Impact of Deep Learning on Personalization
Machine-learning-based personalized marketing has gained popularity over time due to the increasing amounts of data available across different sources and the speed at which organizations and consumers produce new data. The old personalization methods focused on developing business rules using methods like segmentation, which typically didn't address a particular individual customer. Recent advancements in specialized hardware (read the GPU and cloud computing) and the growing toolkits for ML and DDL allow us to build customized customer-specific personalization that can be scaled.
Recommender systems can benefit both the service provider and the user. They lower the costs of searching for and selecting products when shopping online and increase the user experience. Recommendation software has also proven to enhance the decision-making process and the quality of products. In the context of e-commerce, for instance, recommender systems increase sales due to the reason that they're effective in selling more items. In scientific libraries, they assist users in allowing them to move past catalogue searches. Therefore, the necessity to employ efficient and accurate methods of recommendation within the system to offer reliable and accurate recommendations to users can't be under-emphasized.
In Epsilon, we utilized machine learning to address the issue of providing granular recommendations for products across a variety of channels in order to increase customer engagement and improve the bottom line. The typical methods for predicting product recommendations involve creating several models of high-level categories of products. They aren't efficient, resource-intensive, and do not provide specific recommendations. In the following sections, we will quickly discuss how to construct effective recommendation systems.
Collaborative filtering suggests items by identifying users who have similar preferences; it uses their opinions to suggest items to the active user. They learn from interactions between items and users. From this data, we can construct an M x N sparse matrix that will capture all interactions that a user can have with an item. The N represents the number of people, and M is the number of items. The data is generally limited, which means that only a few non-zero elements appear on the table. This is also evident in the long-tailed distribution from the interaction frequency plots for all elements.
These algorithms use the metadata of both users and items and aim to find items that match according to the user's preferences. The metadata of an item is the primary characteristic that defines an item's characteristics. The metadata of users can explain users' particular characteristics, e.g., demographics. Utilizing the previous interaction between item and user information, attributes of the item and user are built for each user and item, and similarity matching is applied to determine the best N suggestions.
The calculation of distance is possible using a variety of different distance metrics; however, typically, cosine is the most used. If the data input is already normalized, one could make use of a linear kernel instead of using cosine similarity. Another popular distance measure is Euclidean distance, but it may not be suitable for recommendation when a large number of Hot encoded variables are at stake.
It is a simple machine learning technique based on the Bayes theorem for conditional probability. It's not a good idea because it presumes that all predictor features are mutually inseparable. The assumption of the independence between features might not be true in all instances in real life. This is a way to produce rapid predictions and lends it the capacity to scale. It is commonly employed as a baseline technique in the text classification problem.
When using a recommender system, this may be used to determine the likelihood of buying an item based on the likelihood of purchases in the past. The output scores are sorted in order of decreasing order, and only the best N products are suggested. It's quick, flexible, and can be used in the use of categorical predictive predictors.
It is the job of predicting the next item or items in the sequence based on prior items. The term is typically employed in the context of RNN or LSTM within Natural Language Processing. Concepts similar to text sequence could be applied to different domains, too, e.g., stock forecasts, the probability of purchasing any product, and so on.