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Filter

Filter可以在评估时根据一定规则来过滤掉部分数据。 Filter的过滤对象是由recommender产生的recommendedList,recommendedList由一组recommendedItem构成,每个recommendedItem表示为一个三元组:(userId itemId value)。 目前支持的过滤器为GenericRecommendedFilter,其功能是返回recommendedList中包含指定userId或itemId的recommendedItem,指定的userId和itemId在GenericRecommendedFilter中以列表的形式提前设置。 目前Filter仅支持在Java代码中使用.

GenericRecommendedFilter过滤效果:

userIdList = {"1", "2"}
recommendedList = {
    {userId:1 itemId:1 value:1.0},
    {userId:1 itemId:2 value:2.0},
    {userId:1 itemId:3 value:3.0},
    {userId:2 itemId:1 value:4.0},
    {userId:2 itemId:2 value:5.0},
    {userId:2 itemId:3 value:6.0},
    {userId:3 itemId:1 value:7.0},
    {userId:3 itemId:2 value:8.0},
    {userId:3 itemId:3 value:9.0}
}
filtered recommendedList = {
    {userId:1 itemId:2 value:2.0},
    {userId:2 itemId:3 value:6.0},
    {userId:1 itemId:1 value:1.0},
    {userId:2 itemId:1 value:4.0},
    {userId:2 itemId:2 value:5.0},
    {userId:1 itemId:3 value:3.0}
}

GenericRecommendedFilter使用示例:


// specify the userIds and itemIds for filter userIdList = new ArrayList<>(); itemIdList = new ArrayList<>(); for (int i=1; i<=2; i++) { userIdList.add(Integer.toString(i)); itemIdList.add(Integer.toString(4-i)); } // generate recommendedList by recommender Configuration conf = new Configuration(); Resource resource = new Resource("rec/cf/userknn-test.properties"); conf.addResource(resource); DataModel dataModel = new TextDataModel(conf); dataModel.buildDataModel(); RecommenderContext context = new RecommenderContext(conf, dataModel); RecommenderSimilarity similarity = new PCCSimilarity(); similarity.buildSimilarityMatrix(dataModel); context.setSimilarity(similarity); Recommender recommender = new UserKNNRecommender(); recommender.setContext(context); recommender.recommend(context); List<RecommendedItem> recommendedItemList = recommender.getRecommendedList(); // filter the recommendedList with GenericRecommendedFilter GenericRecommendedFilter filter = new GenericRecommendedFilter(); filter.setUserIdList(userIdList); filter.setItemIdList(itemIdList); recommendedItemList = filter.filter(recommendedItemList);