Algorithms that Power Alie
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.
Apps and Website powered by Muvi
Collaborative Filtering
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.
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User-based and Item-based Collaborative Filtering
Alie can be customized to use Collaborative Filtering in two ways.
- User-Based: Distance and correlation algorithms are used to compute the distance between a particular user and every other user to determine recommendations based on similar or lookalike users.
- Item-Based: A recommendation list is based on similar or lookalike items and is created by computing the distance between a particular item and every other item.
Content-Based Filtering
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.
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Supervised Learning with KNN
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.
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Natural Language Processing
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.
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Deep Neural Networks
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.
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Data Utilized
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.
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The great thing that I love about Playout is that we can actually go and mimic the actual schedule of the festival, and so that was a very helpful and a really important aspect because being an International Film Festival, you don’t always have the filmmakers from all over the world being able to come in, and so it gives them a chance to watch the festival from home.
Brandon Goldsmith
President, Fort Smith International Film Festival
Thanks to Muvi, we achieved our goals with ease. Their platform provided clear user feedback and usage data, helping us gain 15,000 web registrations, 10,000 Android users, and 5,000 iOS users. The robust analytics in Muvi’s content management system empowered us to make informed decisions, driving streaming for audio and video. We are highly satisfied with Muvi’s services.
Kashif Khan
Chief Executive Officer, EnterInfi
We wanted a platform that is reliable. We made the research and found Muvi, we felt Muvi was the best solution that met our requirements. The fact is that it is DRM Enabled, it protects content from getting downloaded, screen scraped or even screen sharing is not allowed.
Danji Thotapalli
Festival Director, Indic Film Utsav
Muvi provides geo-blocking allowing us to screen films in a specific country so that the future distribution and exhibition options of the films we screen are not hurt. It also provides DRM protection which is a security standard requested by filmmakers.
Ezgi Y
Director of Accessible Film Festival, Puruli Culture Art
Cheers to the Muvi team we are really grateful for all their services. We recommend Muvi to all the educational institutions thinking of a platform to use for addressing their digitalization needs, Muvi is the right platform for you with the perfect product & superb services
Vicson Mabanglo
Operations Manager, Legal Edge Bar Review Center
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