Faculty Publications
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Publications by NITK Faculty
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Item A personalized recommender system using Machine Learning based Sentiment Analysis over social data(Institute of Electrical and Electronics Engineers Inc., 2016) Ashok, M.; Rajanna, S.; Joshi, P.V.; Kamath S․, S.S.Social Media platforms are already an indispensable part of our daily lives. With its constant growth, it has contributed to superfluous, heterogeneous data which can be overwhelming due to its volume and velocity, thus limiting the availability of relevant and required information when a particular query is to be served. Hence, a need for personalized, fine-grained user preference-oriented framework for resolving this problem and also, to enhance user experience is increasingly felt. In this paper, we propose a such a social framework, which extracts user's reviews, comments of restaurants and points of interest such as events and locations, to personalize and rank suggestions based on user preferences. Machine Learning and Sentiment Analysis based techniques are used for further optimizing search query results. This provides the user with quicker and more relevant data, thus avoiding irrelevant data and providing much needed personalization. © 2016 IEEE.Item Saliency prediction for visual regions of interest with applications in advertising(Springer Verlag service@springer.de, 2017) Jain, S.; Kamath S․, S.S.Human visual fixations play a vital role in a plethora of genres, ranging from advertising design to human-computer interaction. Considering saliency in images thus brings significant merits to Computer Vision tasks dealing with human perception. Several classification models have been developed to incorporate various feature levels and estimate free eye-gazes. However, for real-time applications (Here, real-time applications refer to those that are time, and often resource-constrained, requiring speedy results. It does not imply on-line data analysis), the deep convolution neural networks are either difficult to deploy, given current hardware limitations or the proposed classifiers cannot effectively combine image semantics with low-level attributes. In this paper, we propose a novel neural network approach to predict human fixations, specifically aimed at advertisements. Such analysis significantly impacts the brand value and assists in audience measurement. A dataset containing 400 print ads across 21 successful brands was used to successfully evaluate the effectiveness of advertisements and their associated fixations, based on the proposed saliency prediction model. © Springer International Publishing AG 2017.