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Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attention in the current literature. In this work, we propose a model which extends variational autoencoders by exploiting the rich information present in the past preference history. We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network. At each time-step of the RNN, the sequence is fed through a series of fully-connected layers, the output of which models the probability distribution of the most likely future preferences. We show that handling temporal information is crucial for improving the accuracy of the VAE: In fact, our model beats the current state-of-the-art by valuable margins because of its ability to capture temporal dependencies among the user-consumption sequence using the recurrent encoder still keeping the fundamentals of variational autoencoders intact.

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Abstract. K-NearestNeighbor based classifier classifies a query instance based on the class labels of its neighbor instances. Although kNN has proved to be a ubiquitous classification tool with good scalability, but it suffers from some drawbacks. The existing kNN algorithm is equivalent to using only local prior probabilities to predict instance labels, and hence it does not take into account the class distribution around neighborhood of the query instance, which results into undesirable performance on imbalanced data. In this paper, a modified version of kNN algorithm is proposed so that it takes into account the class distribution in a wider region around the query instance. Our empirical experiments with several real world datasets show that our algorithm outperforms current state-of-the-art approaches.

In this paper we introduce a novel and efficient approach to detect topics in a large corpus of research papers. With rapidly growing size of academic literature, the problem of topic detection has become a very challenging task. We present a unique approach that uses closed frequent keywordset to form topics. Our approach also provides a natural method to cluster the research papers into hierarchical, overlapping clusters using topic as similarity measure. To rank the research papers in the topic cluster, we devise a modified PageRank algorithm that assigns an authoritative score to each research paper by considering the sub-graph in which the research paper appears. We test our algorithms on the DBLP dataset and experimentally show that our algorithms are fast, effective and scalable.

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