denotes the feature number.
Y = {0, 1} is the output rank space which is represented by the labels, and 1
represents the tag is labeled by user, and 0 is not. (x, y) ∈ X × Y denotes feature
and label as the training instance.
Given a training set with tags T = {t1 , t2 , ..., tn }, for each tag ti there would
be a {x, y} associated with it, the whole training set could be formulate as
S = {xi , yi }N
i=1 , where N represents the number of all tags.
In Ranking SVM [8], ranking model f is a linear function represented by
hw, xi, where w is the weight vector and h·, ·i denotes the inner product. In
RSVM we need to construct a new training set S ′ according to the original
training set S = {xi , yi }N
i=1 . For every yi 6= yj in S, construct (xi − xj , zij ) and
add it into S ′ , where zij = +1 if yi ≻ yj , and otherwise −1. Here ≻ denotes the
preference relationship, for example, y = 1 is preferred to y = 0. For denotation
consistency, we denotes S ′ as {x1i − x2i , zi }D
i=1 . The final model is formalized as
the following Quadratic Programming problem:
1 X D
minw,ξi kwk2 + ξi
2C i=1
(1)
s.t. ξi > 0, zi hw, x1i − x2i i ≥ 1 − ξi
And (1) could be solved using existing Quadratic Programming methods.
Figure 1 is an example of ranking SVM model.
Fig. 1. Example of ranking SVM model
The ranking SVM model convey the problem of ranking into binary classi-
fication problem: for each objects to be ranked, the model compare it with all
other objects in the same ranking group. For n objects, the model compares the
objects C 2n times, and then outputs the ranking result.This is the advantage over
classification model: in classification model, the existence of other candidate tags
is not being considered, but in ranking model, the existence of other candidate
tags is taken into consideration.
3.3 Ranking Process
For any post Pij in test dataset, we denote collection of all candidate tags
for post Pij as CT {Pij } and CTk (k = 1, 2, ..., n) as the k-th candidate tag for
the post Pij , CT {Pij } = {CT1 , CT2 , ..., CTn } . The ranking model ranks the
candidate tags to {CT 1 ′ , CT 2 ′ , ..., CT n ′ } from top to bottom. Then top-k tags
are selected as prediction of the tags of post Pij . Table 1 shows the steps to rank
the candidate tags.
Table 1. Algorithm of rank the candidate tags
Input: candidate tags {CT1 , CT2 , ..., CTn }
Output: top-k tags {CT1′ , CT2′ , ..., CTk′ }
1. Extract feature x = {xi }(i = 1, 2, ..., n) for a sequence
of candidate tags CT {Pij } = {CT1 , CT2 , ..., CTn }.
2. Rank the features using the learned ranking model as
{CT1′ , CT2′ , ..., CTn′ }.
3. select top-k tags {CT1′ , CT2′ , ..., CTk′ } as recommending tags.
Also, the number of recommended tags affects the performance of the system.
For example, if the actual number of tags for post whose content id=123456 is
3, a loss of precision is suffered when 4 tags are recommend to the user. So a
proper number of tags to recommend should be found. The number used in our
experiment is half the number of all candidate tags. If the number is bigger than
5, we cut them into 5, that means we recommend 5 tags at most.
3.4 Training Process
For all the post in the test dataset, candidate tags CT {Pij } for each post Pij
are extracted. Then they are grouped by the post, and features are extracted for
each of them in the post content. For those CTk ∈ T {Pij }, we label them ’1’,
else label them with ’0’. Then we use SVM-light tool to train a ranking-SVM
model. When predicting the tags of the post in test dataset, the model learned
on the training dataset is applied to rank the candidate tags, and top ranked
tags are selected as recommending tags.
4 Experiments on 08’s recovered dataset
4.1 Experiment settings
2008’s dataset recovery In order to compare our experiments’ performance
with that of the 08’s teams, we try to get the 08’s dataset (both training and
test data) and test our model’s performance on the recovered dataset. Though
the 08’s test data can be downloaded from the web, we found that user IDs have
been changed between the datasets. However, the content id field in 08’s test
data is consistent with 09’s data, so we try to recover the 08’s dataset on the
09’s dataset using the content id field and date time field. The 08’s real training
data and test data are subset of 09’s data, so it is possible to recover 08’s data
on 09’s data. After observing 08’s real test data, we found that all posts in 08’s
test data are between Mar. 31, 2008 and May. 15, 2009, so we use the posts
during this period on 09’s training data as recovered 08’s test data and posts
before Mar. 31, 2008 as our recovered 08’s training data. There are still slight
difference between our recovered data and the 08’s real data. We assume that
the difference won’t affect our performance seriously, so the result is comparable
with 08’s results.
Some statistics have been made on our recovered 08’s dataset. Table 2 shows
the statistics of posts on this recovered dataset. Table 3 shows the statistics
of posts according to the existence of their user and resource in the recovered
training data. In following part in section 4, the training data refers to the
recovered training data, the test data refers to the recovered test data.
Table 2. Statistics of posts on recovered 08’s dataset
BOOKMARK 184,655
Post in recovered training data 234,134
BIBTEX 49,479
BOOKMARK 20,647
Post in recovered test data 63,192
BIBTEX 42,545
Table 3. Statistics of posts according to their user and resource status
Users in recovered test data appear in recovered training data 265
Users in recovered test data do not appear in recovered training data 225
Resources in recovered test data appear in recovered training data 1230
Resources in recovered test data do not appear in recovered training data 61970
Data format description The dataset used in experiments is released by
ECML. The data consists of three tables: TAS table, BOOKMARK table and
BIBTEX table.
Table 4 is a description of the fields of the three tables. Only the fields we
used in experiments are listed in the table.
Data preprocess Firstly, the terms are converted into lowercase. Then the
stop words are removed, such as ”a, the, is, an”, these terms are not likely to be
the tags of the post. Finally, the punctuations as ’:’, ’,’, etc are removed. Latex
symbols such as ’{’ and ’}’ is also removed using regular expressions.
Table 5 shows example results of data preprocess.
4.2 Post Division
It can be observed from data distribution that some users of posts exist in
the training data (54%) and some do not exist in the training data (46%). Also
Table 4. Data fields of TAS, BOOKMARK and BIBTEX
Table name Fields name
TAS user, tag, content id, content type, date
BOOKMARK content id (matches tas.content id) ,url
description ,extended ,description ,date ,bibtex
BIBTEX content id (matches tas.content id) ,simhash1 (hash for duplicate detection
among users) ,title
Table 5. Example results of data preprocess
Before data preprocess After data preprocess
Ben Mezrich: the telling of a true ben mezrich telling true story
story
{XQ}uery 1.0: An {XML} Query xquery 1.0 xml query language w3c
Language, {W3C} Working Draft working draft
some resources of posts exist in the training data (2%) and others do not exist
in the training data (98%).
In the analysis above, we divide the posts in test dataset into two categories
according to the existence of their users in the training data: existed user posts,
non-existed user posts. Also, the posts in test dataset can be divided into two
categories according to the existence of their resource in the training data: existed
resource posts, non-existed resource posts.
The posts can be divided into four different categories according to their user
status and resource status in the training data: existed user existed resource post,
existed user non-existed resource post, non-existed user existed resource post,
non-existed user non-existed resource post.
We denote symbols as shown in Table 6 to simplify the language.
Table 7 and Table 8 show statistics after our post division on our recovered
08’s data.
Table 6. Simplified symbols
EUER post Existed user existed resource post
EUNR post Existed user non-existed resource post
NUER post Non-existed user existed resource post
NUNR post Non-existed user non-existed resource post
It can be observed from statistics that not every category of posts occupies
the same ratio of the posts. In BOOKMARK, EUNR posts occupied about
82.80% of all BOOKMARK posts. In BIBTEX, NUNR posts occupied about
93.43% of all BIBTEX posts. In order to promote our model’s performance on
Table 7. Distribution of different categories of BOOKMARK posts in test dataset
Category Posts number ratio
EUER post 621 3.01%
EUNR post 17099 82.80%
NUER post 346 1.68%
NUNR post 2585 12.52%
Table 8. Distribution of different categories of BIBTEX posts in test dataset
Category Posts number ratio
EUER post 164 0.39%
EUNR post 2532 5.95%
NUER post 99 0.23%
NUNR post 39754 93.43%
the test dataset, we should focus on those data which occupy high proportion of
the posts, that is: EUNR posts of BOOKMARK and NUNR posts in BIBTEX.
After data division, the following steps are carried out for our tag recommen-
dation task.
1. Extract candidate tags by different methods according to the category of
post.
2. Rank the candidate tags, and select top ranked tags as recommendation
tags.
4.3 Candidate tags extraction
According to the statistics of the sources of the tags on the dataset, we can
find that tags can be retrieved from three sources mainly: 1.The content infor-
mation of the post, such as ’description’ field in BOOKMARK and ’title’ field
in BIBTEX. 2. T {Rj }: The tags being assigned to the same resource previously.
3.T {Ui}: The tags assigned by the same user previously. Statistics of tags from
different sources for BOOKMARK and BIBTEX posts are listed in Table 9 and
Table 10.
Table 9. Statistics of the tags from 3 sources of BOOKMARK Post
Total tags 56267
Tags from terms of description 5253
Tags from terms of URL 1353
Tags from user’s previous tags 29672
Table 10. Statistics of the tags from 3 sources of BIBTEX Post
Total tags 95782
Tags from terms of title 41801
Tags from terms of URL 547
Tags from user’s previous tags 5377
The four different categories of test dataset have different characters, for
example, we can explore the tags assigned by user previously and the tags as-
signed to the resource previously for EUER posts. But for NUNR posts, we lack
this information. So we should explore different features for the four different
categories of posts individually, in order that existed information can be used
sufficiently. In the following part, while using the supervised ranking model, we
train four models to handle these four categories of posts individually.
The candidate tags extraction strategies for different categories ofSposts:
For EUER post and NUER post, CT {Pij } = { terms in post (Pij ) T {Rj }}.
For EUNR post and NUNR post, CT {Pij } = { terms in post (Pij )}.
We denote the candidate tags for post whose user id=i and resource is j
as CT {Pij }. { terms in post (Pij )} denotes the remaining set of words after
trimming and removing of the stop words in the text information of post Pij .
Notice should be paid here that we do not take T {Ui } (the user’s pervious
tags) as candidate tags because we find the tags are too massive. When they are
added, the precision of the system drops down and the F-1 value on the whole
dataset also declines dramatically. However, in the ranking procedure, we will
use T {Ui } as one of the features in SVM model to rank the candidate tags.
4.4 SVM Features construction
While using SVM, we select features that discern high ranked tags and low
ranked tags well and add the features according to our experience. For example,
the term frequency in the post content: those words which have high term fre-
quency within the post content tend to rank higher than those which have low
term frequency. Also, whether the candidate words have been used as tags for
other post in the training data is an excellent feature.
Table 11 is a brief description of features of ranking SVM model for BOOK-
MARK posts. The features for BIBTEX posts are almost the same except for
the different data fields:
4.5 Analysis of Model
Table 12 and Table 13 show the results of our supervised Ranking SVM
model on the recovered 08’s data.
Combing different types and category of data together, we can get the overall
performance on the recovered 08’s test data, as shown in Table 14.
Table 11. Some of the features for ranking SVM model for BOOKMARK
Feature1 Candidate tag’s TF (term frequency) in post’s description terms.
Feature2 Candidate tag’s TF in post’s URL terms.
Feature3 Candidate tag’s TF in post’s extended description terms.
Feature4 Candidate tag’s TF in T {Rj } (tags assigned to the post of the same URL
in the training data).
Feature5 Candidate tag’s TF in T {Ui } (tags assigned previously by user in the train-
ing data.)
Feature6 Times of candidate tag being assigned as a tag in the training data.
Table 12. Individual and overall Performance on BOOKMARK posts
Post category Recall Precision F1-value ratio
EUER Post 0.369699 0.394973 0.381918 3.01%
EUNR Post 0.046591 0.053739 0.04991 82.80%
NUER Post 0.160883 0.255652 0.197487 1.68%
NUNR Post 0.069158 0.106366 0.083819 12.52%
overall-performance on BOOKMARK 0.061067 0.073997 0.066633
Table 13. Individual and overall Performance on BIBTEX posts
Post category Recall Precision F1-value ratio
EUER Post 0.4219356 0.3472393 0.3809605 0.39%
EUNR Post 0.2250226 0.1628605 0.1889605 5.95%
NUER Post 0.5667162 0.3715986 0.4488706 0.23%
NUNR Post 0.3561221 0.1603686 0.2211494 93.43%
overall-performance on BIBTEX 0.349063 0.161732 0.220381
Table 14. Overall performance on test dataset using ranking SVM model
Recall Precision F1-value
0.153 0.185 0.167
The F1-value is 0.167, less than the F1-value 0.193 of the team ranked first
in 08’s competition.
It can be observed from the results that the performance of the model is poor
on EUNR posts, which occupied most of the BOOKMARK posts. However, the
model performs well on EUER posts. When comparing the two types of data, we
find that the only difference is that the candidate tags of EUER posts are not
only come from the post content but also from the tags of the same resource in
the training data, however, the candidate tags for EUNR posts come from post
content only. In order to overcome the weakness of lacking candidate tags, we
relax restriction on the definition of the same resource. For those posts whose
resources have not appeared in the training data, the role of the same post is
substituted by the similar post. This method is based on the assumption that
users tend to tag the similar posts with the same tags.
We try to use post content similarity to measure the similarity of posts. For
those EUNR posts, which have no same resources in the training data, we add
the tags of those posts whose content similarity with the current post content is
above a certain threshold to the candidate tags set of the post.
4.6 Post content similarity based KNN model
For EUNR post, the candidate tags come from text of the post content only,
that is CT {Pij } = { terms in post (Pij )}. We attribute the poor performance
of the model on such kind of data to the sparse of candidate tags. So we use
content similarity to expand the candidate tags set. For any EUNR post Pij ,
we set a similarity threshold t, and find in the training dataset content Pmn ,
whose sim(text(Pij ), text(pmn ) > t). Then the tags of postSPmn are added to
the candidate tags ofPij : CT {Pij } = { terms in post (Pij )} T {Pmn }.
Post content Pij and Pmn are mapped into vector space:
text(Pij ) = {W1 , W2 , ..., Wn } , text(Pmn ) = {W 1 ′ , W 2 ′ , ..., W n ′ },Then we
use vector space model to calculate the similarity between two posts Pij and
Pmn .
text(Pij ) ∗ text(Pmn )
sim(text(Pij ), text(Pmn )) = (2)
|text(Pij )| ∗ |text(Pmn )|
Wi means the weigh of word i in the content. The simplest way to define Wi
is as following:Wi = 0,word i in post content, Wi 6= 0,word i not in post content.
In our experiment, we define the Wi as TF(Term Frequency) multiply IDF
(Inverted document frequency) :Wi = T Fi ∗ IDFi .We applied open source soft-
ware Lucene to calculate the similarity of two content , the scoring function of
Lucene is a derivation of vector space model formula using TF/IDF weighing
schema.
The modification of threshold value T and the corresponding performance
on EUNR content in BOOKMARK are shown in Figure 2.
It can be observed that the value of recall, precision and F1 value reach
highest when threshold T=0.5. So, in the further experiment settings, we set
threshold value T to 0.5.
Fig. 2. KNN performance on various threshold t on BOOKMARK EUNR posts, k=5
However, we find that the application of content similarity based KNN model
works for BOOKMARK posts but not for BIBTEX posts. After investigation,
we attribute it to the uneven distribution of the dataset in training datasets
and test datasets. In training datasets, the number of BOOKMARK posts is
184,655 and the number of BIBTEX posts is 20,647. But in test dataset, the
number of BOOKMARK post is 20,647 and the number of BIBTEX post is
49,479, it is easy for 20,647 BOOKMARK posts to find similar posts in 184,655
BOOKMARK posts, but difficult for 42,545 BIBTEX posts in only 20,647 posts.
So this method is especially useful for BOOKMARK posts but not for BIBTEX
posts.
After applying content similarity based KNN model on BOOKMARK EUNR
posts, the performance on overall test dataset is as listed in Table 15.
Table 15. Overall performance on test dataset adding content similarity based KNN
model
Recall Precision F1-value
0.323828 0.200926 0.238803
The F1-value is 0.238, higher than the F1-value 0.193 of the team ranked
first in 08s competition.
5 Experiment on 09’s dataset
5.1 Statistics of 09’s dataset
Table 16 and Table 17 show the distribution of different categories of posts
on 09’s dataset after data division according to the existence of their user and
resource in the training data. In our experiment settings on 09’s test data, clean-
dump dataset is used as training dataset in Task 1, Post-core dataset is used as
training dataset in Task 2.
Table 16. Different categories of BOOKMARK posts in 09s test dataset for Task 1
Category Posts number ratio
EUER Post 821 4.86%
EUNR Post 10622 62.86%
NUER Post 872 5.16%
NUNR Post 4583 27.12%
Table 17. Different categories of BIBTEX posts in 09s test dataset for Task 1
Category Posts number ratio
EUER Post 365 1.40%
EUNR Post 9287 35.71%
NUER Post 591 2.27%
NUNR Post 15761 60.61%
It can be observed from the statistics of the distribution of categories in 09’s
test data for Task 1 agrees with the recovered 08’s dataset: EUER posts occupied
most of the BOOKMARK post and NUNR post occupied large proportion of
BIBTEX posts, so we can expect our model a good result on such data. The
whole posts in 09’s test dataset for Task2 can be classified to EUER posts. Since
the good performance of our model on EUER posts, we can also expect a good
result on task 2.
Eight different models are trained on 09’s clean-dump training data and
applied in 09’s test data for Task 1. For Task 2, we apply the BOOKMARK
EUER post model and the BIBTEX EUER post model trained on 09’s post-
core dataset.
5.2 Experiment results on 09’s test dataset
The performance on the whole 09’s test data of both task 1 and task 2 is
shown in Table 18.
Table 18. Performance on 09’s dataset @5
Task No. Submission ID Precision Recall F1-value
1 67797 0.162478 0.146582 0.154121
2 13651 0.31622 0.222065 0.260908
6 Conclusion
In this paper, we briefly describe an approach utilizes supervised ranking
model for tag recommendations. Our tag prediction contains three steps. First,
posts are divided into four categories according to the existence of the user
and the resource in the training data and then candidate tags are extracted for
the different categories with different strategies. Second, features are decided
according to categories. Then we rank the candidate tags, using the supervised
ranking model, and pick the top tags as recommendation tags.
For the existed user non-existed resource post, we use post content similarity
based KNN model to expand the candidate tags set. Performance of this exper-
iment for the corresponding module is promoted after adding this model on 08’s
dataset. Our tag recommendation system is generated from the combination of
these two models and applied to the 09’s tags recommendation task 1 and task
2.
Acknowledgement
Thanks to Zhen Liao for his helpful discussions and suggestions for this paper.
This paper is supported by the National Natural Science Foundation of China
under the grant 60673009 and China National Hanban under the grant 2007-433.
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