Is User History and Article Meta-Data Enough to Predict Future Behavior?
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,...
| Main Author: | |
|---|---|
| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2018
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| Online Access: | https://eprints.nottingham.ac.uk/54550/ |
| _version_ | 1848799055075868672 |
|---|---|
| author | Nahar, Siddharth |
| author_facet | Nahar, Siddharth |
| author_sort | Nahar, Siddharth |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:29:34Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-54550 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:29:34Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-545502022-08-26T09:21:13Z https://eprints.nottingham.ac.uk/54550/ Is User History and Article Meta-Data Enough to Predict Future Behavior? Nahar, Siddharth 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. 2018-12-01 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/54550/1/Nahar_Siddharth%20Dissertation%20Submission%204309871.pdf Nahar, Siddharth (2018) Is User History and Article Meta-Data Enough to Predict Future Behavior? [Dissertation (University of Nottingham only)] |
| spellingShingle | Nahar, Siddharth Is User History and Article Meta-Data Enough to Predict Future Behavior? |
| title | Is User History and Article Meta-Data Enough to Predict Future Behavior? |
| title_full | Is User History and Article Meta-Data Enough to Predict Future Behavior? |
| title_fullStr | Is User History and Article Meta-Data Enough to Predict Future Behavior? |
| title_full_unstemmed | Is User History and Article Meta-Data Enough to Predict Future Behavior? |
| title_short | Is User History and Article Meta-Data Enough to Predict Future Behavior? |
| title_sort | is user history and article meta-data enough to predict future behavior? |
| url | https://eprints.nottingham.ac.uk/54550/ |