Friend Recommendation Algorithm Research Articles

The application of friend recommendation algorithm in the design of traditional handicraft art communication platform

Abstract Cultural awareness is a context in which the integration and development of traditional arts and crafts can be promoted by establishing an exchange platform. In this paper, the exchange platform is designed through the exploration of traditional handicraft art resources and the study of different classifications. The main points of the platform design and key operational processes are explored based on the analysis of the users’ needs for the traditional handicraft art platform. In response to the lack of accuracy of the traditional buddy recommendation algorithm, a buddy recommendation algorithm incorporating user interests was proposed based on big data to carve out different user preferences for traditional arts and crafts. The user interaction response rate of the platform was good, with TPS of 98.7, 137.4 and 75.6 on the sending side, service side and receiving side, respectively, in stable operation. The friend recommendation algorithm incorporating user interests can effectively dig out user groups with similar interests, which is optimized by 11.83% compared to the traditional friend recommendation algorithm. The exchange platform supported by big data, based on friend recommendations to strengthen user interaction, can effectively promote the exchange and integration of traditional handicraft arts.

RETRACTED ARTICLE: E-commerce User Recommendation Algorithm Based on Social Relationship Characteristics and Improved K-Means Algorithm

In the era of the Internet, information data continue to accumulate, and the explosive growth of network information explosion leads to the reduction of the accuracy of users’ access to information. To enhance the user experience and purchasing desire of e-commerce users, a e-commerce user recommendation algorithm based on social relationship characteristics and improved K-means algorithm is proposed. It combines the Automatic Time Division Dynamic Topic Model based on adaptive time slice division for building a strength calculation model in view of the characteristics of social relations. Then, it proposes an e-commerce user recommendation algorithm in view of the improved K-means algorithm to improve the accuracy of topic feature extraction and user recommendation. The experiment illustrates that there is no fluctuation in the clustering function of the improved K-means algorithm, and the highest, lowest, and average accuracy remain consistent under the three datasets, with average accuracy of 78.9%, 84.5%, and 95.9%, respectively. The community discovery-based friend recommendation algorithm presented in the study has the highest accuracy, illustrating that improving the K-means algorithm can further improve recommendation accuracy. The accuracy of the feature extraction method in view of alternative cost is 0.63, which improves the accuracy by about 9%. The results indicate that this study can provide technical support for user recommendations on e-commerce platforms.

Multi-feature fusion friend recommendation algorithm based on complex network

Multi-feature fusion friend recommendation algorithm based on complex network

Multi-feature fusion friend recommendation algorithm based on complex network

Multi-feature fusion friend recommendation algorithm based on complex network

A Potential Friend Recommendation Algorithm for Obtaining Spatial Information

With the rapid development of social network, friend recommendation algorithm has become an important component of social application. Location-based social network (LBSN) enables users to record and share their locations anytime and anywhere, which is a high quality information source. In order to meet people's demand of expanding social circle and obtaining diversified spatial information when making friends, this paper proposes a potential friend recommendation algorithm based on the similarity of user's check-in behavior and spatial information acquisition level in the real world. Firstly, we employ kernel density estimation and time entropy to solve the problems of data sparsity and low concentration, then employ cosine distance to measure the check-in behavior similarity. Secondly, we analyze users’ spatial distribution of check-in location and cognitive differences on spatial information. Finally, the method mentioned above is tested with dataset called Foursquare. The results of the experiment show that the proposed method has competitive performance.

The impact of common neighbor algorithm on individual friend choices and online social networks

Online social platforms have become increasingly more popular for people to make new friends, by relying on the friend recommendation algorithms implemented on social networks. Nevertheless, how these algorithms impact the choice of friends of individual users and the structure of overall social networks remains unknown. In this paper, we introduce a model in which a group of users interact and make friends as recommended by the common neighbor algorithm, which is one of the most commonly used friend recommendation algorithms, to study the impact of the algorithms on the choice of friends of users in social networks. Based on our results, we found that the algorithm is mostly effective in identifying good matches, but users may group themselves into sub-optimal clusters when they over-rely on the algorithms. These results demonstrate the pros and cons of the increasingly more popular common neighbor algorithm applied in social networks. The model is then examined with the attribute similarity matrix obtained from two real datasets, and the results are consistent with our earlier findings. We also investigate the impacts of user reputation on the common neighbor algorithm and found that users with high reputation may become network hubs connected with a majority of users on the platform. Despite the simplicity of our developed model, our results provide interesting insight into the impact of common neighbor algorithm on friend choices of users and the global characteristics of social networks.

Cultural differences in the effects of contextual factors and privacy concerns on users’ privacy decision on social networking sites

ABSTRACT Many social network sites (SNSs) have become available around the world and users’ online social networks increasingly include contacts from different cultures. However, there is lack of investigation into the concrete cultural differences in the effects of contextual factors and privacy concerns on users’ privacy decisions on social network sites (SNSs). The goal of this paper is to understand how contextual factors and privacy concerns cast different impact on privacy decisions, such as friend request decisions, information disclosure and perceived risk, in different countries. We performed a quantitative study through a large-scale online survey across the US, Korea and China to model the relationships between contextual factors, privacy concerns and privacy decisions. We find that the contextual influence and focus of privacy concerns vary between the individualistic and collectivistic countries in our sample. We suggest that multinational SNS service providers should consider different contextual factors and focus of privacy concerns in different countries and customise privacy designs and friend recommendation algorithms in SNSs in different countries.

Friend Recommendation Method of Weighted Networks Based on Value and Match

To improve the accuracy of the recommendation in the knowledge social network, the paper proposes a friend recommendation algorithm based on user information to measure the value and the match between the users. On the one hand, it establishes the directed graph model of the value by ‘the number of answers’ and ‘the number of likes’, to calculate the users’ value scores. And it uses the material diffusion principle to predict the value scores which are zero in the model. On the other hand, it calculates the users’ similarity through ‘topic of concern’ and ‘industry’ simulates the similarity of user pairs by using probability distribution and establishes likelihood function. After using ‘EM’ algorithm to estimate the function parameters, it gets the users' matched and mismatched probability to calculate their match scores and establish undirected graph model. Finally, it builds the comprehensive score formula between the value and the match. Combined with the directed and undirected graph model, the Top-N friend recommendation algorithm is recommended. Through the experiments, it shows that the method is better than the recommendation algorithm based on collaborative filtering in Precision, Recall and F1-measure on a large scale recommendation.

Friend recommendation in social networks based on multi-source information fusion

Friend recommendation (FR) in social networks has been widely studied in recent years, which mainly focuses on social relationships and user interests. Friend of Friend method is one representative. However, the disadvantage is that most of existing solutions ignored other valuable information, such as user profile, location, influence and indirect trust. In fact, being friends among users is either determined by one or two dominant factors that originate from varying information sources, or the results of multiple main factors gaming. Motivated by the observations above, we propose a scalable FR framework in social networks, where multiple sources have been integrated based on improved D-S evidence theory. More specifically, we first analyzed 7 valuable information sources and categorized them into three classes, including Personal Features, Network Structure Features and Social Features. Furthermore, we also propose a fusion recommendation framework based on D-S evidence theory which embodies the minimal conflicts among evidences. In the proposed method, we first optimize the framework by importance degree and reliability of evidence based on original D-S evidence theory. Then, we designed a novel BPA evidence function by quantifying the evidence, where each evidence measures the relevance of forming friends among users. Finally, we describe the fusion FR algorithm plugged into our recommendation framework. The experiments on real-world dataset show that our proposed approach outperforms the other state-of-the-art algorithms on five evaluation metrics. The experimental results demonstrate the effectiveness of fusing multi-source information for FR in social networks.

Friend suggestion in social network based on user log

Simple friend recommendation algorithms such as similarity, popularity and social aspects is the basic requirement to be explored to methodically form high-performance social friend recommendation. Suggestion of friends is followed. No tags of character were followed. In the proposed system, we use an algorithm for network correlation-based social friend recommendation (NC-based SFR).It includes user activities like where one lives and works. A new friend recommendation method, based on network correlation, by considering the effect of different social roles. To model the correlation between different networks, we develop a method that aligns these networks through important feature selection. We consider by preserving the network structure for a more better recommendations so that it significantly improves the accuracy for better friend-recommendation.

Friend Recommendation Algorithm Based on Fragmentation of Time and Transmission of Interest

Friend Recommendation Algorithm Based on Fragmentation of Time and Transmission of Interest

Social Friend Recommendation Based on Multiple Network Correlation

Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, most of these websites use simple friend recommendation algorithms such as similarity, popularity, or “friend's friends are friends,” which are intuitive but consider few of the characteristics of the social network. In this paper we investigate the structure of social networks and develop an algorithm for network correlation-based social friend recommendation (NC-based SFR). To accomplish this goal, we correlate different “social role” networks, find their relationships and make friend recommendations. NC-based SFR is characterized by two key components: 1) related networks are aligned by selecting important features from each network, and 2) the network structure should be maximally preserved before and after network alignment. After important feature selection has been made, we recommend friends based on these features. We conduct experiments on the Flickr network, which contains more than ten thousand nodes and over 30 thousand tags covering half a million photos, to show that the proposed algorithm recommends friends more precisely than reference methods.

Proposing a new friend recommendation method, FRUTAI, to enhance social media providers' performance

Social media, such as Facebook and Twitter, have grown rapidly in recent years. Friend recommendation systems, as an important emerging component of social media, may efficiently expand social media networks by proactively recommending new and potentially high-quality friends to users. Literature review has shown that prior research work on friend recommendation mainly focuses on the linking relation between users in social media but largely neglects the influence of users' attributes. In this study, we have systematically reviewed and evaluated the existing state-of-the-art friend recommendation algorithms. We introduce a new Friend Recommendation system using a User's Total Attributes Information (FRUTAI) based on the law of total probability. The proposed method can be easily extended according to the increasing number of a user's attributes with low computation cost. Furthermore, the FRUTAI is a universal friend recommendation method and can be applied in different types of social media because it does not distinguish the structure of the network. We have collected 7 million users' public information and their friend relationships from RenRen, commonly regarded as the Facebook of China. Using the real-world data from a dominant social media provider, we extensively evaluate the proposed method with other existing friend recommendation algorithms. Our experimental results have demonstrated the comparatively better performance of FRUTAI. In our empirical studies, we have observed that the performance of FRUTAI is related to the number of a user's friends. In particular, when a user has a small number of friends, the proposed FRUTAI algorithm performs better than other algorithms; when a user has a large number of friends, the overall performance of FRUTAI becomes less competitive but is still comparable to those of other providers, and its precision rate is quite outstanding. Our findings may provide some important practical implications to social media design and performance.

Friend recommendation with content spread enhancement in social networks

Social network is becoming an increasingly popular media for information sharing. More and more people are interacting with others via major social network sites such as Twitter and Flickr. An important aspect of a social network is its capability in efficiently spreading content, not only within a small circle but also in the whole network. However, most existing methods for recommending friends in social networks only aim at achieving high recommendation success rate. The network grown from such recommendations is not optimized for content spread. In this paper, we propose a novel friend recommendation method ACR-FoF (algebraic connectivity regularized friends-of-friends) that considers both success rate and content spread in the network. Using the algebraic connectivity of a connected network to estimate its capability for spreading contents, our recommendation method naturally extends existing friend recommendation algorithms such as FoF to achieve both recommendation relevance and content spread in a social network. Experimental results on simulated and real social network data sets show that our method can significantly improve content spread in a social network with only a very tiny compromise on friend recommendation success rate.

Social Connections in User-Generated Content Video Systems: Analysis and Recommendation

User-generated content (UGC) video systems by definition heavily depend on the input of their community of users and their social interactions for video diffusion and opinion sharing. Nevertheless, we show in this paper, through measurement and analysis of YouKu, the most popular UGC video system in China, that the social connectivity of its users is very low. These observations are consistent with what was reported about YouTube in previous works. As a UGC system can achieve a larger audience through improved connectivity, our findings motivate us to propose a mean to enhance the users' connectivity by taking benefit of friend recommendation. To this end, we assess two similarity metrics based on users' interests that are derived from their uploads and favorites tagging of videos, to evaluate the interest similarity between friends. The results consistently show that friends share to a great extent common interests. Two friend recommendation algorithms are then proposed. The algorithms use public information provided by users to suggest potential friends with similar interests as measured by the similarity metrics. Experiments on our gathered YouKu dataset demonstrate that the social connectivity can be greatly enhanced by our friend proposition set and that users can access a larger set of interesting videos through the recommendations.

Friend Recommendation Algorithm Based on Mixed Graph in Online Social Networks

Friend Recommendation Algorithm Based on Mixed Graph in Online Social Networks