Ranking Based on Collaborative Feature Weighting Applied to the Recommendation of Research Papers

Open access
Datum
2018-03Typ
- Journal Article
ETH Bibliographie
yes
Altmetrics
Abstract
Current research on recommendation systems focuses on optimization and evaluation of the quality of ranked recommended results. One of the most common approaches used in digital paper libraries to present and recommend relevant search results, is ranking the papers based on their features. However, feature utility or relevance varies greatly from highly relevant to less relevant, and redundant. Departing from the existing recommendation systems, in which all item features are considered to be equally important, this study presents the initial development of an approach to feature weighting with the goal of obtaining a novel recommendation method in which features which are more effective have a higher contribution/weight to the ranking process. Furthermore, it focuses on obtaining ranking of results returned by a query through a collaborative weighting procedure carried out by human users. The collaborative feature-weighting procedure is shown to be incremental, which in turn leads to an incremental approach to feature-based similarity evaluation. The obtained system is then evaluated using Normalized Discounted Cumulative Gain (NDCG) with respect to a crowd-sourced ranked results. Comparison between the performance of the proposed and Ranking SVM methods shows that the overall ranking accuracy of the proposed approach outperforms the ranking accuracy of Ranking SVM method. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000272406Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
International Journal of Artificial Intelligence & ApplicationsBand
Seiten / Artikelnummer
Verlag
AIRCC Publishing CorporationThema
Ranking; recommendation system; feature weighting; support vector machineOrganisationseinheit
02634 - Institut für Elektronik / Institute for ElectronicsETH Zürich
03388 - Tröster, Gerhard (emeritus)
ETH Bibliographie
yes
Altmetrics