In social tagging systems, users are allowed to label resources with tags, and thus the system builds a personalized tag vocabulary for every user based on their distinct preferences. In order to make the best of the personalized characteristic of users’ tagging behavior, firstly the transfer matrix is used in this paper, and the tag distributions of query resources are mapped to users’ query before the recommendation. Meanwhile, we find that only considering the user’s preference model, the method cannot recommend new tags for users. So we utilize the thought of collaborative filtering, and produce the recommend tags based on the query user and his/her nearest neighbors' preference models. The experiments conducted on the Delicious corpus show that our method combining transfer matrix with collaborative filtering produces better recommendation results.
With the rapid growth of the web and social networks, more and more people like to share their information on the Internet, including pictures, videos and even political opinions. This leads to the flourish of the web data, and increases difficulties for people to organize and search resources they want. In order to address this challenge, social tagging has attracted more attentions. Delicious, Bibsonomy, Flickr, and Last.fm are all the popular social tagging online services recently.
Tagging allows users to label content by assigning freely chosen keywords (tags). In such open environment, tagging has proven a superior alternative to traditional categorization techniques due to its flexibility that enable users to choose labels that match their real tastes. And the increase of the descriptive keywords on resources is convenient for people to search, especially in the case of multimedia. Different online tagging service distinguishes in the resource type, but they are similar in essence, such as Delicious supports the tagging on webpages, Bibsonomy allows to tag webpages and journal papers, and people can tag pictures and the music in Flickr and Last.fm, respectively [
Tag recommenders support a user during the posting process by suggesting potentially relevant tags. Because of the stability of a user’s tag vocabulary, it is effective to predict the future tagging behavior of a user according to the past. Previous works have showed that the description of the same object is different due to users’ customs and knowledge. So in the social tagging system, each user can generate a set of tags with their own preferences, which is called personalized tag vocabulary. At the same time, tags on the resources are based on the whole tagging system and called folksonomies. The tagging system builds preference models of users and tag models of resources by analyzing users’ tagging history, and generates the correlations among tags, users and resources; then recommends the top n relevant tags to users. Depending on whether consider users’ preference models or not, tag recommendation comes in two forms, personalized and non-personalized. For a resource, personalized tag recommender systems recommend different tags for different users in contrast to non-personalized tag recommenders. And for a good tag recommender system, personalized recommend should be given preference.
Wetzker et al. [
In order to solve this limitation, based on the transfer tags, we integrate the collaborative filtering technology and generate a new algorithm. First, we build the personalized transfer matrix for each user; the element in the matrix represents transfer values between personalized tags and folksonomies. Then, we determine the nearest neighbors of a user by computing their similarities. Finally, we recommend tags by considering the user and his/her nearest neighbors’ preference models and their similarities together. In addition, for similarities between users, we propose a new method based on the personalized transfer matrix.
The remainder of this paper is structured as follows. We begin with a discussion of related work in Section 2. Section 3 describes our method in detail. And then analyze the experiments in Section 4. Section 5 concludes the work of the paper.
Recently, the social tagging system has attracted a lot of attention, and the recommender system based on the social tagging also obtains much more focus.
There are many classical recommendation algorithms. Collaborative filtering based method is traditional and will be described in the next subsection. Resource content based method focuses on the texts or webpages and mainly depends on the key words extraction technology to select the tags. Zhang et al. [
Symeonidis et al. [
Folk Rank algorithm is inspired by the seminal Page Rank algorithm [
And also there are many recommender systems consist of different methods. Zhang et al. [
Collaborative filtering is a widely used and effective recommendation technology. It gets favored by many commercial websites, such as Amazon.com. The underlying assumption of the approach is that users would have similar behavior in the future if they performed similar in the past. In the recommender systems, collaborative filtering comes in two methods, user-based and item-based. The user-based algorithm recommends a user what his/her nearest neighbors interest in. The item-based algorithm recommends a user an item by judging whether the user interests the nearest neighbors of the item.
There are many researches about applying collaborative filtering in the recommender system [
In the social tagging system, different tags are called co-occurrence if they appear in one resource. The paper first makes use of this co-occurrence among tags to build mappings between personalized user tags and folksonomies on resources, and identify the nearest neighbor set N of the user by integrating collaborative filtering technology. Then in the tag recommendation stage, we can map the tag distribution of the query resource to the distributions of tags that the query user and his/her neighbors have used.
The arrangement of the part is as follows. Subsection 3.1 gives the description of the problem. We introduce the build of the personalized transfer matrix and the identification of the nearest neighbors in subsection 3.2. Subsection 3.3 describes the method to recommend tags based on the user and nearest neighbors.
A social tagging system includes users, resources, and tags, and the tagging behavior of a user reflects the relationship among them. A folksonomy is a tuple F: = (U, T, R, A) where U, T, and R are finite sets, whose elements are called users, tags, and resources, respectively, and A is a ternary relation among them, i.e.
For the given user and the resource (u, r), we need to compute correlations between a tag and u and r. The higher correlation represents the higher possibility that the user u assigns this tag to the resource r. Then we can recommend the highest tags to the user.
The relations between resources and tags are represented as a
where xrt is times that the tag t assigned to the resource r, and
The relation between users’ personalized tags and folksonomies is built through resources. If any two tags co-occurrence on many resources, they will have a higher relation. So we set up a mapping between users’ personalized tags and folksonomies based on the co-occurrence. For a user u,
where element
If
where
It needs to identify the nearest neighbor set after building the personalized transfer matrix. Here we use the cosine to measure the similarity, and choose the top K as the nearest neighbors of the query user. According to Marinho [
In addition, we also propose a method to compute the similarity between users based on the personalized transfer matrix. If any two transfer matrixes of users have a high similarity, we can believe that the two users are
Resource Tags User Tags | t1 | …… | tk | …… | t|T| |
---|---|---|---|---|---|
tu1 | 0.242 | 0.135 | 0.335 | ||
…… | |||||
tuk | 0.521 | 0.362 | 0.01 | ||
…… | |||||
0.03 | 0.321 | 0.201 |
similar. If both the personalized tag vocabularies of user u and v include the tag ti, we can define the user cosine similarity based on the tag ti as follows:
where
For the given query user and the resource (u, r), we combine the thought of collaborative filtering with personalized transfer matrix when recommend tags. This can ensure both the personalization and the generation of new tags. For the query user u, we first get his/her nearest neighbor set N(u) by Equation (11), then mapping the tag distribution of the resource r to the tag vocabulary of user u and N(u) by the transfer matrix. Let
where N(u) is the nearest neighbor set of the user u, it includes the user u itself. Let
After the transfer through the Equation (12), we can obtain the weight distribution of tags used by the user u and N(u). The tag vocabulary would be empty if there is no co-occurrence between tags on the resource r and the user u and N(u). In order to solve this problem, and increase the weight of tags that better represent the resource, we compute the final tag weight through Equation (14), and recommend the top n tags to the query user.
where w(r, t) is the weight of tag t on the resource r. Parameter
In order to prove the efficiency of our method, we conduct experiments on the Delicious dataset. Delicious is probably the best researched folksonomy to date [
We use two standard measures from information retrieval to measure the recommendation effectiveness: precision and recall. Meanwhile, we test our method by F1 value, which is defined as the harmonic mean of precision
Dataset | p | Users | Tags | Resources | TAS | Bookmarks |
---|---|---|---|---|---|---|
Delicious (200309-200401) | 1 | 3825 | 21,106 | 79,883 | 246,254 | 104,187 |
5 | 397 | 478 | 805 | 13,361 | 6820 |
and recall.
where test refers to the test set, N is the number of the (u, r) in the test set.
In this paper, we conduct the following experiments using different methods on the same dataset.
1) The traditional collaborative filtering method, denoted as colla. Here we adopt the algorithm described by Marinho [
If
2) The transfer tensor of the user u based personalized tag recommendation, denoted as tensor u. This method is proposed by Wetzker [
3) The method based on the transfer matrix and collaborative filtering technology, which proposed in this paper and denoted as trans + colla. It takes both the preference models of the query user u and his/her nearest neighbors into consideration.
The similarities between users in method (1) and (3) are calculated using three methods referred in subsection 3.3. There are denoted as DV which based on the resources, TV which based on the tags, and Trans V which based on the personalized transfer matrix, respectively.
The experiments are conducted on 5-core dataset in
There are two parameters need to be addressed in the three recommend methods. One is the number of the nearest neighbors K, the other is the
We conduct the experiments with the above best parameter values, and the evaluations are shown in
the Recall will increase if we recommend more tags. But on the other hand, the incorrect recommend tags will also increase, and this leads to the decrease of the Precision in
As shown in
In addition, we can find that the results of method trans + colla_* are similar. The similarity calculated by Equation (12) and Equation (13) is not the best. Because there are many users who share the same personalized tags with the query user due to the sparse dataset, and this lead to the higher similarity of transfer vectors between the query user and his/her nearest neighbor set. So due to the noise, it would produce some uninterested tags with higher tag weight, and finally influence the recommend results.
Personalized tag recommendation can be better solved by transfer tensor, but this method limits to those used tags, and cannot find new interesting tags for users. So this paper integrates collaborative filtering technology to solve this problem. Firstly, we build the personalized transfer matrix for each user based on the co-occurrence of tags. The element of the matrix represents the mapping values between personalized tags and folksonomies. Secondly, combining the collaborative filtering approach, when recommend tags, we not only consider the preference model of query users, but also his/her nearest neighbors’ and similarities between users. Aiming at the similarity, we also propose a method based on the personalized transfer matrix.
Further, we plan to focus on the problem of solving the redundant and ambiguous tags in the tagging system by applying the semantic models in the recommender system.
This work is partially supported by grant from the Natural Science Foundation of China (No. 61277370), Natural Science Foundation of Liaoning Province, China (No. 201202031, 2014020003), State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002).
ShaowuZhang,YanyanGe,11, (2015) Personalized Tag Recommendation Based on Transfer Matrix and Collaborative Filtering. Journal of Computer and Communications,03,9-17. doi: 10.4236/jcc.2015.39002