.png){kind=icon}
.png){kind=icon}
Return
Collaborative recommender systems: Combining effectiveness and efficiency
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2007.05.013.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the information overload problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recornmend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the big picture, we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms. (c) 2007 Elsevier Ltd. All rights reserved.
Keywords:
recommender system
collaborative filtering
nearest neighbors
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W