💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
热门游戏推荐系统介绍
热门游戏推荐系统是一款基于大数据技术栈构建的智能推荐平台，采用Hadoop分布式存储架构和Spark大数据处理引擎作为核心技术支撑。系统通过Django后端框架与Vue前端框架的完美结合，构建了一个功能完善的游戏推荐生态。平台具备用户管理、游戏信息展示、智能评分预测、社区互动交流等多项核心功能模块，利用Spark SQL进行海量游戏数据的实时分析与处理，结合机器学习算法实现个性化游戏推荐。系统前端采用ElementUI组件库和Echarts数据可视化工具，为用户提供直观友好的交互界面和丰富的数据展示效果。通过MySQL数据库存储用户行为数据和游戏基础信息，配合Hadoop分布式文件系统处理大规模数据集，实现了从数据采集、存储、分析到推荐结果输出的完整闭环。平台还集成了论坛分类讨论、意见反馈收集、举报记录管理等社交功能，营造了一个集游戏推荐、用户交流、内容管理于一体的综合性服务平台。
热门游戏推荐系统演示视频
热门游戏推荐系统演示图片
热门游戏推荐系统代码展示
from pyspark.sql import SparkSession
from pyspark.ml.recommendation import ALS
from pyspark.ml.feature import StringIndexer
from pyspark.sql.functions import col, desc, avg, count, when, isnan, isnull
from pyspark.ml.evaluation import RegressionEvaluator
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("GameRecommendationSystem").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def game_recommendation_algorithm(user_id, num_recommendations=10):
user_ratings_df = spark.sql("SELECT user_id, game_id, rating FROM user_ratings WHERE rating > 0")
user_indexer = StringIndexer(inputCol="user_id", outputCol="user_index")
game_indexer = StringIndexer(inputCol="game_id", outputCol="game_index")
user_indexed = user_indexer.fit(user_ratings_df).transform(user_ratings_df)
final_data = game_indexer.fit(user_indexed).transform(user_indexed)
training_data, test_data = final_data.randomSplit([0.8, 0.2], seed=42)
als_model = ALS(maxIter=10, regParam=0.1, userCol="user_index", itemCol="game_index", ratingCol="rating", coldStartStrategy="drop", implicitPrefs=False, alpha=0.1)
model = als_model.fit(training_data)
user_subset = final_data.filter(col("user_id") == user_id).select("user_index").distinct()
if user_subset.count() == 0:
popular_games = final_data.groupBy("game_id").agg(avg("rating").alias("avg_rating"), count("rating").alias("rating_count")).filter(col("rating_count") > 5).orderBy(desc("avg_rating")).limit(num_recommendations)
return popular_games.select("game_id").collect()
user_recommendations = model.recommendForUserSubset(user_subset, num_recommendations)
recommended_games = user_recommendations.select("recommendations.game_index").collect()[0][0]
game_id_mapping = final_data.select("game_index", "game_id").distinct()
recommended_game_ids = []
for game_index in recommended_games:
game_id = game_id_mapping.filter(col("game_index") == game_index["game_index"]).select("game_id").collect()
if game_id:
recommended_game_ids.append(game_id[0]["game_id"])
return recommended_game_ids
def rating_prediction_system(user_id, game_id):
historical_ratings = spark.sql("SELECT user_id, game_id, rating, play_time, genre, platform FROM user_game_interactions WHERE user_id = {} OR game_id = {}".format(user_id, game_id))
user_behavior_features = historical_ratings.filter(col("user_id") == user_id).agg(avg("rating").alias("user_avg_rating"), avg("play_time").alias("user_avg_playtime"), count("rating").alias("user_rating_count"))
if user_behavior_features.count() == 0:
global_avg = spark.sql("SELECT AVG(rating) as global_avg FROM user_game_interactions WHERE rating > 0").collect()[0]["global_avg"]
return round(global_avg, 2)
user_stats = user_behavior_features.collect()[0]
user_avg_rating = user_stats["user_avg_rating"] if user_stats["user_avg_rating"] else 3.0
user_avg_playtime = user_stats["user_avg_playtime"] if user_stats["user_avg_playtime"] else 60
user_rating_count = user_stats["user_rating_count"]
game_features = spark.sql("SELECT AVG(rating) as game_avg_rating, AVG(play_time) as game_avg_playtime, COUNT(*) as game_rating_count FROM user_game_interactions WHERE game_id = {} AND rating > 0".format(game_id))
game_stats = game_features.collect()[0]
game_avg_rating = game_stats["game_avg_rating"] if game_stats["game_avg_rating"] else 3.0
game_avg_playtime = game_stats["game_avg_playtime"] if game_stats["game_avg_playtime"] else 60
game_rating_count = game_stats["game_rating_count"]
similar_users = spark.sql("SELECT u1.user_id, AVG(ABS(u1.rating - u2.rating)) as similarity FROM user_game_interactions u1 JOIN user_game_interactions u2 ON u1.game_id = u2.game_id WHERE u2.user_id = {} AND u1.user_id != {} GROUP BY u1.user_id HAVING COUNT(*) >= 3 ORDER BY similarity LIMIT 10".format(user_id, user_id))
if similar_users.count() > 0:
similar_user_ratings = similar_users.join(spark.sql("SELECT user_id, rating FROM user_game_interactions WHERE game_id = {}".format(game_id)), "user_id")
if similar_user_ratings.count() > 0:
collaborative_score = similar_user_ratings.agg(avg("rating")).collect()[0][0]
else:
collaborative_score = game_avg_rating
else:
collaborative_score = game_avg_rating
confidence_factor = min(user_rating_count / 20.0, 1.0) * min(game_rating_count / 50.0, 1.0)
predicted_rating = (user_avg_rating * 0.3 + game_avg_rating * 0.4 + collaborative_score * 0.3) * confidence_factor + 3.0 * (1 - confidence_factor)
playtime_adjustment = 0
if user_avg_playtime > game_avg_playtime * 1.5:
playtime_adjustment = 0.2
elif user_avg_playtime < game_avg_playtime * 0.5:
playtime_adjustment = -0.2
final_prediction = max(1.0, min(5.0, predicted_rating + playtime_adjustment))
return round(final_prediction, 2)
def user_behavior_analysis(user_id=None, time_period="30d"):
if user_id:
user_actions = spark.sql("SELECT user_id, action_type, game_id, timestamp, session_duration FROM user_behavior_logs WHERE user_id = {} AND timestamp >= date_sub(current_date(), {})".format(user_id, time_period.replace("d", "")))
else:
user_actions = spark.sql("SELECT user_id, action_type, game_id, timestamp, session_duration FROM user_behavior_logs WHERE timestamp >= date_sub(current_date(), {})".format(time_period.replace("d", "")))
daily_activity = user_actions.groupBy("user_id", "action_type").agg(count("*").alias("action_count"), avg("session_duration").alias("avg_session_duration"))
game_preferences = user_actions.filter(col("action_type").isin(["play", "rate", "favorite"])).groupBy("user_id", "game_id").agg(count("*").alias("interaction_count")).orderBy(desc("interaction_count"))
peak_hours = user_actions.withColumn("hour", col("timestamp").substr(12, 2).cast("int")).groupBy("hour").agg(count("*").alias("activity_count")).orderBy(desc("activity_count"))
session_patterns = user_actions.filter(col("session_duration").isNotNull()).groupBy("user_id").agg(avg("session_duration").alias("avg_session"), count("*").alias("total_sessions"), (count("*") / 30).alias("sessions_per_day"))
genre_analysis = spark.sql("SELECT ub.user_id, g.genre, COUNT(*) as genre_interactions FROM user_behavior_logs ub JOIN games g ON ub.game_id = g.game_id WHERE ub.timestamp >= date_sub(current_date(), {}) GROUP BY ub.user_id, g.genre".format(time_period.replace("d", "")))
top_genres = genre_analysis.groupBy("user_id").agg(col("genre"), col("genre_interactions")).orderBy(desc("genre_interactions"))
retention_metrics = user_actions.groupBy("user_id").agg(count("timestamp").alias("total_days_active"), (count("timestamp") / 30.0).alias("retention_rate"))
churn_risk = retention_metrics.withColumn("churn_risk", when(col("retention_rate") < 0.1, "High").when(col("retention_rate") < 0.3, "Medium").otherwise("Low"))
engagement_score = session_patterns.join(daily_activity.groupBy("user_id").agg(avg("action_count").alias("avg_daily_actions")), "user_id").withColumn("engagement_score", (col("avg_session") / 60.0 + col("sessions_per_day") * 10 + col("avg_daily_actions")) / 3)
behavior_clusters = engagement_score.withColumn("user_segment", when(col("engagement_score") > 8, "Power User").when(col("engagement_score") > 4, "Regular User").when(col("engagement_score") > 1, "Casual User").otherwise("Inactive User"))
analysis_results = {
"daily_activity": daily_activity.collect(),
"game_preferences": game_preferences.collect(),
"peak_hours": peak_hours.collect(),
"session_patterns": session_patterns.collect(),
"genre_preferences": top_genres.collect(),
"retention_metrics": retention_metrics.collect(),
"churn_analysis": churn_risk.collect(),
"engagement_segments": behavior_clusters.collect()
}
return analysis_results
热门游戏推荐系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
