Aggregated Recommendation through Random Forests
Joint Authors
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-11
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Aggregated recommendation refers to the process of suggesting one kind of items to a group of users.
Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation.
In this paper, we propose a random forest approach to create aggregated recommender systems.
The approach is used to predict the rating of a group of users to a kind of items.
In the preprocessing stage, we merge user, item, and rating information to construct an aggregated decision table, where rating information serves as the decision attribute.
We also model the data conversion process corresponding to the new user, new item, and both new problems.
In the training stage, a forest is built for the aggregated training set, where each leaf is assigned a distribution of discrete rating.
In the testing stage, we present four predicting approaches to compute evaluation values based on the distribution of each tree.
Experiments results on the well-known MovieLens dataset show that the aggregated approach maintains an acceptable level of accuracy.
American Psychological Association (APA)
Zhang, Heng-Ru& Min, Fan& He, Xu. 2014. Aggregated Recommendation through Random Forests. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1050503
Modern Language Association (MLA)
Zhang, Heng-Ru…[et al.]. Aggregated Recommendation through Random Forests. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1050503
American Medical Association (AMA)
Zhang, Heng-Ru& Min, Fan& He, Xu. Aggregated Recommendation through Random Forests. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1050503
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050503