Faculty Publications

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Publications by NITK Faculty

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Subject: search.filters.subject.recommendation systems

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    A spatial clustering approach for efficient landmark discovery using geo-tagged photos
    (Institute of Electrical and Electronics Engineers Inc., 2016) Deeksha, S.D.; Ashrith, H.C.; Bansode, R.; Kamath S․, S.S.
    Geo-tagged photos enable people to share their personal experiences while visiting various vacation spots through image sharing social networks like Flickr. The geo-tag information offers a wealth of information for capturing additional information on traveler behavior, trends, opinions and interests. In this paper, we propose a landmark discovery system that aims to discover popular tourist attractions in a city by assuming that the popularity of a tourist attraction is positively dependent on the visitor statistics and also the amount of tourist uploaded photos clicked on site. It is a known fact that places with lots of geo-tagged photos uploaded to Flickr are visited more often by social-media savvy tourists, who plan their trip based on others' experiences. We propose to build a system that identifies the most popular tourist places in a particular city by using geo-tagged photos collected from Flickr and recommend the same to the user. In this paper, we present the methodology of spatially clustering the geo-tagged images and present an analysis of algorithm performance in identifying landmarks and their popularity. © 2015 IEEE.
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    Comparative Performance Evaluation of Web-Based Book Recommender Systems
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bhat, S.S.; Pranav, P.; Shashank, K.V.; Raghunandan, A.; Mohan, B.R.
    In today's world, recommendation algorithms are popularly utilised for personalization. To improve their business, e-commerce behemoths rely heavily on their recommendation algorithms. As a result, the quality of suggestions can have a big impact on how much money they make. As a result, effective evaluation of recommender systems is critical. Traditional evaluation measures are limited to error-based and accuracy-based metrics, and do not account for characteristics such as novelty, informedness, markedness, and so on. This research study aims to compare the effectiveness of two web-based book recommendation systems by using the measures like diversity, informedness, and markedness, which are less well-known but equally essential. © 2022 IEEE.