Ranking Based on Collaborative Feature Weighting Applied to the Recommendation of Research Papers
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Sarabadani Tafreshi, Amir Esmaeil
Sarabadani Tafreshi, Amirehsan
Ralescu, Anca L.
- Journal Article
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Rights / licenseCreative Commons Attribution 4.0 International
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 Show more
Journal / seriesInternational Journal of Artificial Intelligence & Applications
Pages / Article No.
PublisherAIRCC Publishing Corporation
SubjectRanking; recommendation system; feature weighting; support vector machine
Organisational unit02634 - Institut für Elektronik / Electronics Laboratory
03388 - Tröster, Gerhard (emeritus)
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