language: en
tags:
- Recommendation
license: apache-2.0
datasets:
- surprise
- numpy
- keras
- pandas
thumbnail: https://github.com/Marcosdib/S2Query/Classification_Architecture_model.png
MCTI Text Classification Task (uncased) DRAFT
Disclaimer: The Brazilian Ministry of Science, Technology, and Innovation (MCTI) has partially supported this project.
The model NLP MCTI Recommendation Multi is part of the project Research Financing Product Portfolio (FPP) focuses on the task of Recommendation and explores different machine learning strategies that provide suggestions of items that are likely to be handy for a particular individual. Several methods were faced against each other to compare the error estimatives. Using LDA model, a simulated dataset was created.
According to the abstract,
XXXXX "Using transfer learning to classify long unstructured texts with small amounts of labeled data".
Model description
The surprise library provides 11 classifier models that try to predict the classification of training data based on several different collaborative-filtering techniques. The models provided with a brief explanation in English are mentioned below, for more information please refer to the package documentation.
random_pred.NormalPredictor: Algorithm predicting a random rating based on the distribution of the training set, which is assumed to be normal.
baseline_only.BaselineOnly: Algorithm predicting the baseline estimate for given user and item.
knns.KNNBasic: A basic collaborative filtering algorithm.
knns.KNNWithMeans: A basic collaborative filtering algorithm, taking into account the mean ratings of each user.
knns.KNNWithZScore: A basic collaborative filtering algorithm, taking into account the z-score normalization of each user.
knns.KNNBaseline: A basic collaborative filtering algorithm taking into account a baseline rating.
matrix_factorization.SVD: The famous SVD algorithm, as popularized by Simon Funk during the Netflix Prize.
matrix_factorization.SVDpp: The SVD++ algorithm, an extension of SVD taking into account implicit ratings.
matrix_factorization.NMF: A collaborative filtering algorithm based on Non-negative Matrix Factorization.
slope_one.SlopeOne: A simple yet accurate collaborative filtering algorithm.
co_clustering.CoClustering: A collaborative filtering algorithm based on co-clustering.
Every model was used and evaluated. When faced with each other different methods presented different error estimatives.
Intended uses
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions of a task that interests you. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like XXX.
How to use
The datasets for collaborative filtering must be: - The dataframe containing the ratings. - It must have three columns, corresponding to the user (raw) ids, the item (raw) ids, and the ratings, in this order.
>>> import pandas as pd
>>> import numpy as np
class Data:
The databases (ml_100k, ml_1m and jester) are built-in the surprise package for collaborative-filtering
def_init_(self):
self.available_databases=['ml_100k', 'ml_1m','jester', 'lda_topics', 'lda_rankings', 'uniform']
def show_available_databases(self):
print('The avaliable database are:')
for i,database in enumerate(self.available_databases):
print(str(i)+': '+database)
def read_data(self,database_name):
self.database_name=database_name
self.the_data_reader= getattr(self, 'read_'+database_name.lower())
self.the_data_reader()
def read_ml_100k(self):
from surprise import Dataset
data = Dataset.load_builtin('ml-100k')
self.df = pd.DataFrame(data.__dict__['raw_ratings'], columns=['user_id','item_id','rating','timestamp'])
self.df.drop(columns=['timestamp'],inplace=True)
self.df.rename({'user_id':'userID','item_id':'itemID'},axis=1,inplace=True)
def read_ml_1m(self):
from surprise import Dataset
data = Dataset.load_builtin('ml-1m')
self.df = pd.DataFrame(data.__dict__['raw_ratings'], columns=['user_id','item_id','rating','timestamp'])
self.df.drop(columns=['timestamp'],inplace=True)
self.df.rename({'user_id':'userID','item_id':'itemID'},axis=1,inplace=True)
def read_jester(self):
from surprise import Dataset
data = Dataset.load_builtin('jester')
self.df = pd.DataFrame(data.__dict__['raw_ratings'], columns=['user_id','item_id','rating','timestamp'])
self.df.drop(columns=['timestamp'],inplace=True)
self.df.rename({'user_id':'userID','item_id':'itemID'},axis=1,inplace=True)
Hyperparameters -
n_users : number of simulated users in the database;
n_ratings : number of simulated rating events in the database.
This is a fictional dataset based in the choice of an uniformly distributed random rating(from 1 to 5) for one of the simulated users of the recommender-system that is being designed in this research project.
def read_uniform(self):
n_users = 20
n_ratings = 10000
import random
opo = pd.read_csv('../oportunidades.csv')
df = [(random.randrange(n_users), random.randrange(len(opo)), random.randrange(1,5)) for i in range(n_ratings)]
self.df = pd.DataFrame(df, columns = ['userID', 'itemID', 'rating'])
Hyperparameters -
n_users : number of simulated users in the database; n_ratings : number of simulated rating events in the database.
This first LDA based dataset builds a model with K = `n_users` topics. LDA topics
are used as proxies for simulated users with different clusters of interest. At first
a random opportunity is chosen, than the amount of a randomly chosen topic inside the description
is multiplied by five. The ceiling operation of this result is the rating that the fictional user
will give to that opportunity.
Because the amount of each topic predicted by the model is disollved among various topics,
it is very rare to find an opportunity that has a higher LDA value. The consequence is that this dataset
has really low volatility and the major part of ratings are equal to 1.
def read_lda_topics(self):
n_users = 20
n_ratings = 10000
import gensim
import random
import math
opo = pd.read_csv('../oportunidades_results.csv')
# opo = opo.iloc[np.where(opo['opo_brazil']=='Y')]
try:
lda_model = gensim.models.ldamodel.LdaModel.load(f'models/lda_model{n_users}.model')
except:
import generate_users
generate_users.gen_model(n_users)
lda_model = gensim.models.ldamodel.LdaModel.load(f'models/lda_model{n_users}.model')
df = []
for i in range(n_ratings):
opo_n = random.randrange(len(opo))
txt = opo.loc[opo_n,'opo_texto']
opo_bow = lda_model.id2word.doc2bow(txt.split())
topics = lda_model.get_document_topics(opo_bow)
topics = {topic[0]:topic[1] for topic in topics}
user = random.sample(topics.keys(), 1)[0]
rating = math.ceil(topics[user]*5)
df.append((user, opo_n, rating))
self.df = pd.DataFrame(df, columns = ['userID', 'itemID', 'rating'])
def read_lda_rankings(self):
n_users = 9
n_ratings = 1000
import gensim
import random
import math
import tqdm
opo = pd.read_csv('../oportunidades.csv')
opo = opo.iloc[np.where(opo['opo_brazil']=='Y')]
opo.index = range(len(opo))
path = f'models/output_linkedin_cle_lda_model_{n_users}_topics_symmetric_alpha_auto_beta'
lda_model = gensim.models.ldamodel.LdaModel.load(path)
df = []
pbar = tqdm.tqdm(total= n_ratings)
for i in range(n_ratings):
opo_n = random.randrange(len(opo))
txt = opo.loc[opo_n,'opo_texto']
opo_bow = lda_model.id2word.doc2bow(txt.split())
topics = lda_model.get_document_topics(opo_bow)
topics = {topic[0]:topic[1] for topic in topics}
prop = pd.DataFrame([topics], index=['prop']).T.sort_values('prop', ascending=True)
prop['rating'] = range(1, len(prop)+1)
prop['rating'] = prop['rating']/len(prop)
prop['rating'] = prop['rating'].apply(lambda x: math.ceil(x*5))
prop.reset_index(inplace=True)
prop = prop.sample(1)
df.append((prop['index'].values[0], opo_n, prop['rating'].values[0]))
pbar.update(1)
pbar.close()
self.df = pd.DataFrame(df, columns = ['userID', 'itemID', 'rating'])
Limitations and bias
In this model we have faced some obstacles that we had overcome, but some of those, by the nature of the project, couldn't be totally solved. Due the fact that our dataset was build it by ourselves, there was no interaction yet between a user and the dataset, therefore we don't have realistic ratings which made us have to generate a simulation, making the results less believable. Also in this part of the project, we have used a database of scrappings of linkedin profiles. The problem is that the profiles that linkedin shows is biased, so the profiles that appears was geographically closed, or related to the users organization and email.
Training data
To train the LDA model, we use a database of linkedin profiles
Training procedure
Preprocessing
Pre-processing was used to standardize the texts for the English language, reduce the number of insignificant tokens and optimize the training of the models. The following assumptions were considered:
- The Data Entry base is obtained from the result of goal 4.
- Labeling (Goal 4) is considered true for accuracy measurement purposes;
- Preprocessing experiments compare accuracy in a shallow neural network (SNN);
- Pre-processing was investigated for the classification goal. From the Database obtained in Meta 4, stored in the project's GitHub, a Notebook was developed in Google Colab to implement the pre-processing code, which also can be found on the project's GitHub. Several Python packages were used to develop the preprocessing code:
Table 3: Python packages used
| Objective | Package |
|---|---|
| Resolve contractions and slang usage in text | contractions |
| Natural Language Processing | nltk |
| Others data manipulations and calculations included in Python 3.10: io, json, math, re (regular expressions), shutil, time, unicodedata; | numpy |
| Data manipulation and analysis | pandas |
| http library | requests |
| Training model | scikit-learn |
| Machine learning | tensorflow |
| Machine learning | keras |
| Translation from multiple languages to English | translators |
| As detailed in the notebook on GitHub, in the pre-processing, code was created to build and evaluate 8 (eight) different | |
| bases, derived from the base of goal 4, with the application of the methods shown in Figure 2. |
Table 4: Preprocessing methods evaluated
| id | Experiments |
|---|---|
| Base | Original Texts |
| xp1 | Expand Contractions |
| xp2 | Expand Contractions + Convert text to lowercase |
| xp3 | Expand Contractions + Remove Punctuation |
| xp4 | Expand Contractions + Remove Punctuation + Convert text to lowercase |
| xp5 | xp4 + Stemming |
| xp6 | xp4 + Lemmatization |
| xp7 | xp4 + Stemming + Stopwords Removal |
| xp8 | ap4 + Lemmatization + Stopwords Removal |
| First, the treatment of punctuation and capitalization was evaluated. This phase resulted in the construction and | |
| evaluation of the first four bases (xp1, xp2, xp3, xp4). | |
| Then, the content simplification was evaluated, from the xp4 base, considering stemming (xp5), stemming (xp6), | |
| stemming + stopwords removal (xp7), and stemming + stopwords removal (xp8). | |
| All eight bases were evaluated to classify the eligibility of the opportunity, through the training of a shallow | |
| neural network (SNN – Shallow Neural Network). The metrics for the eight bases were evaluated. The results are | |
| shown in Table 5. |
Table 5: Results obtained in Preprocessing
| id | Experiment | acurácia | f1-score | recall | precision | Média(s) | N_tokens | max_lenght |
|---|---|---|---|---|---|---|---|---|
| Base | Original Texts | 89,78% | 84,20% | 79,09% | 90,95% | 417,772 | 23788 | 5636 |
| xp1 | Expand Contractions | 88,71% | 81,59% | 71,54% | 97,33% | 414,715 | 23768 | 5636 |
| xp2 | Expand Contractions + Convert text to lowercase | 90,32% | 85,64% | 77,19% | 97,44% | 368,375 | 20322 | 5629 |
| xp3 | Expand Contractions + Remove Punctuation | 91,94% | 87,73% | 79,66% | 98,72% | 386,650 | 22121 | 4950 |
| xp4 | Expand Contractions + Remove Punctuation + Convert text to lowercase | 90,86% | 86,61% | 80,85% | 94,25% | 326,830 | 18616 | 4950 |
| xp5 | xp4 + Stemming | 91,94% | 87,68% | 78,47% | 100,00% | 257,960 | 14319 | 4950 |
| xp6 | xp4 + Lemmatization | 89,78% | 85,06% | 79,66% | 91,87% | 282,645 | 16194 | 4950 |
| xp7 | xp4 + Stemming + Stopwords Removal | 92,47% | 88,46% | 79,66% | 100,00% | 210,320 | 14212 | 2817 |
| xp8 | ap4 + Lemmatization + Stopwords Removal | 92,47% | 88,46% | 79,66% | 100,00% | 225,580 | 16081 | 2726 |
| Even so, between these two excellent options, one can judge which one to choose. XP7: It has less training time, | ||||||||
| less number of unique tokens. XP8: It has smaller maximum sizes. In this case, the criterion used for the choice | ||||||||
| was the computational cost required to train the vector representation models (word-embedding, sentence-embeddings, | ||||||||
| document-embedding). The training time is so close that it did not have such a large weight for the analysis. | ||||||||
| As a last step, a spreadsheet was generated for the model (xp8) with the fields opo_pre and opo_pre_tkn, containing the preprocessed text in sentence format and tokens, respectively. This database was made | ||||||||
| available on the project's GitHub with the inclusion of columns opo_pre (text) and opo_pre_tkn (tokenized). |
Pretraining
The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer used is Adam with a learning rate of 1e-4, and , a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after.
Evaluation results
Model training with Word2Vec embeddings
Now we have a pre-trained model of word2vec embeddings that has already learned relevant meaningsfor our classification problem. We can couple it to our classification models (Fig. 4), realizing transferlearning and then training the model with the labeled data in a supervised manner. The new coupled model can be seen in Figure 5 under word2vec model training. The Table 3 shows the obtained results with related metrics. With this implementation, we achieved new levels of accuracy with 86% for the CNN architecture and 88% for the LSTM architecture.
Table 6: Results from Pre-trained WE + ML models
| ML Model | Accuracy | F1 Score | Precision | Recall |
|---|---|---|---|---|
| NN | 0.8269 | 0.8545 | 0.8392 | 0.8712 |
| DNN | 0.7115 | 0.7794 | 0.7255 | 0.8485 |
| CNN | 0.8654 | 0.9083 | 0.8486 | 0.9773 |
| LSTM | 0.8846 | 0.9139 | 0.9056 | 0.9318 |
Transformer-based implementation
Another way we used pre-trained vector representations was by use of a Longformer (Beltagy et al., 2020). We chose it because of the limitation of the first generation of transformers and BERT-based architectures involving the size of the sentences: the maximum of 512 tokens. The reason behind that limitation is that the self-attention mechanism scale quadratically with the input sequence length O(n2) (Beltagy et al., 2020). The Longformer allowed the processing sequences of a thousand characters without facing the memory bottleneck of BERT-like architectures and achieved SOTA in several benchmarks. For our text length distribution in Figure 3, if we used a Bert-based architecture with a maximum length of 512, 99 sentences would have to be truncated and probably miss some critical information. By comparison, with the Longformer, with a maximum length of 4096, only eight sentences will have their information shortened. To apply the Longformer, we used the pre-trained base (available on the link) that was previously trained with a combination of vast datasets as input to the model, as shown in figure 5 under Longformer model training. After coupling to our classification models, we realized supervised training of the whole model. At this point, only transfer learning was applied since more computational power was needed to realize the fine-tuning of the weights. The results with related metrics can be viewed in table 4. This approach achieved adequate accuracy scores, above 82% in all implementation architectures.
Table 7: Results from Pre-trained Longformer + ML models
| ML Model | Accuracy | F1 Score | Precision | Recall |
|---|---|---|---|---|
| NN | 0.8269 | 0.8754 | 0.7950 | 0.9773 |
| DNN | 0.8462 | 0.8776 | 0.8474 | 0.9123 |
| CNN | 0.8462 | 0.8776 | 0.8474 | 0.9123 |
| LSTM | 0.8269 | 0.8801 | 0.8571 | 0.9091 |
Checkpoints
- Examples
- Implementation Notes
- Usage Example
...
Config
Tokenizer
Benchmarks
| RMSE | MSE | MAE | FCP | |
|---|---|---|---|---|
| NormalPredictor | 1.820737 | 3.315084 | 1.475522 | 0.514134 |
| BaselineOnly | 1.072843 | 1.150992 | 0.890233 | 0.556560 |
| KNNBasic | 1.232248 | 1.518436 | 0.936799 | 0.648604 |
| KNNWithMeans | 1.124166 | 1.263750 | 0.808329 | 0.597148 |
| KNNWithZScore | 1.056550 | 1.116299 | 0.750004 | 0.669651 |
| KNNBaseline | 1.134660 | 1.287454 | 0.825161 | 0.614270 |
| SVD | 0.977468 | 0.955444 | 0.757485 | 0.723829 |
| SVDpp | 0.843065 | 0.710758 | 0.670516 | 0.671737 |
| NMF | 1.122684 | 1.260420 | 0.722101 | 0.688728 |
| SlopeOne | 1.073552 | 1.152514 | 0.747142 | 0.651937 |
| CoClustering | 1.293383 | 1.672838 | 1.007951 | 0.494174 |
BibTeX entry and citation info
@article{recommend22,
author ={Jo\~{a}o Gabriel de Moraes Souza. and Daniel Oliveira Cajueiro. and Johnathan de O. Milagres. and Vin\´{i}cius de Oliveira Watanabe. and V\´{i}tor Bandeira Borges. and Victor Rafael Celestino.},
title ={A comprehensive review of recommendation systems: method, data, evaluation and coding},
booktitle ={xxxx},
year ={xxxx},
pages ={xxxx},
publisher ={xxxx},
organization ={xxxx},
doi ={xxxx},
isbn ={xxxx},
issn ={xxxx},
}
