context_embedding.py 文件源码

def embedding_learning(train_data, user_dic, artist_dic, context_list, n_users, n_items):
    # User embeddings
    UC = np.random.normal(0.0, 0.01, (n_users, dim_num))
    # Item embeddings
    IC = np.random.normal(0.0, 0.01, (n_items, dim_num))

    try:
        for iteration in range(max_iters):
            print 'loading...iteration: %d'%iteration
            t = time.time()

            for each_data in train_data:
                u_i, i, w_i = each_data
                w_i = w_i ** dis_coef
                # print artist_dic[i]
                for u_j in context_list[u_i]:

                    IC[artist_dic[i]] += learning_rate * ((1 - sigmoid(w_i)) * 2 * alpha  * (UC[user_dic[u_i]] - UC[user_dic[u_j]]) - 2 * lamda * IC[artist_dic[i]])
                    UC[user_dic[u_i]] += learning_rate * ((1 - sigmoid(w_i)) * 2 * alpha  * (IC[artist_dic[i]] - UC[user_dic[u_i]]) - 2 * lamda * UC[user_dic[u_i]])
                    UC[user_dic[u_j]] += learning_rate * ((1 - sigmoid(w_i)) * 2 * alpha  * (IC[artist_dic[i]] - UC[user_dic[u_j]]) - 2 * lamda * UC[user_dic[u_j]])

                    # print IC[artist_dic[i]]
            print 'Iter: %d   elapsed:  %fseconds'%(iteration, time.time() - t)
    finally:
        np.save(model_dir + 'Item_Emb', IC)
        np.save(model_dir + 'User_Emb', UC)
        np.savetxt(model_dir + 'Item_Emb.txt', IC)
        np.savetxt(model_dir + 'User_Emb.txt', UC)
        print 'Model saved...'