A Food Recommendation Algorithm with Context

doi:10.3969/j.issn.1006-2475.2015.07.005

Abstract

Abstract:

As the traditional collaborative filtering recommendation algorithm didn’t consider the situation that context information affected the users’ behavior seriously, a

recommendation algorithm with context was put forward and the algorithm was applied to food recommendation. First of all, the context was added to the traditional useritem

model expressed as an attributes vector, resulting in a UIC interest model. Then SubUsers were created according to different context from one user, thus obtaining a

new useritem ratings matrix in a certain context. Because the data sparseness problem was easy to generate in this approach, WSlopeOne algorithm was designed to predict

unknown ratings. Based on optimized similarity formula, more effective recommendation service would be provided that can more accurately find the current users neighbor,

providing users with good service. Last, experiments were done to verify the contents this paper proposed and expectation for further research was brought forward.

Key words: interest model, recommendation algorithm, collaborative filtering, context information, similarity formula

CLC Number:

TP311

ZHAO Xuemei1, GUO Linfeng2, BIAN Xuewen1. A Food Recommendation Algorithm with Context[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.07.005.

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A Food Recommendation Algorithm with Context

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