利用svd进行用户商品推荐的小实践

原创

于 2015-12-21 01:08:12 发布 · 1k 阅读

0 ·

CC 4.0 BY-SA版权

文章标签：

#numpy #python #推荐 #SVD

本文介绍了一个使用Python和numpy实现的基于Singular Value Decomposition (SVD)的商品推荐系统。通过计算用户评分数据的欧氏距离和余弦相似度，对未评分商品进行预测评分，并推荐评分最高的项目。示例展示了如何对特定用户进行商品推荐，并给出了推荐结果。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

from numpy import *
from numpy import linalg as la

def ecludSim(inA, inB):
    return 1.0/(1.0+ la.norm(inA - inB))

def cosSim(inA, inB):
    num = float(inA.T*inB)
    denom = la.norm(inA)*la.norm(inB)
    return 0.5+0.5*(num/denom)

def standEst(dataMat, user, simMeas, item):
    n = shape(dataMat)[1]
    simTotal = 0.0; ratSimTotal = 0.0
    for j in range(n):
        userRating = dataMat[user,j]
        if userRating == 0:continue
        overLap = nonzero(logical_and(dataMat[:,item].A>0,dataMat[:,j].A>0))[0]
        if len(overLap) == 0:similarity = 0
        else:similarity = simMeas(dataMat[overLap,item],dataMat[overLap,j])
        simTotal += similarity
        ratSimTotal += similarity * userRating
    if simTotal == 0:return 0
    else: return ratSimTotal/simTotal

def recommend(dataMat, user, N=4, simMeas = cosSim, estMethod = standEst):
    unratedIte