OUr technology

Deep Recommendation Engine

Deep Collaborative Filtering

Our Modest Research on this Matter

Deep Collaborative filtering allows to understand and present complex patterns of human behavior. Whether it is through grouping data or referencing patterns, it is imperative to gather as much user information as possible from all available sources to acquire usable results.

Simple models fail to learn simultaneously global trends and rare individual preferences from the long tail. Deep Collaborative Filtering scales to settings surpassing traditional boundaries, such as cross-domain or unbalanced data sets.

What other people typically do

Most other solutions use “Matrix Factorization” which reduces an individual's preferences to a simplistic linear model.

Using Deep Collaborative Filtering, our solution instead focuses on pinpointing complex patterns that our algorithms then use to determine user tastes.

Deep Content Extraction

Our Modest Research on this Matter

Recommending new items is a common challenge for businesses. More generally, any item without enough history of interactions from users won't be recommended by traditional methods. A novel solution to this cold-start problem is to extract deep information from text or image data using advanced natural language processing and creating a neural network of associations. Deep Content Extraction allows our software to recommend items no one has interacted with yet because the algorithm is able to understand the genre of the content by automatically extracting information from items such as a movie poster, synopsis, or reviews.

What does other people typically do?

Other solutions commonly patch their recommender system with hand-crafted rules to diminish the cold-start problem. However this requires expensive iterations and does not scale.

Using Deep Content Extraction, our solution can generate accurate recommendations of items as soon as they are available in the data set.

Semantic Graph Embedding

Our Modest Research on this Matter

Semantic Graphing makes the goal of finding correlations between seemingly unrelated data simpler by using variables such as metadata, labels, tags, genre, actors and more. By doing this we can make sense of semantic data in the same way a human mind would. In a nutshell, if we know user likes a certain item, and this item is connected to a second item, for instance if two movies share certain key actors, then our algorithm can consider the second item as a promising candidate to recommend to our user.

What does other people typically do?

Most other solutions do not include any graph databases for their recommendations.

Our algorithm leverages all the available information to increase the accuracy of the recommendations, enabling users to discover hidden gems.

The importance of Embeddings for Recommendations

An embedding is a low-dimensional vector of continuous numbers learned to represent relevant information in a compact and convenient form. Embeddings are designed to reduce the dimensionality of both categorical and continuous variables and meaningfully represent categories or features in the transformed space. 

When Machine learning and embeddings are combined it permits us the ability to more accurately determine which data qualifies as features.

More concretely, embedding serve as input to a machine learning model for a supervised task, such as classification or recommendation. These can be used to personalize an online visitor experience based on their interests.




How to leverage a Recommendation API


Cold Start Recommendation

For a first time user of your brand, attempting to provide recommendations without data-sets reflecting interests and tastes can be challenging, this is what we would categorize as a cold-start problem. For this, we suggest implementing our deep content extraction combined with cross-channel pattern recognition and innovative machine learning algorithms. These methods combined provide a solution for extracting personalized recommendations with limited data sets on user tastes & preferences.

Cross-domain recommendations

Not only is Crossing Minds capable of categorizing and accurately taste-mapping user preferences, we also utilize cross-domain mapping to determine the recommendation for a user to put them in the right room, in the right city, next to the right restaurants and entertainment. Our taste predictive algorithms are designed to provide fully personalized user experiences across the board or across the brands.

Taste Mapping

Our recommendation API supports the creation of concepts and relations between categories. This is helpful to find hidden correlations between seemingly unrelated products or consumers and produce the most insightful taste mapping for all of your users.

Interested in trying our Recommendation API for free?

We are very exited to share our newly released Recommendation API, which businesses can utilize to offer their customers the best recommendations possible.

If you are interested in being among the first to try it out, please answer the interest form. Our team will reach back to you as we are processing the flow of applications. Thanks!

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