diff --git a/AUTHORS b/AUTHORS new file mode 100644 index 0000000..0577fa6 --- /dev/null +++ b/AUTHORS @@ -0,0 +1,5 @@ +Li Yu +Wei Gong +Dongsong Zhang +Yu Ding +Ze Fu \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..9dab7e6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,20 @@ +Copyright 2024, Li Yu, Wei Gong, Dongsong Zhang and Yu Ding + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + diff --git a/README.md b/README.md index 491955f..7eb17f2 100644 --- a/README.md +++ b/README.md @@ -1 +1,59 @@ -# 2023.0131 \ No newline at end of file +[![INFORMS Journal on Computing Logo](https://INFORMSJoC.github.io/logos/INFORMS_Journal_on_Computing_Header.jpg)](https://pubsonline.informs.org/journal/ijoc) + +# From Interaction to Prediction: A Multi-Interactive Attention based Approach to Product Rating Prediction + +This archive is distributed in association with the [INFORMS Journal on +Computing](https://pubsonline.informs.org/journal/ijoc) under the [MIT License](LICENSE). + +The software and data in this repository are a snapshot of the software and data +that were used in the research reported on in the paper +[From Interaction to Prediction: A Multi-Interactive Attention based Approach to Product Rating Prediction](https://doi.org/10.1287/ijoc.2023.0131) by Yu L, Gong W, Zhang D, and Ding Y. + + +## Cite + +To cite the contents of this repository, please cite both the paper and this repo, using their respective DOIs. + +https://doi.org/10.1287/ijoc.2023.0131 + +https://doi.org/10.1287/ijoc.2023.0131.cd + +Below is the BibTex for citing this snapshot of the repository. + +``` +@misc{Yu2024, + author = {Li Yu, Wei Gong, Dongsong Zhang and Yu Ding}, + publisher = {INFORMS Journal on Computing}, + title = {From Interaction to Prediction: A Multi-Interactive Attention based Approach to Product Rating Prediction}, + year = {2024}, + doi = {10.1287/ijoc.2023.0131.cd}, + url = {https://github.com/INFORMSJoC/2023.0131}, + note = {Available for download at https://github.com/INFORMSJoC/2023.0131}, +} +``` + +## Description +Despite increasing research on product rating prediction, very few studies have considered user-item interaction relationships at multiple levels. To address this critical limitation, we propose a novel Rating Prediction method based on Multi-Interaction Attention (RPMIA) by learning user-item interaction relationships at three levels simultaneously from online consumer reviews for predicting product ratings with reasonable interpretability. Specifically, RPMIA first deploys a multi-head cross attention mechanism to capture the interaction between contexts of items and users. Then, it uses a bi-layer gate-based mechanism to extract aspects of users and items, and a self-attention mechanism is further used to learn their interaction at the aspect level. +Finally, the aspects of users and items are coupled together to form meaningful user-item aspect pairs (UIAP) via a joint attention. A multi-task predictor that integrates a factorization machine and a feedforward neural network is designed to generate a rating prediction. We have empirically evaluated RPMIA with seven real-world datasets. The results demonstrate that RPMIA outperforms the state-of-the-art methods consistently and significantly. We also conduct a user study to assess the interpretability of the RPMIA method. + + + +## Data and instructions to run RPMIA + +We used seven publicly available [OCR datasets](http://jmcauley.ucsd.edu/data/amazon/) collected from Amazon.com on different products, including music instruments, office products, digital music, grocery & gourmet food,video games, tools & home improvement, and sports and outdoors products, which have been widely used for recommendation evaluation in previous studies.These datasets consist of consumers’ product ratings ranging from 1 to 5 and corresponding textual reviews. Each consumer or item has at least 5 reviews. All the datasets are in folder `dataset/` (where `dataset_D`->Digital Music,`dataset_G`->Grocery and Gourmet Food,`dataset_M`->Musical Instruments,`dataset_O`->Office Products,`dataset_S`->Sports and Outdoors,`dataset_T`->Tools and Home Improvement,`dataset_V`->Video Games. In this repository, we use `dataset_G` as an example). + +The code `src/RPMIA.py` implements and RPMIA model. In this implementation, we define five modules, including an embedding module, a context-aware module, an aspect-aware module, a UIAP-aware module, and a prediction module. + +The package `_auxiliaryTools/` implements data preprocessing of datasets, where code `ExtractData.py` implements extracting dataset from file, `Evaluation.py` defines a function named `get_test_list_mask` to batch process test data (including user ID, item ID, rating, etc.) and related user reviews, item reviews, and mask information according to a given batch size, and return multiple lists containing batches of different types of data. + +Use the following command to run the code: +``` +python src/RPMIA.py +``` + + +## Prerequisties (please install the following packages before you run our RPMIA model) +- tensorflow 1.15 +- python 3.6 +- numpy 1.19.2 +- pandas 1.1.5 \ No newline at end of file diff --git a/_auxiliaryTools/Evaluation.py b/_auxiliaryTools/Evaluation.py new file mode 100644 index 0000000..c0e102d --- /dev/null +++ b/_auxiliaryTools/Evaluation.py @@ -0,0 +1,28 @@ +import math + + +def get_test_list_mask(batch_size, test_rating, user_reviews, item_reviews, user_masks, item_masks): + user_test_batchs, item_test_batchs, user_input_test_batchs, item_input_test_batchs, user_mask_test_batchs, item_mask_test_batchs, rating_input_test_batchs = [], [], [], [], [], [], [] + for count in range(int(math.ceil(len(test_rating) / float(batch_size)))): + user_test, item_test, user_input_test, item_input_test, user_mask_test, item_mask_test, rating_input_test = [], [], [], [], [], [], [] + for idx in range(batch_size): + index = (count * batch_size + idx) + if (index >= len(test_rating)): + break + rating = test_rating[index] + user_test.append(rating[0]) + item_test.append(rating[1]) + user_input_test.append(user_reviews.get(rating[0])) + item_input_test.append(item_reviews.get(rating[1])) + user_mask_test.append(user_masks.get(rating[0])) + item_mask_test.append(item_masks.get(rating[1])) + rating_input_test.append([rating[2]]) + user_test_batchs.append(user_test) + item_test_batchs.append(item_test) + user_input_test_batchs.append(user_input_test) + item_input_test_batchs.append(item_input_test) + user_mask_test_batchs.append(user_mask_test) + item_mask_test_batchs.append(item_mask_test) + rating_input_test_batchs.append(rating_input_test) + #print count, len(item_input_test_batchs[count]) + return user_test_batchs, item_test_batchs, user_input_test_batchs, item_input_test_batchs, user_mask_test_batchs, item_mask_test_batchs, rating_input_test_batchs diff --git a/_auxiliaryTools/ExtractData.py b/_auxiliaryTools/ExtractData.py new file mode 100644 index 0000000..6939888 --- /dev/null +++ b/_auxiliaryTools/ExtractData.py @@ -0,0 +1,112 @@ +import numpy as np +import scipy.sparse as sp +import pandas as pd +from time import time +import os + +class Dataset(object): + "'extract dataset from file'" + + def __init__(self, max_length, path): + self.word_id_dict = self.load_word_dict(path + "WordDict.out") + print ("wordId_dict finished") + self.userReview_dict, self.userMask_dict = self.load_reviews(max_length, len(self.word_id_dict), path + "UserReviews.out") + self.itemReview_dict, self.itemMask_dict = self.load_reviews(max_length, len(self.word_id_dict), path + "ItemReviews.out") + print ("load reviews finished") + self.num_users, self.num_items = len(self.userReview_dict), len(self.itemReview_dict) + self.trainMtrx = self.load_ratingFile_as_mtrx(path + "TrainInteraction.out") + self.testRatings = self.load_ratingFile_as_list(path + "TestInteraction.out") + + def load_word_dict(self, path): + wordId_dict = {} + + with open(path, "r") as f: + line = f.readline().replace("\n", "") + while line != None and line != "": + arr = line.split("\t") + wordId_dict[arr[0]] = int(arr[1]) + line = f.readline().replace("\n", "") + + return wordId_dict + + def load_reviews(self, max_doc_length, padding_word_id, path): + entity_review_dict = {} + entity_mask_dict = {} + + with open(path, "r") as f: + line = f.readline().replace("\n", "") + while line != None and line != "": + review = [] + mask = [] + arr = line.split("\t") + entity = int(arr[0]) + word_list = arr[1].split(" ") + + for i in range(len(word_list)): + if (word_list[i] == "" or word_list[i] == None or (not (word_list[i] in self.word_id_dict))): + continue + review.append(self.word_id_dict.get(word_list[i])) + mask.append(1.0) + if (len(review) >= max_doc_length): + break + if (len(review) < max_doc_length): + review, mask = self.padding_word(max_doc_length, padding_word_id, review, mask) + entity_review_dict[entity] = review + entity_mask_dict[entity] = mask + line = f.readline().replace("\n", "") + return entity_review_dict, entity_mask_dict + + def padding_word(self, max_size, max_word_idx, review, mask): + review.extend([max_word_idx]*(max_size - len(review))) + mask.extend([0.0] * (max_size - len(mask))) + return review, mask + + def load_ratingFile_as_mtrx(self, file_path): + mtrx = sp.dok_matrix((self.num_users, self.num_items), dtype=np.float32) + with open(file_path, "r") as f: + line = f.readline() + line = line.strip() + while line != None and line != "": + arr = line.split("\t") + user, item, rating = int(arr[0]), int(arr[1]), float(arr[2]) + if (rating > 0): + mtrx[user, item] = rating + line = f.readline() + + return mtrx + + def load_ratingFile_as_list(self, file_path): + rateList = [] + + with open(file_path, "r") as f: + line = f.readline() + while line != None and line != "": + arr = line.split("\t") + user, item = int(arr[0]), int(arr[1]) + rate = float(arr[2]) + rateList.append([user, item, rate]) + line = f.readline() + + return rateList + +if __name__ == "__main__": + os.environ["CUDA_VISIBLE_DEVICES"] = "1" + latent_dim = 25#denote as k + word_latent_dim = 300#denote as d in paper + window_size = 3#denote as c in paper + max_doc_length = 300 + + # loading data + firTime = time() + dataSet = Dataset(max_doc_length, "output/") + word_dict, user_reviews, item_reviews, user_masks, item_masks, train, testRatings = dataSet.word_id_dict, dataSet.userReview_dict, dataSet.itemReview_dict, dataSet.userMask_dict, dataSet.itemMask_dict, dataSet.trainMtrx, dataSet.testRatings + secTime = time() + + word_dict_df = pd.DataFrame([word_dict]) + user_reviews_df = pd.DataFrame([user_reviews]) + item_reviews_df = pd.DataFrame([item_reviews]) + + num_users, num_items = train.shape + print ("load data: %.3fs" % (secTime - firTime)) + print (num_users, num_items) + \ No newline at end of file diff --git a/_auxiliaryTools/__init__.py b/_auxiliaryTools/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/dataset/dataset_G/TestInteraction.out b/dataset/dataset_G/TestInteraction.out new file mode 100644 index 0000000..e2f0418 --- /dev/null +++ b/dataset/dataset_G/TestInteraction.out @@ -0,0 +1,30466 @@ +1 7024 5.0 1355443200 +1 4913 5.0 1384905600 +1 6600 3.0 1313107200 +2 6630 5.0 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5.605802066295998 +grapefuit 10.217479578184859 +meltsfrom 2.836365789810378 +scrumpious 4.587032665138905 +response 1449.5598309223087 +responsive 128.9154877555706 +4something 6.39318087181906 +rival 314.4876621671422 diff --git a/figs/model.png b/figs/model.png new file mode 100644 index 0000000..cb3fa07 Binary files /dev/null and b/figs/model.png differ diff --git a/src/RPMIA.py b/src/RPMIA.py new file mode 100644 index 0000000..1d5838b --- /dev/null +++ b/src/RPMIA.py @@ -0,0 +1,465 @@ +# coding: utf-8 +import tensorflow as tf +import numpy as np +import math +import tensorflow as tf +from time import time +from _auxiliaryTools.ExtractData import Dataset +from _auxiliaryTools.Evaluation import get_test_list_mask +epsilon = 1e-9 + +def ini_word_embed(num_words, latent_dim): + word_embeds = np.random.rand(num_words, latent_dim) + return word_embeds + +def word2vec_word_embed(num_words, latent_dim, path, word_id_dict): + word2vect_embed_mtrx = np.zeros((num_words, latent_dim)) + with open(path, "r") as f: + line = f.readline() + while line != None and line != "": + arr = line.split("\t") + row_id = word_id_dict.get(arr[0]) + vect = arr[1].strip().split(" ") + for i in range(len(vect)): + word2vect_embed_mtrx[row_id, i] = float(vect[i]) + line = f.readline() + + return word2vect_embed_mtrx + +def get_train_instance(train): + user_input, item_input, rates = [], [], [] + + for (u, i) in train.keys(): + # positive instance + user_input.append(u) + item_input.append(i) + rates.append(train[u,i]) + return user_input, item_input, rates + +def get_train_instance_batch(count, batch_size, user_input, item_input, ratings, user_reviews, item_reviews, user_masks, item_masks): + users_batch, items_batch, user_input_batch, item_input_batch, user_mask_batch, item_mask_batch, labels_batch= [], [], [], [], [], [], [] + + for idx in range(batch_size): + index = (count*batch_size + idx) % len(user_input) + users_batch.append(user_input[index]) + items_batch.append(item_input[index]) + user_input_batch.append(user_reviews.get(user_input[index])) + item_input_batch.append(item_reviews.get(item_input[index])) + user_mask_batch.append(user_masks.get(user_input[index])) + item_mask_batch.append(item_masks.get(item_input[index])) + labels_batch.append([ratings[index]]) + + return users_batch, items_batch, user_input_batch, item_input_batch, user_mask_batch, item_mask_batch, labels_batch +# user_batch, item_batch, user_input_batch, item_input_batch, user_mask_batch, item_mask_batch, rates_batch + +def multi_head_cross_attention_layer(num_filters,cU,cI,rand_matrix): + sec_dim = int(cU.get_shape()[1]) + tmpcU = tf.reshape(cU, [-1, num_filters]) + cU_mul_rand = tf.reshape(tf.matmul(tmpcU, rand_matrix), [-1, sec_dim, num_filters]) + f = tf.matmul(cU_mul_rand, cI, transpose_b=True) + f = tf.expand_dims(f, -1) + att1 = tf.tanh(f) + + pool_user = tf.reduce_mean(att1, 2) + pool_item = tf.reduce_mean(att1, 1) + + user_flat = tf.squeeze(pool_user, -1) + item_flat = tf.squeeze(pool_item, -1) + weight_user = tf.nn.softmax(user_flat) + weight_item = tf.nn.softmax(item_flat) + + weight_user_exp = tf.expand_dims(weight_user, -1) + weight_item_exp = tf.expand_dims(weight_item, -1) + + weighted_U = cU * weight_user_exp + weighted_I = cI * weight_item_exp + + return weighted_U, weighted_I + +def asps_gate(word_dim, contextual_words, asps_embeds, W_word, W_asps, b_gate, drop_out): + W_word = tf.nn.dropout(W_word, drop_out) + W_asps = tf.nn.dropout(W_asps, drop_out) + contextual_words_reshape = tf.reshape(contextual_words, [-1, num_filters]) + asps_gate = tf.reshape( + tf.nn.sigmoid(tf.matmul(contextual_words_reshape, W_word) + tf.matmul(asps_embeds, W_asps) + b_gate), + [-1, word_dim, num_filters]) + gated_contextual_words = contextual_words * asps_gate + return gated_contextual_words + +def asps_prj(word_num, gated_contextual_embeds, W_prj): + gated_contextual_embeds_reshape = tf.reshape(gated_contextual_embeds, [-1, num_filters]) + return tf.reshape(tf.matmul(gated_contextual_embeds_reshape, W_prj), [-1, word_num, num_filters]) + +def squash(vector): + '''Squashing function corresponding to Eq. 1 + Args: + vector: A tensor with shape [batch_size, 1, num_caps, vec_len, 1] or [batch_size, num_caps, vec_len, 1]. + Returns: + A tensor with the same shape as vector but squashed in 'vec_len' dimension. + ''' + vec_squared_norm = tf.reduce_sum(tf.square(vector), -2, keep_dims=True) + scalar_factor = vec_squared_norm / (1 + vec_squared_norm) / tf.sqrt(vec_squared_norm + epsilon) + vec_squashed = scalar_factor * vector # element-wise + return (vec_squashed) + +def simple_self_attn(word_dim, u_hat, input_masks, caps_b, b_ij, cap_1_b_inputs): + third_dim = int(u_hat.get_shape()[2]) + + u_hat = tf.reshape(u_hat, shape=[-1, word_dim, int(third_dim / num_filters), num_filters, 1]) + u_hat_stopped = tf.stop_gradient(u_hat) + + b_ij = tf.expand_dims(tf.expand_dims(cap_1_b_inputs * b_ij, -1), -1) + + for r_itr in range(itr_0): + if (r_itr > 0): + b_ij = b_ij * tf.expand_dims(tf.expand_dims(tf.expand_dims(input_masks, -1), -1), -1) + c_ij = tf.nn.softmax(b_ij, dim=1) + + if r_itr == itr_0 - 1: + s_j = tf.multiply(c_ij, u_hat) + s_j = tf.reduce_sum(s_j, axis=1, keep_dims=True) + caps_b + v_j = squash(s_j) + + elif r_itr < itr_0 - 1: + s_j = tf.multiply(c_ij, u_hat_stopped) + s_j = tf.reduce_sum(s_j, axis=1, keep_dims=True) + caps_b + v_j = squash(s_j) + + v_j_tiled = tf.tile(v_j, [1, word_dim, 1, 1, 1]) + u_produce_v = tf.reduce_sum(u_hat_stopped * v_j_tiled, axis=3, + keep_dims=True) + + b_ij += u_produce_v + + return tf.squeeze(v_j, [1, -1]) + +def pack_vects(vect_1, vect_2, type): + if type == "mul": + return tf.multiply(vect_1, vect_2) + elif type == "sub": + return tf.subtract(vect_1, vect_2) + elif type == "both": + return tf.concat([tf.multiply(vect_1, vect_2), tf.subtract(vect_1, vect_2)], 2) + +import tensorflow as tf + +# attention +def attention(inputs, attention_size, time_major=False, return_alphas=False): + + hidden_size = inputs.shape[2].value + + # Trainable parameters + w_omega = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1)) + b_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1)) + u_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1)) + + with tf.name_scope('v'): + # Applying fully connected layer with non-linear activation to each of the B*T timestamps; + # the shape of `v` is (B,T,D)*(D,A)=(B,T,A), where A=attention_size + v = tf.tanh(tf.tensordot(inputs, w_omega, axes=1) + b_omega) + + # For each of the timestamps its vector of size A from `v` is reduced with `u` vector + vu = tf.tensordot(v, u_omega, axes=1, name='vu') # (B,T) shape + alphas = tf.nn.softmax(vu, name='alphas') # (B,T) shape + output = tf.reduce_sum(inputs * tf.expand_dims(alphas, -1), 1) + + if not return_alphas: + return output + else: + return output, alphas + +def eval_model(users, items, users_inputs, items_inputs, users_masks_inputs, items_masks_inputs, dropout_rate, + b_inputs, predict_rating, sess, user_tests, item_tests, user_input_tests, + item_input_tests, user_mask_tests, item_mask_tests, rate_tests, b_test,rmses, maes): + predicts = sess.run(predict_rating, feed_dict={users: user_tests, items: item_tests, users_inputs: user_input_tests, + items_inputs: item_input_tests, users_masks_inputs: user_mask_tests, + items_masks_inputs: item_mask_tests, b_inputs: b_test, + dropout_rate: 1.0}) + row, col = predicts.shape +# print(predicts) + for r in range(row): + rmses.append(pow((predicts[r, 0] - rate_tests[r][0]), 2)) + maes.append(abs(predicts[r, 0] - rate_tests[r][0])) + return rmses, maes + + +if __name__ == "__main__": + # os.environ["CUDA_VISIBLE_DEVICES"] = "1" + latent_dim = 50#denote as k + word_latent_dim = 300#denote as d in paper + num_filters = 50#number of filters of convolution operation + window_size = 3#denote as c in paper + max_doc_length = 300 + learning_rate = 0.001 + epochs = 200 + batch_size = 200 + num_aspect = 5#denote as M in paper + _EPSILON = 10e-9 + itr_0 = 2#number of iteration of self attention, we use 2 + lambda_1 = 0.001 + gama = 0.5#denote as λ in paper + drop_out = 0.5 + v_dim = 50 + gama = 0.01 + + # loading data + firTime = time() + dataSet = Dataset(max_doc_length, "./outputs/output_G/") #!!!!!在这里更改数据所在目录!!!!! + word_dict, user_reviews, item_reviews, user_masks, item_masks, train, testRatings = dataSet.word_id_dict, dataSet.userReview_dict, dataSet.itemReview_dict, dataSet.userMask_dict, dataSet.itemMask_dict, dataSet.trainMtrx, dataSet.testRatings + secTime = time() + + num_users, num_items = train.shape + print ("load data: %.3fs" % (secTime - firTime)) + print (num_users, num_items) + + #load word embeddings + word_embedding_mtrx = ini_word_embed(len(word_dict), word_latent_dim) + # word_embedding_mtrx = word2vec_word_embed(len(word_dict), word_latent_dim, + # "Directory of pretrained WordEmbedding.out", + # word_dict) + + print ("shape", word_embedding_mtrx.shape) + + # get train instances + user_input, item_input, rateings = get_train_instance(train) + + # get test/val instances + user_test, item_test, user_input_test, item_input_test, user_mask_test, item_mask_test, rating_input_test = get_test_list_mask( + 200, testRatings, user_reviews, item_reviews, user_masks, item_masks) + #user_tests, item_tests, user_input_test, item_input_test, rating_input_test = get_test_list(200, testRatings, user_reviews, item_reviews) + + b_train = np.zeros([batch_size, 1, 1]) + + # 变量定义及初始化 + users = tf.placeholder(tf.int32, shape=[None], name='users') + items = tf.placeholder(tf.int32, shape=[None], name='items') + users_inputs = tf.placeholder(tf.int32, shape=[None, max_doc_length], name='users_inputs') + items_inputs = tf.placeholder(tf.int32, shape=[None, max_doc_length], name='items_inputs') + users_masks_inputs = tf.placeholder(tf.float32, shape=[None, max_doc_length], name='users_masks_inputs') + items_masks_inputs = tf.placeholder(tf.float32, shape=[None, max_doc_length], name='items_masks_inputs') + b_inputs = tf.placeholder(tf.float32, shape=[None, 1, 1], name='b_inputs') + ratings = tf.placeholder(tf.float32, shape=[None, 1], name='ratings') + dropout_rate = tf.placeholder(tf.float32, name='dropout_rate') + + text_embedding = tf.Variable(word_embedding_mtrx, dtype=tf.float32) + padding_embedding = tf.Variable(np.zeros([1, word_latent_dim]), dtype=tf.float32) + + word_embeddings = tf.concat([text_embedding, padding_embedding], 0) + + # interaction embedding + user_bias = tf.Variable(tf.random_normal([num_users, 1], mean=0, stddev=0.02)) + item_bias = tf.Variable(tf.random_normal([num_items, 1], mean=0, stddev=0.02)) + user_bs = tf.nn.embedding_lookup(user_bias, users) + item_bs = tf.nn.embedding_lookup(item_bias, items) + + + #-----从这里开始是模型的核心部分----- + # 1. Embedding Layer + user_reviews_representation = tf.nn.embedding_lookup(word_embeddings, users_inputs) + item_reviews_representation = tf.nn.embedding_lookup(word_embeddings, items_inputs) + user_reviews_representation_expnd = tf.expand_dims(user_reviews_representation, -1) + item_reviews_representation_expnd = tf.expand_dims(item_reviews_representation, -1) + + # 2. Multiple Convolution Operations + W_conv_u = tf.Variable( tf.truncated_normal([window_size, word_latent_dim, 1, num_filters], stddev=0.3), name="review_W_u") + W_conv_i = tf.Variable(tf.truncated_normal([window_size, word_latent_dim, 1, num_filters], stddev=0.3), name="review_W_i") + convU = tf.nn.conv2d(user_reviews_representation_expnd, W_conv_u, strides=[1, 1, word_latent_dim, 1], padding='SAME') + convI = tf.nn.conv2d(item_reviews_representation_expnd, W_conv_i, strides=[1, 1, word_latent_dim, 1], padding='SAME') + cU = tf.nn.relu(tf.squeeze(convU, 2)) + cI = tf.nn.relu(tf.squeeze(convI, 2)) + + #print("cU->>>>>> ",cU) + #print("cI->>>>>> ",cI) + + # 3. Multi-head Cross-attention Layer + rand_matrix = tf.Variable(tf.truncated_normal([num_filters, num_filters], stddev=0.3), name="review_rand_matrix") + weighted_U, weighted_I = multi_head_cross_attention_layer(num_filters, cU, cI,rand_matrix) + + #print("weighted_U->>>>>> ",weighted_U) + #print("weighted_I->>>>>> ",weighted_I) + + user_word_dim = int(weighted_U.get_shape()[1]) + item_word_dim = int(weighted_I.get_shape()[1]) + + # 4. Bi-layer Gate Mechanism + user_viewpoint_embeddings = [ + tf.Variable(tf.truncated_normal([1, num_filters], stddev=0.3), name="user_aspect_embedding_{}".format(i)) for i + in range(num_aspect)] + item_aspect_embeddings = [ + tf.Variable(tf.truncated_normal([1, num_filters], stddev=0.3), name="user_aspect_embedding_{}".format(i)) for i + in range(num_aspect)] + + W_word_gate_u = [ + tf.Variable(tf.truncated_normal([num_filters, num_filters], stddev=0.1), name="user_word_gate_u_{}".format(i)) + for i in range(num_aspect)] + W_asps_gate_u = [ + tf.Variable(tf.truncated_normal([num_filters, num_filters], stddev=0.1), name="user_asps_gate_u_{}".format(i)) + for i in range(num_aspect)] + W_word_gate_i = [ + tf.Variable(tf.truncated_normal([num_filters, num_filters], stddev=0.1), name="item_word_gate_i_{}".format(i)) + for i in range(num_aspect)] + W_asps_gate_i = [ + tf.Variable(tf.truncated_normal([num_filters, num_filters], stddev=0.1), name="item_asps_gate_i_{}".format(i)) + for i in range(num_aspect)] + b_gate_u = [tf.Variable(tf.constant(0.1, shape=[num_filters]), name="gate_b_u_{}".format(i)) for i in + range(num_aspect)] + b_gate_i = [tf.Variable(tf.constant(0.1, shape=[num_filters]), name="gate_b_i_{}".format(i)) for i in + range(num_aspect)] + + user_pre_attn_contextual_embeds, item_pre_attn_contextual_embeds = [], [] + for i in range(num_aspect): + user_pre_attn_contextual_embeds.append( + asps_gate(user_word_dim, weighted_U, user_viewpoint_embeddings[i], W_word_gate_u[i], + W_asps_gate_u[i], b_gate_u[i], dropout_rate)) + item_pre_attn_contextual_embeds.append( + asps_gate(item_word_dim, weighted_I, item_aspect_embeddings[i], W_word_gate_i[i], + W_asps_gate_i[i], b_gate_i[i], dropout_rate)) + + + # 5. Aspect Self-Attention + W_prj_u = [tf.Variable(tf.truncated_normal([num_filters, num_filters], mean=0, stddev=0.3), + name="user_project_W_{}".format(i)) for i in range(num_aspect)] + b_prj_u = [tf.Variable(tf.constant(0.1, shape=[1, 1, 1, num_filters, 1]), name="user_project_b_{}".format(i)) for i in + range(num_aspect)] + b_ij_u = [tf.constant(np.zeros([1, max_doc_length, 1], dtype=np.float32), name="user_bij_{}".format(i)) for i in + range(num_aspect)] + + W_prj_i = [tf.Variable(tf.truncated_normal([num_filters, num_filters], mean=0, stddev=0.3), + name="item_project_W_{}".format(i)) for i in range(num_aspect)] + b_prj_i = [tf.Variable(tf.constant(0.1, shape=[1, 1, 1, num_filters, 1]), name="item_project_b_{}".format(i)) for i in + range(num_aspect)] + b_ij_i = [tf.constant(np.zeros([1, max_doc_length, 1], dtype=np.float32), name="item_bij_{}".format(i)) for i in + range(num_aspect)] + + user_viewpoint_words = [] + item_viewpoint_words = [] + for i in range(num_aspect): + user_viewpoint_words.append(asps_prj(user_word_dim, user_pre_attn_contextual_embeds[i], W_prj_u[i])) + item_viewpoint_words.append(asps_prj(item_word_dim, item_pre_attn_contextual_embeds[i], W_prj_i[i])) + + user_viewpoint_embeddings = [] + item_aspect_embeddings = [] + for i in range(len(W_prj_u)): + u_viewpoint_v = simple_self_attn(user_word_dim, user_viewpoint_words[i], users_masks_inputs, + b_prj_u[i], b_ij_u[i], b_inputs) + user_viewpoint_embeddings.append(u_viewpoint_v) + for i in range(len(W_prj_i)): + i_asps_v = simple_self_attn(item_word_dim, item_viewpoint_words[i], items_masks_inputs, + b_prj_i[i], b_ij_i[i], b_inputs) + item_aspect_embeddings.append(i_asps_v) + + + # 6. Generating Aspect Pairs + aspect_embeds_pack = [] + for i in range(num_aspect): + user_asps_embed = user_viewpoint_embeddings[i] + for j in range(num_aspect): + item_asps_embed = item_aspect_embeddings[j] + aspect_embeds_pack.append(pack_vects(user_asps_embed, item_asps_embed, "both")) + aspect_pairs = tf.concat(aspect_embeds_pack, 1) + num_intrn = int(aspect_pairs.get_shape()[1]) + cap1_latent_dim = int(aspect_pairs.get_shape()[-1]) + + + # 7. Joint-Attention + ATTENTION_SIZE=50 + with tf.name_scope('Attention_layer'): + attention_output, alphas = attention(aspect_pairs, ATTENTION_SIZE, return_alphas=True) + tf.summary.histogram('alphas', alphas) + print("attention_output->>>>>> ",attention_output) + + + # 8. Final Representation using FM and FNN + W_mlp = tf.Variable(tf.random_normal([num_filters*2, latent_dim], mean=0, stddev=0.02), name="review_W_mlp") + W_mlp = tf.nn.dropout(W_mlp, dropout_rate) + b_mlp = tf.Variable(tf.constant(0.1, shape=[latent_dim]), name="review_b_mlp") + + aspect_pairs_embeds = tf.nn.relu(tf.matmul(attention_output, W_mlp) + b_mlp) + print("aspect_pairs_embeds->>>>>> ",aspect_pairs_embeds) + + w_0 = tf.Variable(tf.zeros(1), name="review_w_0") + w_1 = tf.Variable(tf.truncated_normal([1, latent_dim], stddev=0.3), name="review_w_1") + v = tf.Variable(tf.truncated_normal([latent_dim, v_dim], stddev=0.3), name="review_v") + + J_1 = w_0 + tf.matmul(aspect_pairs_embeds, w_1, transpose_b=True) + print("J_1->>>>>> ",J_1) + + embeds_1 = tf.expand_dims(aspect_pairs_embeds, -1) + embeds_2 = tf.expand_dims(aspect_pairs_embeds, 1) + + + J_2 = tf.reduce_sum(tf.reduce_sum(tf.multiply(tf.matmul(embeds_1, embeds_2), tf.matmul(v, v, transpose_b=True)),2), 1, keepdims=True) + J_3 = tf.trace(tf.multiply(tf.matmul(embeds_1, embeds_2), tf.matmul(v, v, transpose_b=True))) + J_total = (J_1 + 0.5 * (J_2 - J_3)) + + # 预测评分 + predict_rating = J_total + user_bs + item_bs + loss = tf.reduce_mean(tf.squared_difference(predict_rating, ratings)) + + #print("predict_rating->>>>>> ",predict_rating) + + loss +=lambda_1 * (tf.nn.l2_loss(rand_matrix) + tf.nn.l2_loss(user_bs) + tf.nn.l2_loss(item_bs))+ tf.nn.l2_loss(W_conv_u)+tf.nn.l2_loss(W_conv_i)+tf.nn.l2_loss(W_word_gate_u)+tf.nn.l2_loss(W_word_gate_i)+tf.nn.l2_loss(W_prj_u)+tf.nn.l2_loss(W_prj_i) + #print("loss->>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>",loss) + + +# Model training and evaluation +train_step = tf.train.RMSPropOptimizer(learning_rate).minimize(loss) + +with tf.Session() as sess: + sess.run(tf.global_variables_initializer()) + + for e in range(epochs): + t = time() + loss_total = 0.0 + count = 0.0 + for i in range(int(math.ceil(len(user_input) / float(batch_size)))): + user_batch, item_batch, user_input_batch, item_input_batch, user_mask_batch, item_mask_batch, rates_batch= get_train_instance_batch( + i, batch_size, user_input, item_input, rateings,user_reviews, item_reviews, user_masks, item_masks) + _, loss_val = sess.run([train_step, loss], + feed_dict={users: user_batch, items: item_batch, + users_inputs: user_input_batch, + items_inputs: item_input_batch, + users_masks_inputs: user_mask_batch, + items_masks_inputs: item_mask_batch, + ratings: rates_batch, + b_inputs: b_train, + dropout_rate: drop_out}) + loss_total += loss_val + count += 1.0 + t1 = time() + mses, maes = [], [] + for i in range(len(user_input_test)): + b_test = np.zeros([len(user_input_test[i]),1, 1]) + eval_model(users, items, users_inputs, items_inputs, users_masks_inputs, items_masks_inputs, + dropout_rate, b_inputs, predict_rating, sess, user_test[i], + item_test[i], user_input_test[i], + item_input_test[i], user_mask_test[i], item_mask_test[i], rating_input_test[i], b_test, mses, maes) + + mse = np.array(mses).mean() + mae = np.array(maes).mean() + t2 = time() + print ("epoch%d time: %.3fs loss: %.3f test time: %.3fs mse: %.3f mae: %.3f"%(e, (t1 - t),loss_total/count,(t2 - t1), mse, mae)) + + saver = tf.train.Saver() + saver.save(sess, "model/model_{}/epoch{}".format(e, e)) + + # 将对应的变量写入文件中 + f = open('var_epoch_{}.txt'.format(e), 'a') + for i in range(len(user_input_test)): + b_test = np.zeros([len(user_input_test[i]),1, 1]) + predicts = sess.run(predict_rating, feed_dict={users: user_test[i], items: item_test[i], users_inputs: user_input_test[i], + items_inputs: item_input_test[i], users_masks_inputs: user_mask_test[i], + items_masks_inputs: item_mask_test[i], b_inputs: b_test, + dropout_rate: 1.0}) + f.write("user items predict_rating") + + for (x, y, z) in zip(user_test[i], item_test[i], predicts.tolist()): + print(x, y, z[0], file=f) + + print(rand_matrix, file=f) + + f.close() +# break + + # 保存最终的模型 +# saver.save(sess, "model/v1")