Referral based expertise search system in a time evolving social network

To solve some difficult problems that requires procedural knowledge, people often seek the advice of experts who have got competence in that problem domain. This paper focuses on locating and determining an expert in a particular knowledge domain. In most cases, social network of a user is explored through referrals to locate human experts. Past work in searching for experts through referrals focused primarily on static social network. However, static social network fail to accurately represent the set of experts, as in a knowledge domain as time evolves experts continuously keep changing. This paper focuses on the problem of finding experts through referrals in a time evolving co-author social network. Authors and co-authors of research publication for instance are domain experts. In this paper, we propose a solution where the network is expanded incrementally and the information on domain experts is suitably modified. This will avoid periodic global expertise recomputation and would help to effectively retrieve relevant information on domain experts. A novel data structure is also introduced in our study to effectively track the change in expertise of an author with time. © 2010 ACM

Expert Finding Using Social Networking

In today’s world, knowledge transfer is considered an important and essential activity for the success of an enterprise. Large corporations have realized the need to reuse existing knowledge rather than spend time and effort on solving the same problems again. For these reasons, most corporations now have knowledge repositories. These repositories are visited for possible solutions whenever there is a problem that cannot be easily resolved by using the expertise of the existing team. Apart from this, the problems faced by the people in the company can also be resolved by asking for help from expert in that problem domain. This approach proves to be more efficient in terms of time and manual efforts, while also saving resources. This report proposes a strategy of finding an expert in a required domain by analyzing a company’s social network i.e. communication amongst its employees. Efficiently finding an expert is one of the most important tasks currently faced by the information industry and this problem has not been sufficiently addressed in the past. Conducting further research in this field can improve the time required to solve critical time-sensitive problems in an enterprise environment, thereby improving the overall efficiency of the enterpris

JNET: Learning User Representations via Joint Network Embedding and Topic Embedding

User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users' social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow

Expert finding social network for informal and semi-formal experts

Applied project submitted to the Department of Computer Science, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2017Patatte is a simple social network that helps people to find and collaborate with informal and semi-formal experts. This project’s aim is to solve the ‘expert finding+ problem’ which is the problem of finding an unknown expert (for any purpose including hiring, working with or questioning the expert) by searching through real world or online social networks with skill or interest keywords. The system is designed to be simple and intuitive to use. This is because some of the targeted users (informal and semi-formal experts) are basically educated and may have limited experience with the usage of web applications and social networks. The system is also accessible by the most basic internet user requiring a computer, internet access and a basic knowledge of how to use social networks. Users can share projects they have worked on, follow other users, view those users’ projects, like some of those projects, search for users with specific skills and collaborate with them in an integrated media sharing and chat application.Ashesi University Colleg

Coauthor prediction for junior researchers

Research collaboration can bring in different perspectives and generate more productive results. However, finding an appropriate collaborator can be difficult due to the lacking of sufficient information. Link prediction is a related technique for collaborator discovery; but its focus has been mostly on the core authors who have relatively more publications. We argue that junior researchers actually need more help in finding collaborators. Thus, in this paper, we focus on coauthor prediction for junior researchers. Most of the previous works on coauthor prediction considered global network feature and local network feature separately, or tried to combine local network feature and content feature. But we found a significant improvement by simply combing local network feature and global network feature. We further developed a regularization based approach to incorporate multiple features simultaneously. Experimental results demonstrated that this approach outperformed the simple linear combination of multiple features. We further showed that content features, which were proved to be useful in link prediction, can be easily integrated into our regularization approach. © 2013 Springer-Verlag

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