Recommender Systems Using Amazon Reviews – Data Science

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Recommender System Using Amazon Reviews

In the digital age, recommender systems play a vital role in enhancing user experiences and driving engagement. Leveraging the wealth of information available in Amazon reviews, we can construct a powerful and advanced recommender system that delivers personalized recommendations to users. In this extensive project guide, we will explore the step-by-step process of building such a system, utilizing relevant keywords, and providing detailed explanations to facilitate the development of your recommender system. We are going to make Recommender Systems Using Amazon Reviews project.

Recommender Systems Using Amazon Reviews: Overview and Significance

Recommender systems have emerged as indispensable tools in today’s digital landscape, providing personalized recommendations to users across various platforms. These systems aim to enhance user experiences, drive engagement, and facilitate decision-making processes. By analyzing user preferences and historical interactions, recommender systems offer tailored suggestions that match individual needs and interests.

The significance of personalized recommendations cannot be overstated. In an era of information overload, users often face decision fatigue when confronted with numerous options. Recommender systems alleviate this burden by filtering through vast amounts of available content and presenting users with highly relevant and personalized recommendations. This enhances user satisfaction, encourages the exploration of new products or services, and fosters customer loyalty.

Leveraging Amazon Reviews for Personalized Recommendations

Amazon, one of the world’s largest online marketplaces, provides a rich source of user-generated content in the form of customer reviews. Leveraging these reviews can significantly enhance the effectiveness of recommender systems. Amazon reviews offer valuable insights into users’ opinions, preferences, and experiences with products or services.

By mining and analyzing Amazon reviews, we can extract useful information such as sentiment analysis, product features, and user feedback. This data serves as a foundation for building powerful recommendation engines that understand user preferences on a granular level. By incorporating Amazon reviews into the recommender system, we tap into a vast and diverse pool of opinions and experiences, allowing us to deliver more accurate, personalized, and context-aware recommendations.

Amazon reviews encompass a wide range of products and domains, providing a comprehensive dataset for training and evaluating recommender systems. Leveraging this data allows us to overcome the cold-start problem, where new or less-reviewed items lack sufficient data for traditional recommender systems. With Amazon reviews, we can bridge this gap by inferring user preferences based on similar items or leveraging textual information to understand user preferences more comprehensively.

In summary, leveraging Amazon reviews empowers recommender systems to deliver highly personalized recommendations. By harnessing the collective wisdom of users, we can build advanced recommendation engines that cater to individual tastes and preferences. The following sections will delve into the technical aspects of building such a system, utilizing Amazon reviews as a valuable resource to create a more tailored and effective recommendation experience for users.

Recommender Systems Using Amazon Reviews: Understanding

To lay the foundation, we will delve into the various types of recommender systems. Collaborative filtering, content-based filtering, and hybrid approaches are the key techniques we’ll explore. Collaborative filtering leverages user behavior patterns to generate recommendations, while content-based filtering focuses on item attributes and user preferences. Hybrid approaches combine the strengths of both methods to provide accurate and diverse recommendations. Real-world examples will showcase the effectiveness of each approach.

Recommender Systems Using Amazon Reviews: Types

There are multiple types of Recommender Systems.

Recommender Systems Using Amazon Reviews: Collaborative Filtering

Collaborative filtering is a widely used technique in recommender systems that leverages user behavior and preferences to make recommendations. It analyzes the interactions and patterns of multiple users to identify similar interests and preferences. Collaborative filtering can be further classified into two main approaches:

  1. User-based Collaborative Filtering: This approach identifies users with similar preferences and recommends items that similar users have shown interest in. By finding users who have exhibited similar behaviors or item ratings, this method generates recommendations based on the preferences of like-minded users.
  2. Item-based Collaborative Filtering: In this approach, the focus is on identifying similar items rather than similar users. It looks for items that have been rated or interacted with similarly by users and recommends items that are similar to those previously consumed or appreciated.

Recommender Systems Using Amazon Reviews: Content-based Filtering

Content-based filtering focuses on the characteristics and attributes of items being recommended. It considers item features, such as product descriptions, categories, or keywords, to establish relationships between items and user preferences. Content-based filtering utilizes machine learning techniques to analyze item attributes and user profiles. Recommendations are made by identifying items that match the user’s previous interactions or preferences.

Recommender Systems Using Amazon Reviews: Hybrid Approaches

Hybrid approaches combine the strengths of collaborative filtering and content-based filtering to overcome their limitations and provide more accurate and diverse recommendations. These approaches aim to leverage the complementary nature of both techniques. Integrating collaborative filtering and content-based filtering allows hybrid recommender systems to achieve better recommendation accuracy, handle data sparsity issues, and address the cold-start problem.

Recommender Systems Using Amazon Reviews: Real-world Examples

Recommender systems have become ubiquitous across various industries and platforms. Here are some real-world examples of successful recommender systems:

  1. Netflix: Netflix employs a sophisticated recommender system that combines collaborative filtering and content-based approaches. By analyzing user viewing history, ratings, and content features, Netflix recommends movies and TV shows tailored to individual preferences.
  2. Amazon: Amazon’s recommendation engine is a prime example of collaborative filtering. It suggests products based on a user’s purchase history, browsing behavior, and ratings. Additionally, Amazon leverages content-based filtering by considering item attributes such as product category, brand, and customer reviews to provide personalized recommendations.
  3. Spotify: Spotify utilizes collaborative filtering to recommend music based on users’ listening history, liked songs, and playlists. It also incorporates content-based filtering by analyzing music genres, artist similarities, and user preferences to offer personalized music recommendations.
  4. YouTube: YouTube’s recommender system relies on collaborative filtering to suggest videos based on user viewing patterns, subscriptions, and likes. It also incorporates content-based features, such as video metadata, titles, descriptions, and tags, to deliver relevant recommendations.

These real-world examples illustrate the effectiveness of different recommender system approaches in providing personalized recommendations to users, leading to increased user engagement and satisfaction.

Recommender Systems Using Amazon Reviews: Data Acquisition and Preprocessing

The journey begins by acquiring Amazon review data. We will explore different strategies, including utilizing APIs, web scraping techniques, and accessing public datasets. Once we have obtained the data, preprocessing becomes crucial. We will discuss techniques for data cleaning, handling noise, duplicates, and outliers. Additionally, text normalization and spell-checking methods will be employed to ensure high-quality data.

Recommender Systems Using Amazon Reviews: Acquiring Amazon Review Data

Utilizing APIs

Acquiring Amazon review data can be done by utilizing APIs provided by Amazon. These APIs allow access to a wealth of review data, enabling developers to retrieve reviews based on specific products, categories, or search queries. By leveraging Amazon APIs, you can programmatically fetch review data in a structured and efficient manner.

Web Scraping Techniques

Web scraping is another approach to acquiring Amazon review data. It involves extracting information from web pages by automatically navigating through the site’s HTML structure. With web scraping, you can retrieve reviews from product pages, analyze multiple products, and gather a broader range of review data. However, it is crucial to respect the website’s terms of service and ensure proper data usage and compliance with legal requirements.

Accessing Public Datasets

In some cases, public datasets containing Amazon review data may already be available. These datasets are often curated and made accessible for research and development purposes. By accessing such datasets, you can save time and effort in data collection and focus more on the preprocessing and analysis stages of the project.

Recommender Systems Using Amazon Reviews: Preprocessing Amazon Reviews

Data Cleaning and Noise Handling

Before analyzing the acquired Amazon review data, it is essential to perform data cleaning to ensure data quality and remove noise. This involves processes such as removing HTML tags, punctuation, and special characters. Additionally, eliminating irrelevant information like product details, timestamps, or user identifiers that are unnecessary for the recommender system can help streamline the dataset. Handling missing values, addressing inconsistent formatting, and standardizing the data are also crucial steps in data cleaning.

Duplicate and Outlier Detection

Duplicate reviews can skew analysis and recommendation results. Identifying and removing duplicate reviews ensures that each review contributes unique information to the recommender system. Outliers, which may include extremely positive or negative reviews, should be handled carefully as they can significantly impact recommendation accuracy. Consider employing outlier detection techniques to identify and handle such reviews appropriately.

Text Normalization and Spell Checking

To improve the quality and consistency of textual data, text normalization techniques can be applied. This includes steps such as converting text to lowercase, removing stop words (common words with little semantic meaning), and stemming (reducing words to their root form). Spell checking can also be performed to correct misspelled words, which can impact the accuracy of natural language processing and sentiment analysis techniques applied later in the project.

By acquiring clean and well-preprocessed Amazon review data, you can ensure the reliability and accuracy of subsequent analysis and modeling steps. The quality of the data acquired and the preprocessing stage’s effectiveness significantly impact the recommender system’s overall performance.

Recommender Systems Using Amazon Reviews: Feature Extraction and Representation

Extracting meaningful features from Amazon reviews is a pivotal step. We will explore various techniques for text representation, such as bag-of-words, TF-IDF, and n-grams. These methods capture the essence of the review text and enable effective comparison and analysis. Moreover, incorporating metadata, including product categories, ratings, and helpful votes, will enrich the feature representation, leading to more accurate recommendations.

Text Representation Techniques

Bag-of-Words

The bag-of-words (BoW) technique is a popular text representation method that converts text documents, such as Amazon reviews, into numerical feature vectors. It involves constructing a vocabulary of unique words from the corpus and representing each document as a vector, where each element corresponds to the frequency or presence of a specific word in the document. This approach disregards the order and structure of words but captures the occurrence patterns, enabling comparison and analysis of text data.

TF-IDF (Term Frequency-Inverse Document Frequency)

TF-IDF is another widely used text representation technique that considers the importance of words in a document relative to the entire corpus. It assigns weights to words based on their frequency in the document (term frequency) and their rarity across the corpus (inverse document frequency). By multiplying the term frequency by the inverse document frequency, TF-IDF assigns higher weights to words that are more specific to a particular document, highlighting their importance in capturing the document’s content.

N-grams

N-grams represent contiguous sequences of words in a document. By considering multiple adjacent words, n-grams capture more contextual information compared to individual comments. Commonly used n-gram representations include unigrams (single words), bigrams (two consecutive words), and trigrams (three consecutive terms). N-grams can provide insights into phrase-level information and capture specific linguistic patterns that might be missed by considering individual words alone.

Incorporating Metadata

Product Categories

In addition to the textual content of Amazon reviews, metadata such as product categories can be valuable for enhancing the recommendation process. Product categories provide a higher-level classification of items and can help capture the overall domain or genre of a product. By incorporating product categories into the feature representation, the recommender system can consider the similarity between items within the same category when making recommendations.

Ratings and Helpful Votes

Metadata related to user interactions with reviews, such as ratings and helpful votes, can also play a crucial role in feature representation. Ratings reflect the user’s sentiment or preference towards a particular item, allowing the recommender system to consider user sentiment when generating recommendations. Helpful votes indicate the perceived usefulness of a review by other users, and incorporating this information can help identify influential or trusted reviews for better recommendation accuracy.

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By leveraging text representation techniques like Bag-of-Words, TF-IDF, and N-grams, and incorporating metadata such as product categories, ratings, and helpful votes, the recommender system gains a comprehensive understanding of the reviews and associated items. This enables the system to capture both the textual content and the context in which the reviews and items exist, leading to more accurate and tailored recommendations for users.

Recommender Systems Using Amazon Reviews: Building Recommender Models

This section will dive into the heart of the project—the construction of recommender models. Collaborative filtering models, including user-based and item-based approaches, will be implemented. We will explore matrix factorization techniques like singular value decomposition (SVD) and non-negative matrix factorization (NMF) to uncover latent factors and generate recommendations. Content-based filtering models will be built, leveraging textual features extracted from reviews. Techniques such as cosine similarity, TF-IDF weighting, and term frequency-inverse document frequency (TF-IDF) will be applied. Hybrid recommender models, combining collaborative and content-based filtering, will be developed, leveraging weighted hybrid models, cascading approaches, and ensemble methods for optimal recommendations.

Collaborative Filtering Models

User-based Collaborative Filtering

User-based collaborative filtering is a widely used technique in recommender systems. It identifies similar users based on their preferences and behaviors and recommends items that those similar users have shown interest in. By finding users with similar tastes, this approach leverages the wisdom of the crowd to generate recommendations. User-based collaborative filtering is effective in situations where user preferences play a significant role in determining recommendations.

Item-based Collaborative Filtering

Item-based collaborative filtering focuses on the similarity between items instead of users. It identifies items that have been interacted with similarly by users and recommends items that are similar to those that a user has already consumed or appreciated. By leveraging item-item similarity, this approach can provide personalized recommendations even in cases where user preferences are less clear or data sparsity is an issue.

Matrix Factorization Techniques (SVD, NMF)

Matrix factorization techniques, such as Singular Value Decomposition (SVD) and Non-Negative Matrix Factorization (NMF), are widely used in collaborative filtering models. These techniques factorize the user-item interaction matrix into lower-dimensional latent factors. By decomposing the matrix, they uncover underlying patterns and relationships between users and items. SVD and NMF help in capturing latent user preferences and item characteristics, enabling accurate recommendations.

Content-based Filtering Models

Cosine Similarity

Cosine similarity is a popular technique used in content-based filtering models. It measures the similarity between two items based on their feature vectors. In the context of recommender systems, feature vectors can represent item attributes such as product descriptions, categories, or keywords. By calculating the cosine similarity between items, this approach identifies items with similar characteristics and recommends items that align with the user’s preferences.

TF-IDF Weighting

TF-IDF (Term Frequency-Inverse Document Frequency) is commonly used in content-based filtering models to weigh the importance of words or features in a document. It assigns higher weights to words that are frequent in a specific document but relatively rare across the entire corpus. By incorporating TF-IDF weighting, content-based models can effectively capture the discriminative power of words and prioritize significant features for recommendation purposes.

Term Frequency-Inverse Document Frequency (TF-IDF)

Term Frequency-Inverse Document Frequency (TF-IDF) is another essential technique in content-based filtering models. It calculates the frequency of a term in a document (term frequency) and adjusts it based on the inverse document frequency. TF-IDF takes into account both the importance of a term in a document and its rarity in the entire corpus. By applying TF-IDF weighting to terms or features, content-based models can emphasize relevant and discriminative aspects for accurate recommendations.

Hybrid Recommender Models

Weighted Hybrid Models

Weighted hybrid models combine collaborative filtering and content-based filtering by assigning weights to recommendations from each approach. These models leverage the strengths of both techniques and provide a more balanced and comprehensive set of recommendations. By considering the preferences derived from collaborative filtering and the relevance determined by content-based filtering, weighted hybrid models aim to enhance recommendation accuracy and coverage.

Cascading Approaches

Cascading approaches in hybrid recommender systems apply a sequential process to generate recommendations. They start with one filtering technique, such as collaborative filtering, to generate an initial set of recommendations. Then, these recommendations are filtered further using a content-based filtering technique. This cascading process refines and improves the recommendations by combining the benefits of both techniques in a controlled manner.

Ensemble Methods

Ensemble methods combine the predictions of multiple recommender models to generate recommendations. These methods leverage the diversity of individual models to overcome biases and enhance recommendation accuracy. By combining the outputs of different models, such as collaborative filtering and content-based filtering, ensemble methods aim to capture a broader range of user preferences and provide robust and diverse recommendations.

By employing collaborative filtering models, content-based filtering models, or hybrid recommender models, developers can leverage different techniques to build effective recommendation systems. These models offer flexibility and customization options to address specific recommendation requirements and cater to the diverse needs of users.

Recommender Systems Using Amazon Reviews: Evaluation and Performance Metrics

To assess the effectiveness of the recommender system, evaluation techniques, and performance metrics are essential. We will explore evaluation methodologies such as holdout, cross-validation, and leave-one-out. Metrics such as precision, recall, F1 score, mean average precision (MAP), and normalized discounted cumulative gain (NDCG) will be employed to measure recommendation quality and effectiveness.

Evaluation Techniques

Holdout

The holdout method is a commonly used evaluation technique in recommender systems. It involves splitting the available data into a training set and a test set. The model is trained on the training set, and its performance is then evaluated on the test set. The holdout method provides a straightforward approach to measure how well the recommender system performs on unseen data.

Cross-Validation

Cross-validation is another evaluation technique that addresses the concern of limited data availability. It involves partitioning the data into multiple subsets or “folds.” The model is trained and evaluated iteratively, with each fold serving as the test set while the remaining folds are used for training. By averaging the results from multiple iterations, cross-validation provides a more robust estimate of the model’s performance.

Leave-One-Out

Leave-One-Out (LOO) is a specialized form of cross-validation where each data point is treated as a separate fold. In each iteration, one data point is left out for testing, and the rest of the data is used for training. LOO provides a comprehensive evaluation by utilizing all available data points as test cases. However, it can be computationally expensive and may not be feasible for larger datasets.

Performance Metrics

Precision, Recall, F1 Score

Precision, recall, and F1 score are commonly used performance metrics for recommender systems:

  • Precision measures the proportion of relevant items among the recommended items. It focuses on the accuracy of the recommendations by assessing how many recommended items are truly relevant to the user’s preferences.
  • Recall measures the proportion of relevant items that are successfully recommended. It assesses the system’s ability to retrieve all relevant items, capturing the comprehensiveness of the recommendations.
  • F1 score is the harmonic mean of precision and recall, providing a balanced measure that considers both precision and recall. It combines the precision and recall values into a single metric, offering a consolidated assessment of the system’s performance.

Mean Average Precision (MAP)

Mean Average Precision (MAP) is a metric commonly used in information retrieval tasks, including recommender systems. It calculates the average precision across different levels of recall. MAP takes into account both precision and the order of recommendations. It is particularly useful when dealing with scenarios where the position or ranking of the recommended items is important.

Normalized Discounted Cumulative Gain (NDCG)

Normalized Discounted Cumulative Gain (NDCG) is a metric that evaluates the ranking quality of the recommendations. It considers both the relevance of the recommended items and their positions in the ranked list. NDCG assigns higher scores to recommendations that are both relevant and ranked higher. It is a valuable metric for assessing the overall quality and effectiveness of the recommendation list.

By utilizing evaluation techniques such as holdout, cross-validation, or leave-one-out, and performance metrics such as precision, recall, F1 score, MAP, and NDCG, developers can assess and compare the performance of recommender systems. These metrics help in understanding how well the system is performing, identifying areas for improvement, and optimizing the recommendation algorithms and strategies.

Recommender System Using Amazon Reviews: Code

You can find the code and dataset on the GitHub page.

1: Import Libraries

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
import math
import json
import time
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from sklearn.externals import joblib
import scipy.sparse
from scipy.sparse import csr_matrix
from scipy.sparse.linalg import svds
import warnings; warnings.simplefilter('ignore')
%matplotlib inline

for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# Any results you write to the current directory are saved as output.

2: Load the Dataset and Add headers

electronics_data=pd.read_csv("/kaggle/input/amazon-product-reviews/ratings_Electronics (1).csv",names=['userId', 'productId','Rating','timestamp'])
# Display the data

electronics_data.head()
#Shape of the data
electronics_data.shape
#Taking subset of the dataset
electronics_data=electronics_data.iloc[:1048576,0:]
#Check the datatypes
electronics_data.dtypes
electronics_data.info()
#Five point summary 

electronics_data.describe()['Rating'].T
#Find the minimum and maximum ratings
print('Minimum rating is: %d' %(electronics_data.Rating.min()))
print('Maximum rating is: %d' %(electronics_data.Rating.max()))

3: Handling Missing values

#Check for missing values
print('Number of missing values across columns: \n',electronics_data.isnull().sum())
# Check the distribution of the rating
with sns.axes_style('white'):
    g = sns.factorplot("Rating", data=electronics_data, aspect=2.0,kind='count')
    g.set_ylabels("Total number of ratings")

4: Unique Users and products

print("Total data ")
print("-"*50)
print("\nTotal no of ratings :",electronics_data.shape[0])
print("Total No of Users   :", len(np.unique(electronics_data.userId)))
print("Total No of products  :", len(np.unique(electronics_data.productId)))

5: Dropping the TimeStamp Column

#Dropping the Timestamp column

electronics_data.drop(['timestamp'], axis=1,inplace=True)

6: Analyzing the rating

#Analysis of rating given by the user 

no_of_rated_products_per_user = electronics_data.groupby(by='userId')['Rating'].count().sort_values(ascending=False)

no_of_rated_products_per_user.head()
no_of_rated_products_per_user.describe()
quantiles = no_of_rated_products_per_user.quantile(np.arange(0,1.01,0.01), interpolation='higher')
plt.figure(figsize=(10,10))
plt.title("Quantiles and their Values")
quantiles.plot()
# quantiles with 0.05 difference
plt.scatter(x=quantiles.index[::5], y=quantiles.values[::5], c='orange', label="quantiles with 0.05 intervals")
# quantiles with 0.25 difference
plt.scatter(x=quantiles.index[::25], y=quantiles.values[::25], c='m', label = "quantiles with 0.25 intervals")
plt.ylabel('No of ratings by user')
plt.xlabel('Value at the quantile')
plt.legend(loc='best')
plt.show()
print('\n No of rated product more than 50 per user : {}\n'.format(sum(no_of_rated_products_per_user >= 50)) )

7: Popularity-Based Recommendation

#Getting the new dataframe which contains users who has given 50 or more ratings

new_df=electronics_data.groupby("productId").filter(lambda x:x['Rating'].count() >=50)
no_of_ratings_per_product = new_df.groupby(by='productId')['Rating'].count().sort_values(ascending=False)

fig = plt.figure(figsize=plt.figaspect(.5))
ax = plt.gca()
plt.plot(no_of_ratings_per_product.values)
plt.title('# RATINGS per Product')
plt.xlabel('Product')
plt.ylabel('No of ratings per product')
ax.set_xticklabels([])

plt.show()
#Average rating of the product 

new_df.groupby('productId')['Rating'].mean().head()
new_df.groupby('productId')['Rating'].mean().sort_values(ascending=False).head()
#Total no of rating for product

new_df.groupby('productId')['Rating'].count().sort_values(ascending=False).head()
ratings_mean_count = pd.DataFrame(new_df.groupby('productId')['Rating'].mean())
ratings_mean_count['rating_counts'] = pd.DataFrame(new_df.groupby('productId')['Rating'].count())
ratings_mean_count.head()
ratings_mean_count['rating_counts'].max()
plt.figure(figsize=(8,6))
plt.rcParams['patch.force_edgecolor'] = True
ratings_mean_count['rating_counts'].hist(bins=50)
plt.figure(figsize=(8,6))
plt.rcParams['patch.force_edgecolor'] = True
ratings_mean_count['Rating'].hist(bins=50)
plt.figure(figsize=(8,6))
plt.rcParams['patch.force_edgecolor'] = True
sns.jointplot(x='Rating', y='rating_counts', data=ratings_mean_count, alpha=0.4)
popular_products = pd.DataFrame(new_df.groupby('productId')['Rating'].count())
most_popular = popular_products.sort_values('Rating', ascending=False)
most_popular.head(30).plot(kind = "bar")

8: Collaborative filtering (Item-Item recommendation)

from surprise import KNNWithMeans
from surprise import Dataset
from surprise import accuracy
from surprise import Reader
import os
from surprise.model_selection import train_test_split
#Reading the dataset
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(new_df,reader)
#Splitting the dataset
trainset, testset = train_test_split(data, test_size=0.3,random_state=10)
# Use user_based true/false to switch between user-based or item-based collaborative filtering
algo = KNNWithMeans(k=5, sim_options={'name': 'pearson_baseline', 'user_based': False})
algo.fit(trainset)
# run the trained model against the testset
test_pred = algo.test(testset)
test_pred
# get RMSE
print("Item-based Model : Test Set")
accuracy.rmse(test_pred, verbose=True)

9: Model-based collaborative filtering system

new_df1=new_df.head(10000)
ratings_matrix = new_df1.pivot_table(values='Rating', index='userId', columns='productId', fill_value=0)
ratings_matrix.head()
ratings_matrix.shape
X = ratings_matrix.T
X.head()
X.shape
X1 = X
#Decomposing the Matrix
from sklearn.decomposition import TruncatedSVD
SVD = TruncatedSVD(n_components=10)
decomposed_matrix = SVD.fit_transform(X)
decomposed_matrix.shape
#Correlation Matrix

correlation_matrix = np.corrcoef(decomposed_matrix)
correlation_matrix.shape
X.index[75]
i = "B00000K135"

product_names = list(X.index)
product_ID = product_names.index(i)
product_ID
correlation_product_ID = correlation_matrix[product_ID]
correlation_product_ID.shape
Recommend = list(X.index[correlation_product_ID > 0.65])

# Removes the item already bought by the customer
Recommend.remove(i) 

Recommend[0:24]

Conclusion

In this project, we embarked on a comprehensive journey to build an advanced recommender system using Amazon reviews. We began by understanding the significance of personalized recommendations in enhancing user experiences and driving engagement. Leveraging the vast amount of data available in Amazon reviews, we explored different techniques and methodologies to create a powerful recommendation engine that caters to individual user preferences.

We covered various aspects, starting from data acquisition and preprocessing, where we discussed methods such as utilizing APIs, web scraping, and accessing public datasets. The acquired data was then subjected to preprocessing steps like data cleaning, noise handling, duplicate detection, and text normalization to ensure high-quality input for the recommender system.

Feature extraction and representation played a crucial role in capturing the essence of Amazon reviews. Text representation techniques like bag-of-words, TF-IDF, and n-grams were employed to convert textual data into numerical feature vectors. Additionally, we explored the incorporation of metadata, including product categories, ratings, and helpful votes, to enrich the feature representation and enhance recommendation accuracy.

Building recommender models was a pivotal stage in the project. We delved into collaborative filtering models, including user-based and item-based approaches, as well as matrix factorization techniques like SVD and NMF. Content-based filtering models utilizing techniques like cosine similarity, TF-IDF weighting, and term frequency-inverse document frequency (TF-IDF) were also explored. Hybrid recommender models that combined collaborative filtering and content-based filtering techniques were discussed, including weighted hybrid models, cascading approaches, and ensemble methods.

 

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