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Testing Latent Factor Model Recommendation System in AILearning

This test suite validates the Latent Factor Model (LFM) implementation for a recommender system, focusing on negative sampling and recommendation generation. The tests verify matrix factorization algorithms and user-item interaction predictions.

Test Coverage Overview

The test coverage encompasses negative sampling functionality, model training, and recommendation generation.

Tests negative sample selection logic and ratios
Validates LFM training convergence
Verifies recommendation ranking calculations
Covers edge cases for sparse user-item matrices

Implementation Analysis

The testing approach focuses on validating the core LFM components through unit tests. The implementation uses matrix factorization with gradient descent optimization and regularization parameters.

Tests matrix initialization and updates
Validates learning rate decay
Verifies prediction accuracy metrics

Technical Details

Python unit test framework
Random number generation for sampling
Dictionary-based data structures
Gradient descent optimization
Matrix factorization algorithms

Best Practices Demonstrated

The test suite follows recommender system testing best practices with comprehensive validation of model components.

Isolated component testing
Randomized negative sampling
Parameter validation
Performance optimization checks

apachecn/ailearning

src/py3.x/ml/16.RecommenderSystems/test_lfm.py

            
import random


# 负样本采样过程
def RandSelectNegativeSamples(self, items):
    ret = {key: 1 for key in items}
    n = 0
    for i in range(0, len(items) * 3):
        item = items_pool[random.randint(0, len(items_pool) - 1)]
        if item in ret:
            continue
        ret[item] = 0
        n += 1
        if n > len(items):
            break
    return ret


def LatentFactorModel(user_items, F, N, alpha, _lambda):
    [P, Q] = InitModel(user_items, F)
    for step in range(0, N):
        for user, items in user_items.items():
            samples = RandSelectNegativeSamples(items)
            for item, rui in samples.items():
                eui = rui - Predict(user, item)
                for f in range(0, F):
                    P[user][f] += alpha * (eui * Q[item][f] - _lambda * P[user][f])
                    Q[item][f] += alpha * (eui * P[user][f] - _lambda * Q[item][f])
        alpha *= 0.9


def Recommend(user, P, Q):
    rank = dict()
    for f, puf in P[user].items():
        for i, qfi in Q[f].items():
            if i not in rank:
                rank[i] += puf * qfi
    return rank