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Nahar, Siddharth (2018) Is User History and Article Meta-Data Enough to Predict Future Behavior? [Dissertation (University of Nottingham only)]

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Abstract

The exponential growth of Internet penetration in recent years has transformed the media

landscape. A growing number of people consume their media online, a trend paralleled by a

proportionate, and now exceeding increase in the amount of content produced. Exempted from

barriers of cost and reach, users now consume from a bevy of publishers. This paradigm has

necessitated content aggregators - platforms that offer content in bundles from different publishers.

Given the multiplicity of voices on the platform, aggregators face a novel challenge. Mere

aggregation doesn't guarantee retention - aggregated content now needs to be reliable, relevant and

personalized. To have repeat use, the aggregators need to predict a user's disposition, and collect

content that appeals to the user at a deeper level, than just her 'interests'. This study introduces a

machine learning approach using principles of recommender systems to model and predict user

behavior for a content-aggregation platform. Unlike traditional recommender systems, this study

incorporates content related meta-data in a predictive model. The study evaluates the performance

of the model through a comparison of popular classification algorithms. Finally, based on the

results of the predictive model the study undertakes a series of investigations which indicate key

bottlenecks in the approach and paves the way for future work.

Item Type:	Dissertation (University of Nottingham only)
Depositing User:	Nahar, Siddharth
Date Deposited:	26 Aug 2022 09:21
Last Modified:	26 Aug 2022 09:21
URI:	https://eprints.nottingham.ac.uk/id/eprint/54550

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