This paper examines the correlation of implicit and explicit user behaviour indicators in a task specific domain. An experiment was conducted and data was collected from 77 undergraduate students of Computer science. Users’ implicit features and explicit ratings of document relevance were captured and logged through a plugin in Firefox browser. A number of implicit indicators were correlated with user explicit ratings and a predictive function model was derived. Classification algorithms were also used to classify documents according to how relevant they are to the current task. It was found that implicit indicators could be used successfully to predict the user rating. These findings can be utilised in building individual and group profile for users of a context-based recommender system.
|Title of host publication||Engineering Applications of Neural Networks|
|Editors||Lazaros Iliadis, Chrisina Jayne|
|Place of Publication||Switzerland|
|ISBN (Print)||Print: 978-3-319-23981-1, Online: 978-3-319-23983-5|
|Publication status||Published - 2015|
Bibliographical noteThere is no full text available at this time.
- Implicit indicators
- Explicit rating
- Context based
- Recommender system
Akuma, S., Jayne, C., Iqbal, R., & Doctor, F. (2015). Inferring Users’ Interest on Web Documents Through Their Implicit Behaviour. In L. Iliadis, & C. Jayne (Eds.), Engineering Applications of Neural Networks (Vol. 517, pp. 315-325). Switzerland: Springer Verlag. https://doi.org/10.1007/978-3-319-23983-5_29