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Testing Item-Based Collaborative Filtering Algorithms in ailearning

This test suite validates item-based recommendation system algorithms, implementing similarity calculations and recommendation generation for a machine learning system. The tests cover core functionality for computing item similarities and generating personalized recommendations based on user-item interactions.

Test Coverage Overview

The test suite covers two main item similarity calculation methods and corresponding recommendation algorithms.

Tests ItemSimilarity1 for basic co-occurrence based similarity
Tests ItemSimilarity2 with logarithmic penalty for popular items
Validates Recommendation1 for basic weighted scoring
Verifies Recommendation2 with detailed reasoning tracking

Implementation Analysis

The testing approach focuses on validating mathematical correctness of similarity calculations and recommendation rankings. The implementation uses dictionary-based data structures for efficient storage and retrieval of similarity matrices and user-item interactions.

Key patterns include matrix-based similarity computations and top-K filtering for recommendations.

Technical Details

Testing utilizes:

Python’s math module for numerical computations
Operator.itemgetter for efficient sorting
Dictionary-based sparse matrix representations
Custom similarity metrics and recommendation algorithms

Best Practices Demonstrated

The test implementation demonstrates robust handling of sparse data structures and efficient computation of similarity metrics. Notable practices include:

Separation of similarity calculation and recommendation logic
Efficient top-K filtering implementation
Support for recommendation reasoning and weight tracking

apachecn/ailearning

src/py3.x/ml/16.RecommenderSystems/test_基于物品.py

            
import math
from operator import itemgetter


def ItemSimilarity1(train):
    #calculate co-rated users between items
    C = dict()
    N = dict()
    for u, items in train.items():
        for i in users:
            N[i] += 1
            for j in users:
                if i == j:
                    continue
                C[i][j] += 1

    #calculate finial similarity matrix W
    W = dict()
    for i,related_items in C.items():
        for j, cij in related_items.items():
            W[u][v] = cij / math.sqrt(N[i] * N[j])
    return W


def ItemSimilarity2(train):
    #calculate co-rated users between items
    C = dict()
    N = dict()
    for u, items in train.items():
        for i in users:
            N[i] += 1
            for j in users:
                if i == j:
                    continue
            C[i][j] += 1 / math.log(1 + len(items) * 1.0)

    #calculate finial similarity matrix W
    W = dict()
    for i,related_items in C.items():
        for j, cij in related_items.items():
            W[u][v] = cij / math.sqrt(N[i] * N[j])
    return W


def Recommendation1(train, user_id, W, K):
    rank = dict()
    ru = train[user_id]
    for i,pi in ru.items():
        for j, wj in sorted(W[i].items(), key=itemgetter(1), reverse=True)[0:K]:
            if j in ru:
                continue
            rank[j] += pi * wj
    return rank


def Recommendation2(train, user_id, W, K):
    rank = dict()
    ru = train[user_id]
    for i,pi in ru.items():
        for j, wj in sorted(W[i].items(), key=itemgetter(1), reverse=True)[0:K]:
            if j in ru:
                continue
            rank[j].weight += pi * wj
            rank[j].reason[i] = pi * wj
    return rank