Several machine learning models are widely used to create product and user vectors, which are essential for tasks like recommendation systems and personalization. The most common approaches include matrix factorization techniques, word embedding methods adapted from natural language processing (NLP), and neural network-based models. These methods aim to represent products and users in a shared vector space, enabling similarity comparisons or predictions based on their interactions.
Traditional methods like matrix factorization (e.g., Singular Value Decomposition or SVD) decompose user-item interaction matrices into lower-dimensional user and product vectors. For example, in collaborative filtering, a user-item rating matrix is split into two matrices: one representing users and the other products. Each row in these matrices corresponds to a vector capturing latent features (e.g., preferences for movie genres). Libraries like Surprise or implicit provide implementations of these algorithms. Another approach is word2vec or its variants (e.g., item2vec), which treat user interactions as sequences (like sentences in NLP) and learn embeddings by predicting related items. For instance, Spotify’s playlist recommendations have used item2vec to model songs based on co-occurrence in playlists.
Neural networks like autoencoders or two-tower architectures are also popular. Autoencoders compress interaction data into dense vectors by reconstructing input signals, while two-tower models (used in retrieval systems) train separate towers for users and products. For example, YouTube’s recommendation system uses a two-tower model to map user watch history and video metadata into vectors. More advanced methods like BERT or Graph Neural Networks (GNNs) can incorporate contextual or graph-structured data. BERT-based models encode text descriptions for products, while GNNs capture relationships in user-product interaction graphs. Tools like TensorFlow Recommenders or PyTorch simplify implementing these architectures. The choice of model depends on data type (e.g., structured, text, graph) and scalability needs.