You are a machine learning specialist for a retail clothing company.
Your company receives a significant amount of its revenue from your retail website.
Your marketing team wishes to implement a recommendation feature for your customers that uses online and in-store shopping patterns, user preferences, and overall fashion trends to give suggestions to your customers for items to purchase.
Your dataset contains customer data such as demographics, prior visits, prior purchases, and location.
Your task is to develop a machine learning model that uses the customer data to enhance the user's experience with your website while also giving informed recommendations.
Which option best suits your task?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: B.
Option A is incorrect.
The Random Cut Forest (RCF) algorithm is better suited to anomaly detection in your data.
It is not a choice one would use for a recommendation engine.
Option B is correct.
Collaborative filtering is the preferred approach for building an online recommendation engine that leverages customer behavior data.
Option C is incorrect.
A Recurrent Neural Network (RNN) is best suited to forecasting scalar (one-dimensional) time series.
You don't have time-series data.
You have clickstream data from your website and customer purchasing patterns from in-store and online purchases.
Option D is incorrect.
The Neural Topic Model algorithm is best suited for organizing a corpus of documents into topics that contain word groupings based on their statistical distribution.
This type of document processing algorithm would not work as a recommendation engine for an online business.
References:
Please see the AWS SageMaker developer guide titled Use Amazon SageMaker Built-in Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html),
The AWS Media blog titled What's new in recommender systems (https://aws.amazon.com/blogs/media/whats-new-in-recommender-systems/),
The AWS Machine Learning blog titled Building a customized recommender system in Amazon SageMaker (https://aws.amazon.com/blogs/machine-learning/building-a-customized-recommender-system-in-amazon-sagemaker/),
The AWS Database blog titled Using collaborative filtering on Yelp data to build a recommendation system in Amazon Neptune (https://aws.amazon.com/blogs/database/using-collaborative-filtering-on-yelp-data-to-build-a-recommendation-system-in-amazon-neptune/)
The best option for the given task is B. Build a model based on collaborative filtering that leverages implicit feedback derived from user activity, such as clicks and views to identify patterns in the customer data.
Collaborative filtering is a technique commonly used for recommendation systems. It is based on the idea that people who have similar preferences for items in the past are likely to have similar preferences in the future. This approach is especially useful when there is no explicit information about customer preferences, such as ratings or reviews, and instead relies on implicit feedback, such as clicks and views.
Collaborative filtering can be implemented using two main approaches: user-based and item-based. In user-based collaborative filtering, similarities between users are calculated based on their preferences for items, and recommendations are made to a user based on the preferences of similar users. In item-based collaborative filtering, similarities between items are calculated based on the preferences of users who have interacted with those items, and recommendations are made based on the similarity of items to those that a user has interacted with.
In the given scenario, building a recommendation system based on collaborative filtering that leverages implicit feedback derived from user activity such as clicks and views would be a good fit. The dataset contains customer data such as demographics, prior visits, prior purchases, and location, which can be used to identify patterns in customer behavior and preferences. Collaborative filtering can help identify similar customer profiles and suggest items that customers with similar profiles have purchased or interacted with in the past.
The other options mentioned in the question are not the best fit for the given task. Random Cut Forest (RCF) algorithm (Option A) is used for anomaly detection in data, which is not the objective of the given task. Recurrent Neural Networks (RNN) using the DeepAR algorithm (Option C) are commonly used for time-series forecasting, and are not the best fit for the given scenario where the objective is to build a recommendation system. Neural Topic Model (NTM) algorithm using the Stochastic Gradient Descent (SGD) optimizer (Option D) is used for topic modeling, which is not the objective of the given task.