Cosine similarity-based algorithm for social networking recommendation

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities

An Efficient and Improved Algorithm for a Recommender System to Detect & Recognize Communities in Social Networks

Social Network is a communicative platform which is a part of social media, useful for interaction of information among people i.e. users. There will be millions of users over online Social Networks, they might or might not have similar interests. People with similar interests / mindset would like to have friendly relationship among themselves. Connections with many similar mindset people forms groups or communities. These Communities will be helpful for gaining knowledge/information transmission. In this paper, we will observe efficient methods for recommending groups or communities to users based on their similarities with their friend's or user’s similar to them and groups followed by their friend's, using Hybrid Recommendation Filtering System combined with Singular Value Decomposition

Detection of Trending Topic Communities: Bridging Content Creators and Distributors

The rise of a trending topic on Twitter or Facebook leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users, being able to dynamically identify communities of users related to this trending topic would allow for a rapid spread of information. Indeed, individual users inside a community might receive recommendations of content generated by the other users, or the community as a whole could receive group recommendations, with new content related to that trending topic. In this paper, we tackle this challenge, by identifying coherent topic-dependent user groups, linking those who generate the content (creators) and those who spread this content, e.g., by retweeting/reposting it (distributors). This is a novel problem on group-to-group interactions in the context of recommender systems. Analysis on real-world Twitter data compare our proposal with a baseline approach that considers the retweeting activity, and validate it with standard metrics. Results show the effectiveness of our approach to identify communities interested in a topic where each includes content creators and content distributors, facilitating users' interactions and the spread of new information.Comment: 9 pages, 4 figures, 2 tables, Hypertext 2017 conferenc

Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com