S. Chaudhury

Bio: S. Chaudhury is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Content management. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Predicting User-to-content Links in Flickr Groups

Abstract: The last few years have seen an exponential increase in the amount of multimedia content that is available online thanks to collaborative-online communities such as Flickr, You Tube etc. As opposed to "pure" social networking services these collaborative-online communities not only allow users to create new social links (e.g. add other users to their friend or contact list) but also allow users to contribute multimedia content and engage in content-driven interactions (called user-to-content interactions). A good example of this can be seen in Flickr, in general and Flickr Group in particular where users can comment on or "like" an image contributed by another user. This paper looks at the task of predicting the formation of such user-to-content links in Flickr Groups. More specifically, "what is the chance that a user will comment/like an image contributed by another user?". Our proposed method for predicting user-to-content links takes into account both community effect and content effect. Our results on real-world Flickr Group data reveals that the proposed method shows good performance for the user-to-content link prediction task.

Interactive Mining of Strong Friends from Social Networks and Its Applications in E-Commerce

Abstract: Social networks are generally made of individuals who are linked by some types of interdependencies such as friendship. Most individuals in social networks have many linkages in terms of friends, connections, and/or followers. Among these linkages, some of them are stronger than others. For instance, some friends may be acquaintances of an individual, whereas others may be friends who care about him or her (e.g., who frequently post on his or her wall). In this study, we integrate data mining with social computing to form a social network mining algorithm, which helps the individual distinguish these strong friends from a large number of friends in a specific portion of the social networks in which he or she is interested. Moreover, our mining algorithm allows the individual to interactively change his or her mining parameters. Furthermore, we discuss applications of our social mining algorithm to organizational computing and e-commerce

Predicting the number of comments on facebook posts using an ensemble regression model

Abstract: The nature and importance of user’s comments in various social media systems play an important role in creating or changing people's perceptions of certain topics or popularizing them. It has now an important place in various fields, including education, sales, prediction, and so on. In this paper, Facebook social network has been considered as a case study. The purpose of this study is to predict the volume of Facebook users' comments on the published content called post. Therefore, the existing problem is classified as a regression problem. In the method presented in this paper, three regression models called elastic network, M5P model, and radial basis function regression model are combined and an ensemble model is made to predict the volume of comments. In order to combine these base models, a strategy called stack generalization is used, based on which the output of the base models is provided to a linear regression model as new features. This linear regression model combines the outputs of the 3 base models and determines the final output of the system. To evaluate the performance of the proposed model, a database of the UCI dataset, which has 5 training sets and 10 test sets, has been used. Each test set in this database has 100 records. In the present study, the efficiency of the base models and the proposed ensemble model is evaluated on all these sets. Finally, it is concluded that the use of the ensemble model can reduce the average correlation coefficient (as one of the evaluation criteria of the model) to 74.4 ± 16.4, which is an acceptable result.

Comment Volume Prediction using Regression

Abstract: The latest decade lead to a unconstrained advancement of the importance of online networking. Due to the gigantic measures of records appearing in web organizing, there is a colossal necessity for the programmed examination of such records. Online networking customer's comments expect a basic part in building or changing the one's acknowledgments concerning some specific indicate or making it standard. This paper demonstrates a preliminary work to exhibit the sufficiency of machine learning prescient calculations on the remarks of most well known long range informal communication site, Facebook. We showed the customer remark patters, over the posts on Facebook Pages and expected that what number of remarks a post is depended upon to get in next H hrs. To automate the technique, we developed an item display containing the crawler, information processor and data disclosure module. For prediction, we used the Linear Regression model (Simple Linear model, Linear relapse model and Pace relapse model) and Non-Linear Regression model(Decision tree, MLP) on different data set varieties and evaluated them under the appraisal estimations Hits@10, AUC@10, Processing Time and Mean Absolute Error.