Learning the Dynamic Change of User Interests from Noise Web Data
Julius Onyancha, Valentina Plekhanova · 2021
The web is noise, inconsistent and irrelevant by nature, finding useful information that defines interest of user has become a challenge. Noisy web data is currently considered as data that is not part of the main web page content. However, not every item of information that forms part of the main web content meets the interests of web users. Existing research acknowledges that there is a need to propose machine learning tools capable of addressing problems with data available on the web and what users are interested in. This research work presents an investigation of current research work proposed in minimizing the levels of noisy data on the web. It examines their contribution as well as existing challenges. It proposes an approach that learns web data in relation to interestingness of users. The proposed approach considers the dynamic change of user interest as well as evolving web data. This is to ensure not only reduction of noise in web data but also decrease of useful information otherwise identified and eliminated as noise by existing tools. The practical application of the proposed tool will contribute towards creating dynamic web contents that are seamless to dynamic user interests. Experiments conducted in this research shows that noise web data reduction process is driven by evolving user interests. As a result, what is currently identified and eliminated as noise can be useful when user interests and their changes over time are considered