DynFluid: Predicting Time-Evolving Rating in Recommendation Systems via Fluid Dynamics

In trust-based recommendation systems, if a user is predicted to have a high rating of a product, then this product is recommended to that user for shopping potential. Therefore, rating predictions are critical for qualified recommendations. In this paper, based on the fluid dynamics theory, we prop...

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Bibliographic Details
Published in2015 IEEE Trustcom/BigDataSE/ISPA Vol. 1; pp. 1 - 8
Main Authors Huanyang Zheng, Jie Wu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2015
Subjects
Computers
Containers
Fluid dynamics
Online social networks
rating prediction
recommendation systems
Social network services
Temperature measurement
trust propagation
Valves
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Summary:In trust-based recommendation systems, if a user is predicted to have a high rating of a product, then this product is recommended to that user for shopping potential. Therefore, rating predictions are critical for qualified recommendations. In this paper, based on the fluid dynamics theory, we propose a novel rating prediction scheme called DynFluid. The key observation is that the rating of a user depends on his/her user experience, as well as the ratings of other users. For example, users may refer to friends' ratings upon rating a product, themselves. DynFluid analogizes the rating reference among the users to the fluid flow among containers: each user is represented by a container, the rating of a user is mapped to be the fluid temperature in the corresponding container. Two user characteristics, persistency and persuasiveness, are also incorporated into DynFluid. Finally, real data-driven experiments in Epinions and Ciao validate the efficiency and effectiveness of the proposed DynFluid.
DOI:10.1109/Trustcom.2015.350