Alie’s AI is powered by algorithms that are designed to anticipate your customer's needs and create personalized experiences. Alie’s engine does this by analyzing item attributes and behavioral patterns to make predictions about the user’s needs. The engine then recommends items catered specifically for the user resulting in a high probability of interaction, thus improving user engagement and creating a customized experience.
With Collaborative filtering, Alie makes recommendations based on your user’s behavior. Alie utilizes behavioral patterns such as user preferences, activities, and interactions to find similar or matching users, and proceeds to predict what the user will like based on their similarities with other users. The algorithm can be customized to find similarities based on items, users, or both. With the Nearest Neighborhood Algorithm model, Alie generates a rating system based on the nearest neighbor in your database and recommends the most likely match.
Alie can be customized to use Collaborative Filtering in two ways.
Alie uses content-based filtering to determine a user’s preferred choices. Alie determines the right item to be recommended by analyzing the keywords used to describe the items along with the user’s profile that is built to state the type of item this user likes. The algorithms try to recommend products that are similar to the ones that a user has liked in the past. This is especially helpful in cold-start situations when there is not enough data on an item or user.
K Nearest Neighbor(KNN) is a supervised machine learning technique mostly used for classification problems. With KNN, you can train Alie to learn a function by ingesting labeled data and reproducing results for unlabelled data. Use an existing database to train Alie’s AI to learn and understand your customer’s behaviors. Alie utilizes the learning from labeled data to provide recommendations to both new and existing users.
With NLP, Alie gets the ability to analyze unstructured data and provide the optimum ranked list to every user. NLP lets Alie analyze large chunks of unstructured data and solve a wide range of problems such as relationship extraction, sentiment analysis, and topic segmentation.
The matrix factorization methods used to design recommendation systems have limitations such as the inability to use side features that impact recommendations (such as the user rating of an item or the U/PG rating of a movie), and usually end up suggesting popular items every time that do not always reflect the interests of the user. With Deep Neural Networks, Alie is able to overcome these limitations by creating stronger user-item interaction functions. This enables Alie to predict what your user needs with a greater degree of accuracy, resulting in better user experiences, meaningful interactions, greater product usage, and loyalty.
The degree of accuracy for every recommendation depends on the quality and volume of the data being utilized. Alie’s recommendations are based on the analysis of a wide variety of data sets which includes user behavior, browser cookies, and item popularity. Alie can also be trained to identify and predict the needs of your current users through supervised learning. By analyzing volumes of existing data, Alie learns about your users preferences and recommends every item with a high degree of accuracy.