def analyzeBestFitUser():
"""
对测试集中最佳预测的用户进行深入的探究
"""
movies, movieTagMat, userRankMat, testCases = loadData()
user2userPredictor = user2user(userRankMat, topK=105)
item2itemPredictor = item2item(userRankMat, movieTagMat, topK=20)
# do test
# _, results = predictTest(user2userPredictor, testCases, "")
# _, results = predictTest(item2itemPredictor, testCases, "")
# userAvgSSE = defaultdict(float)
# for res in results:
# userAvgSSE[res[0]] += (res[2] - res[1]) ** 2
# sse = list(userAvgSSE.items())
# sse.sort(key=lambda x: x[1])
# # best-fit user
# uid, minSSE = sse[0]
# print("(uid, smallest SSE): ({}, {})".format(uid, minSSE))
uid = 480
# do recommend
# 使用不同的推荐系统进行结果对比
# recommender = Recommender(movieTagMat, userRankMat, movies, user2userPredictor)
recommender = Recommender(movieTagMat, userRankMat, movies,
item2itemPredictor)
recommendMovies = recommender.doRecommend(uid, 50)["recommended_movies"]
print("recommended movies:")
recommendedCategory = defaultdict(int)
for m, r in recommendMovies.items():
for genre in movies[r[0]].genres:
recommendedCategory[genre] += 1
for k, v in sorted(recommendedCategory.items(),
key=lambda d: d[0],
reverse=True):
print("{}: {}".format(k, v))
print("")
# compare
print("His or her favorite movies:")
userRank = userRankMat[uid]
idx = np.argsort(-userRank)[:50]
userLikeCategory = defaultdict(int)
for i in idx:
for genre in movies[i].genres:
userLikeCategory[genre] += 1
for k, v in sorted(userLikeCategory.items(),
key=lambda d: d[0],
reverse=True):
print("{}: {}".format(k, v))
print("")
for k, v in recommendedCategory.items():
if k in userLikeCategory:
print("{},{},{}".format(k, v, userLikeCategory[k]))
else:
print("{},{},0".format(k, v))
for k, v in userLikeCategory.items():
if k not in recommendedCategory:
print("{},0,{}".format(k, v))
def doRecommender() -> None:
"""
对所有用户进行推荐，输出到recommend文件夹中，结果以json文件的格式保存
"""
begin = time()
movies, movieTagMat, userRankMat, _ = loadData()
predictor = item2item(userRankMat, movieTagMat, topK=100)
recommender = Recommender(movieTagMat, userRankMat, movies, predictor)
resFilePrefix: str = "./recommend/user"
for i in range(userRankMat.shape[0]):
with open(resFilePrefix + str(i + 1) + ".json", "w") as f:
f.write(
json.dumps(recommender.doRecommend(i, 50),
indent=4,
separators=(',', ':')))
end = time()
print("total time usage: {}".format(end - begin))