(四一)电商应用性能优化：启动时间、内存占用与用户体验提升 原创

import com.bumptech.glide.Glide;                import android.widget.ImageView;                                 // 在 RecyclerView 的 Adapter 中                @Override                public void onBindViewHolder(ViewHolder holder, int position) {                    String imageUrl = getItem(position).getImageUrl();                    Glide.with(holder.itemView.getContext())                      .load(imageUrl)                      .into(holder.imageView);                }
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import java.util.concurrent.ExecutorService;                import java.util.concurrent.Executors;                                 public class AppInitializer {                    private static final ExecutorService executorService = Executors.newSingleThreadExecutor();                                     public static void initAsync() {                        executorService.submit(() -> {                            // 进行一些耗时的初始化操作，如加载配置文件                            loadConfig();                        });                    }                                     private static void loadConfig() {                        // 模拟加载配置文件                        try {                            Thread.sleep(2000);                        } catch (InterruptedException e) {                            e.printStackTrace();                        }                    }                }
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debugImplementation 'com.squareup.leakcanary:leakcanary-android:2.10'
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import leakcanary.LeakCanary;                import android.app.Application;                                 public class MyApplication extends Application {                    @Override                    public void onCreate() {                        super.onCreate();                        if (LeakCanary.isInAnalyzerProcess(this)) {                            return;                        }                        LeakCanary.install(this);                    }                }
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import retrofit2.Retrofit;                import retrofit2.converter.gson.GsonConverterFactory;                import okhttp3.Cache;                import okhttp3.OkHttpClient;                import java.io.File;                                 // 创建 OkHttpClient 并设置缓存                File cacheDirectory = new File(context.getCacheDir(), "http_cache");                int cacheSize = 10 * 1024 * 1024; // 10 MB                Cache cache = new Cache(cacheDirectory, cacheSize);                OkHttpClient client = new OkHttpClient.Builder()                  .cache(cache)                  .build();                                 // 创建 Retrofit 实例                Retrofit retrofit = new Retrofit.Builder()                  .baseUrl("https://api.example.com/")                  .addConverterFactory(GsonConverterFactory.create())                  .client(client)                  .build();
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import pandas as pd                from sklearn.metrics.pairwise import cosine_similarity                                 # 假设这是用户的购买记录数据                data = {                    'user_id': [1, 1, 2, 2, 3, 3],                    'product_id': [101, 102, 101, 103, 102, 103],                    'rating': [5, 3, 4, 2, 3, 4]                }                df = pd.DataFrame(data)                                 # 创建用户 - 商品矩阵                user_product_matrix = df.pivot(index='user_id', columns='product_id', values='rating').fillna(0)                                 # 计算用户之间的相似度                user_similarity = cosine_similarity(user_product_matrix)                                 # 为用户 1 推荐商品                target_user = 1                similar_users = user_similarity[target_user - 1].argsort()[::-1][1:]                recommended_products = []                for similar_user in similar_users:                    similar_user_products = user_product_matrix.iloc[similar_user].index[user_product_matrix.iloc[similar_user] > 0]                    for product in similar_user_products:                        if product not in user_product_matrix.iloc[target_user - 1].index[user_product_matrix.iloc[target_user - 1] > 0]:                            recommended_products.append(product)                    if len(recommended_products) >= 3:                        break                                 print("为用户 1 推荐的商品：", recommended_products)
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