Rajhans Mishra

A web recommendation system considering sequential information

Abstract With the rapid growth of information technology, the current era is witnessing an exponential increase in the generation and collection of web data. Projecting the right information to the right person is becoming more difficult day by day, which in turn adds complexity to the decision making process. Recommendation systems are intelligent systems that address this issue. They are widely used in e-commerce websites to recommend products to users. Most of the popular recommendation systems consider only the content information of users and ignore sequential information. Sequential information also provides useful insights about the behavior of users. We have developed a novel system that considers sequential information present in web navigation patterns, along with content information. We also consider soft clusters during clustering, which helps in capturing the multiple interests of users. The proposed system has utilized similarity upper approximation and singular value decomposition (SVD) for the generation of recommendations for users. We tested our approach on three datasets, the MSNBC benchmark dataset, simulated dataset and CTI dataset. We compared our approach with the first order Markov model as well as random prediction model. The results validate the viability of our approach.

Clustering Web Logs Using Similarity Upper Approximation with Different Similarity Measures

In this paper we adopted the similarity upper approximation based clustering of web logs using various similarity/distance metrics. The paper shows the viability of our methodology. Web logs capture the information about web sites as well the sequence of the visit. Sequence of visit provides an important insight about the behavior of the user. Rough set, a soft computing technique, deals with vagueness present in data. It captures the indiscernibility at different levels of granularity. The paper has shown the results on msnbc data set with different similarity measures along with explanation of results. Index Terms—Clustering, sequential data, similarity upper approximation.

An alternative approach for clustering web user sessions considering sequential information

Clustering is a prominent technique in data mining applications. It generates groups of data points that are similar to each other in a given aspect. Each group has some inherent latent similarity which is computed using the similarity measures. Clustering web users based on navigational pattern has always been an interesting as well as a challenging task. A web user, based on its navigational pattern, may belong to multiple categories. Intrinsically, web user navigation pattern exhibits sequential property. When dealing with sequence data, a similarity measure should be chosen, which captures both the order as well as content information during computation of similarity among sequences. In this paper, we have utilized the Sequence and Set Similarity Measure S^{3}M with rough set based similarity upper approximation clustering algorithm to group web users based on their navigational patterns. The quality of cluster formed using rough set based clustering algorithm with S^{3}M measure has been compared with the well known clustering algorithm, Density based spatial clustering of applications with noise DBSCAN. The experimental results show the viability of our approach.

The evolution of knowledge in communities of practice

Organizations and groups rely on the effective capture and sharing of knowledge for their survival. They spend a significant amount of effort and time to codify and manage the body of knowledge that their constituents collectively possess. Despite these efforts, tacit knowledge tends to solely reside within those who use it for their day to day work. It is widely believed that tacit knowledge disappears when the individual possessing it leaves an organization or group. More and more organizations are fostering communities of practice as a mechanism to influence knowledge creation and dissemination. Hence, it becomes imperative for us to understand how best to capture the knowledge that now resides within these communities, which could extend across multiple organizations. In this research article, we establish that the knowledge contained within communities of practice evolves over a period of time. We examine the evolution of this knowledge, and its impact on the community as well as the invidividuals concerned.

Emerging synergies between Internet of Things and social technologies

ABSTRACT This essay discusses the phenomena of amalgamation of two prominent technologies: Internet of Things (IoT) and Social technologies. IoT devices are primarily used for connectivity between physical objects while Social technologies are responsible for collaboration and social interaction. The domain of Social Internet of Things (SIoTs) points toward social interactions of IoT devices. This phenomenon will further enhance the collaboration capabilities of IoTs to deliver huge amounts of human–computer interactions with very limited interventions from humans. Thus, high degrees of human–computer interfaces can be created among physical objects by enabling them with human-like capabilities and social interactions. In this context, we discuss relevant research developments, contextually analyze the drivers and challenges of SIoTs, and describe some interesting business use cases along with suitable recommendations going forward.

Interest Diffusion in Heterogeneous Information Network for Personalized Item Ranking

Personalized item ranking for recommending top-N items of interest to a user is an interesting and challenging problem in e-commerce. Researchers and practitioner are continuously trying to devise new methodologies to improve the accuracy of recommendations. Recommendation problem becomes more challenging for sparse binary implicit feedback, due to the absence of explicit signals of interest and sparseness of data. In this paper, we deal with the problem of the sparseness of data and accuracy of recommendations. To address the issue, we propose an interest diffusion methodology in heterogeneous information network for items to be recommended using the meta-information related to items. In this heterogeneous information network, graph regularized interest diffusion is performed to generate personalized recommendations of top-N items. For interest diffusion, personalized weight learning is performed for different meta-information object types in the network. The experimental evaluation and comparison of the proposed methodology with the state-of-the-art techniques using the real-world datasets show the effectiveness of the proposed approach

Classification of information in a twitter stream

Social media has become an important mode of transfer of information across the individuals. Twitter is one such social media platform where individuals and corporations share information with public. Tweeting patterns can give important information about the context. In this paper we have proposed a mechanism that can be used to categorize the information flowing across the Twitter streams. We have used the proposed mechanism on a real dataset and reported our results. We also look at the velocity of retweets and its growth and decay during the life of a tweet.