Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

&nbsp;

IIT Palakkad

Learning from multiple groups of observations are often useful due to the advantage of sharing of statistical information. Hierarchical Bayesian models provide a natural mechanism to achieve this, and hierarchical Dirichlet processes (HDPs) have shown significant impact in this field. HDPs define a collection of probability measures one for each group. All the measures provide support on a common countably infinite set of atoms to share information. The fundamental mechanism in all the variants of HDP make the weights on these atoms positively correlated across groups. This structural limitation is impossible to resolve without changing the sharing principle. But this property hinders the applicability of HDP priors to many problems, when an atom may be highly probable in some groups despite being rare in all other groups. This becomes evident in clustering through association of atoms and observations. Some clusters may be weakly present in most of the groups in spite of being prominent in some groups and vice-versa. In this paper, we pose the problem of weight agnosticism, that of constructing a collection of probability measures on a common countably infinite set of atoms with mutually independent weights across groups. This implies that, a cluster can contain observations from all groups, but popularities of a cluster across groups are mutually independent. So the size of a cluster in a group does not interfere in the participation of observations in other groups to that cluster. Our contribution is also to construct a novel hierarchical Bayesian nonparametric prior, Weight-Agnostic hierarchical Stick-breaking process (WAS), which models weight agnosticism. WAS extends the framework of stick-breaking process (SBP) in a novel direction. However, WAS becomes non-exchangeable and that makes inference process non-standard. But, We derive tractable predictive probability functions for WAS, which is useful in deriving efficient truncation-free MCMC inference competitive with those in HDP settings. We discuss few real life applications of WAS in topic moeling and information retrieval. Furthermore, experimenting with five real life datasets we show that, WAS significantly outperforms HDP in various settings. © 2018 IEEE

Department of Data Science

World University root node

Proceedings - 9th IEEE International Conference on Big Knowledge, ICBK 2018

Weight-agnostic hierarchical stick-breaking process

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

International Workshop on Semantic Evaluation

SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

Recommending lifestyle articles is of immediate interest to theffecommerce industry and is beginning to attract research attention. Offen followed strategies, such as recommending popular items are inadequate for this vertical because of two reasons. Firstly, users have their own personal preference over items, referred to as personal styles, which lead to the long-tail phenomenon. Secondly, each user displays multiple personas, each persona has a preference over items which could be dictated by a particular occasion, e.g. dressing for a party would be different from dressing to go to o?ce. Recommendation in this vertical is crucially dependent on discovering styles for each of the multiple personas. There is no literature which addresses this problem. We posit a generative model which describes each user by a Simplex Over PERsona, SOPER, where a persona is described as the individuals preferences over prevailing styles modelled as topics over items. The choice of simplex and the long-tail nature necessitates the use of stick-breaking process. The main technical contribution is an efficient collapsed Gibbs sampling based algorithm for solving the attendant inference problem. Trained on large-scale interaction logs spanning more than halfa-million sessions collected from an e-commerce portal, SOPER outperforms previous baselines such as [9] by a large margin of 35% in identifying persona. Consequently it outperforms several competitive baselines comprehensively on the task of recommending from a catalogue of roughly 150 thousand lifestyle articles, by improving the recommendation quality as measured by AUC by a staggering 12:23%, in addition to aiding the interpretability of uncovered personal and fashionable styles thus advancing our precise understanding of the underlying phenomena. © 2017 ACM.

International Conference on Information and Knowledge Management, Proceedings

SOPER: Discovering the influence of fashion and the many faces of user from session logs using stick breaking process

We consider the problem of recommending comment-worthy articles such as news and blog-posts. An article is defined to be comment-worthy for a particular user if that user is interested to leave a comment on it. We note that recommending commentworthy articles calls for elicitation of commenting-interests of the user from the content of both the articles and the past comments made by users. We thus propose to develop content-driven user profiles to elicit these latent interests of users in commenting and use them to recommend articles for future commenting. The difficulty of modeling comment content and the varied nature of users' commenting interests make the problem technically challenging. The problem of recommending comment-worthy articles is resolved by leveraging article and comment content through topic modeling and the co-commenting pattern of users through collaborative filtering, combined within a novel hierarchical Bayesian modeling approach. Our solution, Collaborative Correspondence Topic Models (CCTM), generates user profiles which are leveraged to provide a personalized ranking of comment-worthy articles for each user. Through these content-driven user profiles, CCTM effectively handle the ubiquitous problem of cold-start without relying on additional meta-data. The inference problem for the model is intractable with no off-the-shelf solution and we develop an effcient Monte Carlo EM algorithm. CCTM is evaluated on three real world data-sets, crawled from two blogs, ArsTechnica (AT) Gadgets (102,087 comments) and AT-Science (71,640 comments), and a news site, DailyMail (33,500 comments). We show average improvement of 14% (warm-start) and 18% (cold-start) in AUC, and 80% (warm-start) and 250% (cold-start) in Hit-Rank@5, over state of the art [1, 2]. © 2015 ACM.

RecSys 2015 - Proceedings of the 9th ACM Conference on Recommender Systems

Content driven user profiling for comment-worthy recommendations of news and blog articles

Understanding user generated comments in response to news and blog posts is an important area of research. After ignoring irrelevant comments, one finds that a large fraction, approximately 50%, of the comments are very specific and can be further related to certain parts of the article instead of the entire story. For example, in a recent product review of Google Nexus 7 in ArsTechnica (a popular blog), the reviewer talks about the prospect of "Retina equipped iPad mini" in a few sentences. It is interesting that although the article is on Nexus 7, but a significant number of comments are focused on this specific point regarding "iPad". We pose the problem of detecting such comments as specific comments location (SCL) problem. SCL is an important open problem with no prior work. SCL can be posed as a correspondence problem between comments and the parts of the relevant article, and one could potentially use Corr-LDA type models. Unfortunately, such models do not give satisfactory performance as they are restricted to using a single topic vector per article-comments pair. In this paper we propose to go beyond the single topic vector assumption and propose a novel correspondence topic model, namely SCTM, which admits multiple topic vectors (MTV) per article-comments pair. The resulting inference problem is quite complicated because of MTV and has no off-the-shelf solution. One of the major contributions of this paper is to show that using stick-breaking process as a prior over MTV, one can derive a collapsed Gibbs sampling procedure, which empirically works well for SCL. SCTM is rigorously evaluated on three datasets, crawled from Yahoo! News (138,000 comments) and two blogs, ArsTechnica (AT) Science (90,000 comments) and AT-Gadget (160,000 comments). We observe that SCTM performs better than Corr-LDA, not only in terms of metrics like perplexity and topic coherence but also discovers more unique topics. We see that this immediately leads to an order of magnitude improvement in F1 score over Corr-LDA for SCL. © 2014 ACM.

WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining

Going beyond Corr-LDA for detecting specific comments on news &amp; blogs

Scatter/Gather systems are increasingly becoming useful in browsing document corpora. Usability of the present-day systems are restricted to monolingual corpora, and their methods for clustering and labeling do not easily extend to the multilingual setting, especially in the absence of dictionaries/machine translation. In this paper, we study the cluster labeling problem for multilingual corpora in the absence of machine translation, but using comparable corpora. Using a variational approach, we show that multilingual topic models can effectively handle the cluster labeling problem, which in turn allows us to design a novel Scatter/Gather system ShoBha. Experimental results on three datasets, namely the Canadian Hansards corpus, the entire overlapping Wikipedia of English, Hindi and Bengali articles, and a trilingual news corpus containing 41,000 articles, confirm the utility of the proposed system. © 2012 Springer-Verlag Berlin Heidelberg.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Cluster labeling for multilingual scatter/gather using comparable corpora

Motivated by the task of vernacular news analysis using known news topics from national news-papers, we study the task of topic analysis, where given source datasets with observed topics, data items from a target dataset need to be assigned either to observed source topics or to new ones. Using Hierarchical Dirichlet Processes for addressing this task imposes unnecessary and often inappropriate generative assumptions on the observed source topics. In this paper, we explore Dirichlet Processes with partially observed groups (POG-DP). POG-DP avoids modeling the given source topics. Instead, it directly models the conditional distribution of the target data as a mixture of a Dirichlet Process and the posterior distribution of a Hierarchical Dirichlet Process with known groups and topics. This introduces coupling between selection probabilities of all topics within a source, leading to effective identification of source topics. We further improve on this with a Combinatorial Dirichlet Process with partially observed groups (POG-CDP) that captures finer grained coupling between related topics by choosing intersections between sources. We evaluate our models in three different real-world applications. Using extensive experimentation, we compare against several baselines to show that our model performs significantly better in all three applications. © 2011 IEEE.

Proceedings - IEEE International Conference on Data Mining, ICDM

Learning Dirichlet Processes from partially observed groups

In this paper a new concept of ranking among the solutions of the same front, along with elite preservation mechanism and ensuring diversity through the nearest neighbor method is proposed for multi-objective genetic algorithms. This algorithm is applied on a set of benchmark multi-objective test problems and the results are compared with that of NSGA-II (a similar algorithm). The proposed algorithm is seen to over perform the existing algorithm. More specifically, the new approach has been used to solve the deceptive multi-objective optimization problems in a better way.