The development of recommendation system comes with the research of data sparsity, cold start, scalability, and privacy protection problems. Even though many papers proposed different improved recommendation algorithms to solve those problems, there is still plenty of room for improvement. In the complex social network, we can take full advantage of dynamic information such as user’s hobby, social relationship, and historical log to improve the performance of recommendation system. In this paper, we proposed a new recommendation algorithm which is based on social user’s dynamic information to solve the cold start problem of traditional collaborative filtering algorithm and also considered the dynamic factors. The algorithm takes user’s response information, dynamic interest, and the classic similar measurement of collaborative filtering algorithm into account. Then, we compared the new proposed recommendation algorithm with the traditional user based collaborative filtering algorithm and also presented some of the findings from experiment. The results of experiment demonstrate that the new proposed algorithm has a better recommended performance than the collaborative filtering algorithm in cold start scenario.
A social network site, such as Facebook, Twitter, and Sina Weibo, has become an indispensable part of Internet users online life. It is also an important way of user information sharing and obtaining. However, with the number of social network users going into explosive growth, the information generated by the user also increases numerously. Therefore, when the user’s ability to process information cannot keep up with the speed of the network information explosion, the user will have the problem of information overload [
In the traditional personalized recommendation algorithm, collaborative filtering algorithm [
In this paper, we proposed the recommendation algorithm based on user’s dynamic information in complex social network to address the above problems. By considering the dynamic information of user’s response information and time factor to reflect the user’s dynamic preference feature, we proposed an improved recommendation algorithm, combined with a new similarity measurement. Then the experiments show that the new proposed algorithm has better recommendation performance than the traditional collaborative filtering recommendation algorithm.
The collaborative filtering recommendation algorithm is one of the most successful recommendation algorithms. The core idea of it can be divided into three parts: first, to calculate the similarity between users from the user’s historical interest information; then, to select the
The key point of the collaborative filtering recommendation is the measurement of similarity between different users. The widely adopted method is based on the similarity calculation of users’ common historical ratings data. Among the similarity calculation methods, although each one has its own advantages and disadvantages, the most common method [
Once we get the set of nearest neighbor users [
For the context of social user and user’s dynamic interest pattern, the traditional collaborative filtering recommendation algorithm did not take them into account. And the already proposed method defined the time weight function
In the social network, the most common way to construct the relationship of users is the graph construction
In social network, we can regard the behavior of information forwarding, collection, and other actions as the positive response type. So when the user
Similarly, if user did not show any interest in the item or even take actions such as shielding, cancelling the attention, and so on we can regard those actions as the negative response information. So when user
Based on the above response type definition, at the same time, considering the time effect of user interest preferences and the drawback of traditional collaborative filtering algorithm to this aspect, this paper put forward the new type of user similarity measurement.
We defined the user similarity by considering the user response information and the benefits of only considering the user’s response information without paying too much attention to the content of the response information are that it can ensure the diversity of the recommendation results compared with the traditional collaborative filtering recommendation algorithm [
Similarly, we can get the user similarity measurement based on user negative response from this formula:
To take the dynamic interest and response information of social user into consideration, we design a decreasing time function to model the user’s dynamic interest feature in social network sites and then combine it with the new proposed similarity measurement. The
Then, we will use the regulatory factor
Finally, we try to combine the similarity measurement based on social user response with the similarity calculation process of traditional collaborative filtering recommendation algorithm. On the one hand, in the early stage of the new register user, the traditional recommendation system cannot give a good recommendation result, due to the fact that no available user historical data can be used. But once the recommendation got enough user preference historical data, the performance of it will be much better. But it takes time to collect user useful time.
By combination with the user’s response information, we can alleviate the cold start problem of recommendation system. It collects the response information more quickly by just some user clicks and gives the preliminary recommendation to user. On the other hand, it can also guarantee a certain diversity of the recommendation result by only focusing on the amount of response information not the content. The formalized user Pearson similarity method can be defined as
After calculating the user similarity, the highest
In the process of recommendation algorithm, the algorithm involves a big part of user similarity computation. In particular when faced with the large data level of processing historical data, the performance of recommendation algorithm becomes extremely important. Here, we will analyze the time complexity of new proposed recommendation algorithm. The user similarity computation mainly involves two parts: one is the traditional similarity measure like Pearson similarity or Cosine similarity and other one is the similarity measure based on social user’s dynamic response information. The collaborative filtering algorithm mainly includes user similarity calculation and forecasts the target user’s rating scores.
According to Algorithms
users similarity according to formula ( ++
Set
the item
Step 4;
negative response formula (
to item
In order to validate the fact that the new proposed recommendation algorithm has better performance than the traditional user based collaborative filtering recommendation algorithm, we have collected the data of domestic mainstream social network site called Sina Weibo to complete the relevant experiments.
Since the grabbed data from the original Sina Weibo site has a lot of redundant information, therefore, it needs to extract and transform the raw data, commonly known as the process of ETL, eliminate the irrelevant information, and get the exact information we need. The data has about 6040 Sina users with about 3682 pieces of Weibo information, and 100 thousand response information logs. Considering the scenario in Sina Weibo, the negative response information of user is very difficult to define. The situation of without the user’s browsing, no forwarding, no comments, and so forth often denotes the user has no interest in this information. So consider that the new similarity measurement regulatory factor
For the existing data set, the data set is equally divided into 10 subsets by way of random selection, of which nine were randomly selected as the training set and the remaining one was selected as the test set. Due to the presence of the decreasing function parameter
The impact of the user interest decay rate
We can draw a conclusion that the improved algorithm has the best performance from Figure
Once we determined the parameter
Comparative experiment results of two algorithms.
From Figure
In this paper, the theory of collaborative filtering recommendation algorithm is briefly introduced. The collaborative filtering recommendation algorithm is a kind of widely used and more mature algorithms and has a good recommended effect of recommendation algorithm. However, the collaborative filtering recommendation algorithm is a flawed one, in some aspects. For example, the traditional collaborative filtering algorithm did not take into account the temporal characteristics of user’s interest while computing the similarity, so it will lose a part of the recommendation accuracy and diversity. Meanwhile, with the rise of social network, the social network user surged, so the users are faced with the problem of information overload on social network sites. But the social user contains rich contextual information; therefore, this paper takes the dynamic information of social user into account to propose the improved algorithm to alleviate the problem and validates the fact that the new improved algorithm has the improvement of recommended effect by contrast experiment. However, the social network user’s dynamic information not just only the response information and time factor; there are also geographical information, social relationship information, and other context information. We will continually optimize the current deficiency of the proposed algorithm and also try to do the work of how to better model social user’s dynamic information, dig deeper into the user’s behavior patterns, and combine them into recommendation algorithm to improve the recommendation algorithm performance in our next step of research.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported in part by NSFC Grant nos. 61472284, 61472004, and 61202384, by National 863 Programs Grant no. 2012AA062800, by Natural Science Foundation Programs of Shanghai Grant no. 13ZR1443100, and by ISTCP Grant no. 2013FM10100.