Engineering the features for a recommender system | Algolia

In this article, we delve into the process of feature engineering for recommender systems. Feature engineering is crucial in transforming raw data into a format that can be ingested by machine learning models. The process involves converting different types of unstructured data into standardized descriptions and extracting keywords from underlying data. Feature weighting and selection are also discussed, which involve giving differential weights to features based on their importance or including/excluding attributes based on relevance. Various methods for scaling continuous variables such as normalization and standardization are explained, along with techniques for converting categorical features into integers. The article then explores natural language processing (NLP) techniques like bag-of-words model and preprocessing sentences using tokenization, removing unnecessary punctuation and stop words, stemming, and lemmatization. It also covers image feature extraction methods such as rearranging all the pixels to generate a feature vector or creating the feature vector by using the mean value of pixels from all the channels. Finally, it introduces the concept of a feature store, which acts as a central vault for storing documented, curated, and access-controlled features within an organization. This helps in addressing infrastructure complexities and duplication of work often faced by distributed organizations. The article concludes with some best practices for feature engineering that include generating simple features, reusing legacy systems, using IDs as features when needed, reducing cardinality when possible, making feature selection when necessary, carefully testing the code, keeping the code, model, and data in sync, isolating feature-extraction code, serializing the model and feature extractor together, and logging the values of features.