A Probabilistic View of Neighborhood-based Recommendation Methods

WANG, Jun; Tang, Qiang

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A Probabilistic View of Neighborhood-based Recommendation Methods

WANG, Jun; Tang, Qiang

2016 • In ICDM 2016 - IEEE International Conference on Data Mining series (ICDM) workshop CLOUDMINE

Peer reviewed

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https://hdl.handle.net/10993/29142

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Keywords :

recommender system; probabilistic graphical model; neighborhood

Abstract :

[en] Probabilistic graphic model is an elegant framework to compactly present complex real-world observations by modeling uncertainty and logical flow (conditionally independent factors). In this paper, we present a probabilistic framework of neighborhood-based recommendation methods (PNBM) in which similarity is regarded as an unobserved factor. Thus, PNBM leads the estimation of user preference to maximizing a posterior over similarity. We further introduce a novel multi-layer similarity descriptor which models and learns the joint influence of various features under PNBM, and name the new framework MPNBM. Empirical results on real-world datasets show that MPNBM allows very accurate estimation of user preferences.

Disciplines :

Computer science

Author, co-author :

WANG, Jun ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)

Tang, Qiang

External co-authors :

Language :

English

Title :

A Probabilistic View of Neighborhood-based Recommendation Methods

Publication date :

12 December 2016

Event name :

ICDM 2016 - IEEE International Conference on Data Mining series (ICDM) workshop CLOUDMINE

Event date :

from 12-12-2016 to 15-12-2016

Audience :

International

Main work title :

ICDM 2016 - IEEE International Conference on Data Mining series (ICDM) workshop CLOUDMINE

Peer reviewed :

Peer reviewed

FnR Project :

FNR5856658 - Boosting Security And Efficiency In Recommender Systems, 2013 (15/04/2014-14/04/2017) - Qiang Tang

Funders :

FNR - Fonds National de la Recherche

Available on ORBilu :

since 21 December 2016

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