Learning and exploiting context dependencies for robust recommendations

Learning and exploiting context dependencies for robust recommendations

We consider the recommendation problem, where a set of available items or choices are rated and recommended to users accordingly. Over and above the ratings information used in traditional filtering algorithms, the context of the user-recommender interaction is used to improve the recommendation...

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Bibliographic Details
Main Author: Yap, Ghim Eng
Other Authors: Pang Hwee Hwa
Format: Theses and Dissertations
Language:English
Published: 2010
Subjects:
DRNTU::Engineering::Computer science and engineering::Information systems::Information systems applications
Online Access:https://hdl.handle.net/10356/41737
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Institution: Nanyang Technological University
Language: English
Description
Summary:We consider the recommendation problem, where a set of available items or choices are rated and recommended to users accordingly. Over and above the ratings information used in traditional filtering algorithms, the context of the user-recommender interaction is used to improve the recommendation quality. Specifically, we study how the effective learning and exploitation of context dependencies can help to generate more personal and relevant recommendations.

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