Chidansh Bhatt, Ph.D.

Research Scientist

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Chidansh is a Research Scientist at FXPAL. His research focuses on context-related media search, classification, recommendation, and interactive visualization. Further interests are multimedia data mining, information retrieval, machine learning, natural language processing, big data analytics, IoT, HCI, semantic analytics (concept/action/event /object detection, novelty re-ranking), analysis of social media data and user behavior using crowdsourcing techniques.

Prior to joining FXPAL, Chidansh was working as an assistant professor at Indian Institute of Technology (IIT), Roorkee, India. Chidansh was a post-doc researcher at IDIAP Research Institute, Switzerland, where he developed multimodal recommender and summarization system with visualization for scientific material (Video-Lectures) and his system secured the 1^stposition for hyperlinking task in MediaEval benchmarking evaluation. Chidansh also worked as a researcher at Big Data Experimental Laboratory, Hitachi Research & Development Ltd., Singapore and did research internship at University of Winnipeg, Canada. Chidansh actively participates as a technical program committee member/reviewer of leading international conferences and journals (e.g., best reviewer award at ICME 2014, ETRI 2012).

Dr. Bhatt received a Ph.D. in computer science from National University of Singapore (NUS) in 2012. He also holds a M.E. in internet science and engineering from Indian Institute of Science (IISc) and a B.E. in information science and engineering from Visweswariah Technological University (VTU).

Co-Authors

David Ayman Shamma

Jian Zhao

Publications by Chidansh Bhatt

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2018

Using Recommendation to Explore Educational Video

By Matthew Cooper, Jian Zhao, Chidansh Bhatt and David Ayman Shamma

Publication Details

ACM Intl. Conf. on Multimedia Retrieval (ICMR)
Jun June 11, 2018

Abstract

Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. Increasingly, professionals consume this content to augment or update specific skills rather than complete degree or certification programs. To better address the needs of this emergent user population, we describe a visual recommender system called MOOCex. The system recommends lecture videos {\em across} multiple courses and content platforms to provide a choice of perspectives on topics. The recommendation engine considers both video content and sequential inter-topic relationships mined from course syllabi. Furthermore, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context.

Flexible Learning with Semantic Visual Exploration and Sequence-Based Recommendation of MOOC Videos

By Jian Zhao, Chidansh Bhatt, Matthew Cooper and David Ayman Shamma

Publication Details

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Apr April 21, 2018

Abstract

Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. This paper presents MOOCex, a technique that can offer a more flexible learning experience for MOOCs. MOOCex can recommend lecture videos across different courses with multiple perspectives, and considers both the video content and also sequential inter-topic relationships mined from course syllabi. MOOCex is also equipped with interactive visualization allowing learners to explore the semantic space of recommendations within their current learning context. The results of comparisons to traditional methods, including content-based recommendation and ranked list representation, indicate the effectiveness of MOOCex. Further, feedback from MOOC learners and instructors suggests that MOOCex enhances both MOOC-based learning and teaching.

SeqSense: video recommendation using topic sequence mining

By Chidansh Bhatt, Matthew Cooper and Jian Zhao

Publication Details

Multimedia Modeling 2018
Feb February 5, 2018

Abstract

This paper examines content-based recommendation in domains exhibiting sequential topical structure. An example is educational video, including Massive Open Online Courses (MOOCs) in which knowledge builds within and across courses. Conventional content-based or collaborative filtering recommendation methods do not exploit courses' sequential nature. We describe a system for video recommendation that combines topic-based video representation with sequential pattern mining of inter-topic relationships. Unsupervised topic modeling provides a scalable and domain-independent representation. We mine inter-topic relationships from manually constructed syllabi that instructors provide to guide students through their courses. This approach also allows the inclusion of multi-video sequences among the recommendation results. Integrating the resulting sequential information with content-level similarity provides relevant as well as diversified recommendations. Quantitative evaluation indicates that the proposed system, \textit{SeqSense}, recommends fewer redundant videos than baseline methods, and instead emphasizes results consistent with mined topic transitions.

2017

FXPAL Experiments for TRECVID 2016 Video Hyperlinking

By Chidansh Bhatt and Matthew Cooper

Publication Details

TRECVID Workshop
Mar March 1, 2017

Abstract

This is a summary of our participation in the TRECVID 2016 video hyperlinking task (LNK). We submitted four runs in total. A baseline system combined on established vectorspace text indexing and cosine similarity. Our other runs explored the use of distributed word representations in combination with fine-grained inter-segment text similarity measures.

2016

Audiovisual Summarization of Lectures and Meetings Using a Segment Similarity Graph

By Chidansh Bhatt, Andrei Popescu-Belis and Matthew Cooper

Publication Details

ACM International Conference on Multimedia Retrieval (ICMR)
Jun June 6, 2016

Abstract

We propose a method for extractive summarization of audiovisual recordings focusing on topic-level segments. We first build a content similarity graph between all segments of all documents in the collection, using word vectors from the transcripts, and then select the most central segments for the summaries. We evaluate the method quantitatively on the AMI Meeting Corpus using gold standard reference summaries and the Rouge metric, and qualitatively on lecture recordings using a novel two-tiered approach with human judges. The results show that our method compares favorably with others in terms of Rouge, and outperforms the baselines for human scores, thus also validating our evaluation protocol.