Item-Based Recommendations with Hadoop

Mahout在MapReduce上实现了Item-Based Collaborative Filtering,这里我尝试运行一下。

  1. 安装Hadoop

  2. 从下载Mahout并解压

  3. 准备数据

    下载1 Million MovieLens Dataset,解压得到ratings.dat,用

    sed 's/:😦[0-9]{1,}):😦[0-9]{1})::[0-9]{1,}$/,\1,\2/' ratings.dat

    处理成需要的格式。

  4. 运行

    mahout recommenditembased -s SIMILARITY_LOGLIKELIHOOD -i /path/to/input/file -o /path/to/desired/output -n 25

    参数:

    MAHOUT-JOB: /home/laxe/apple/mahout/mahout-examples-0.11.0-job.jar
    Job-Specific Options:
    --input (-i) input Path to job input directory.
    --output (-o) output The directory pathname for output.
    --numRecommendations (-n) numRecommendations Number of recommendations per user.
    --usersFile usersFile File of users to recommend for.
    --itemsFile itemsFile File of items to recommend for.
    --filterFile (-f) filterFile File containing comma-separated userID,itemID pairs. Used to exclude the item from the recommendations for that user(optional).
    --userItemFile (-uif) userItemFile File containing comma-separated userID,itemID pairs(optional). Used to include only these items into recommendations. Cannot be used together with usersFile or itemsFile.
    --booleanData (-b) booleanData Treat input as without prefvalues.
    --maxPrefsPerUser (-mxp) maxPrefsPerUser Maximum number of preferences considered per user in final recommendation phase.
    --minPrefsPerUser (-mp) minPrefsPerUser Ignore users with less preferences than this in the similarity computation (default: 1).
    --maxSimilaritiesPerItem (-m) maxSimilaritiesPerItem Maximum number of similarities considered per item.
    --maxPrefsInItemSimilarity (-mpiis) maxPrefsInItemSimilarity Max number of preferences to consider per user or item in the item similarity computation phase, users or items with more preferences will be sampled down(default: 500).
    --similarityClassname (-s) similarityClassname Name of distributed similarity measures class to instantiate,
    alternatively use one of the predefined similarities([SIMILARITY_COOCCURRENCE, SIMILARITY_LOGLIKELIHOOD, SIMILARITY_TANIMOTO_COEFFICIENT, SIMILARITY_CITY_BLOCK, SIMILARITY_COSINE, SIMILARITY_PEARSON_CORRELATION, SIMILARITY_EUCLIDEAN_DISTANCE])
    --threshold (-tr) threshold Discard item pairs with a similarity value below this.
    --outputPathForSimilarityMatrix (-opfsm) outputPathForSimilarityMatrix Write the items imilarity matrix to this path(optional).
    --randomSeed randomSeed Use this seed for sampling.
    --sequencefileOutput Write the output into a Sequence File instead of a text file.
    --help (-h) Print out help.
    --tempDir tempDir Intermediate output directory.
    --startPhase startPhase First phase to run.
    --endPhase endPhase Last phase to run specify HDFS directories while running on hadoop; else specify local file system directories.

参考
Introduction to Item-Based Recommendations with Hadoop
mahout分布式:Item-based推荐

相关推荐
鸭鸭鸭进京赶烤2 小时前
大学专业科普 | 云计算、大数据
大数据·云计算
G皮T6 小时前
【Elasticsearch】自定义评分检索
大数据·elasticsearch·搜索引擎·查询·检索·自定义评分·_score
掘金-我是哪吒8 小时前
分布式微服务系统架构第156集:JavaPlus技术文档平台日更-Java线程池使用指南
java·分布式·微服务·云原生·架构
亲爱的非洲野猪8 小时前
Kafka消息积压的多维度解决方案:超越简单扩容的完整策略
java·分布式·中间件·kafka
活跃家族8 小时前
分布式压测
分布式
搞笑的秀儿9 小时前
信息新技术
大数据·人工智能·物联网·云计算·区块链
SelectDB9 小时前
SelectDB 在 AWS Graviton ARM 架构下相比 x86 实现 36% 性价比提升
大数据·架构·aws
二二孚日9 小时前
自用华为ICT云赛道Big Data第五章知识点-Flume海量日志聚合
大数据·华为
前端世界10 小时前
HarmonyOS开发实战:鸿蒙分布式生态构建与多设备协同发布全流程详解
分布式·华为·harmonyos
DavidSoCool10 小时前
RabbitMQ使用topic Exchange实现微服务分组订阅
分布式·微服务·rabbitmq