To combine user profile data and clickstreams into a vector, you need to preprocess both data types into numerical formats and then merge them into a single representation. Start by structuring the user profile data—such as age, location, or preferences—into normalized or encoded numerical values. For example, categorical data like “country” can be one-hot encoded, while numerical data like “age” can be scaled to a 0-1 range. Clickstream data, which includes actions like page views or item clicks, can be aggregated into features like session duration, click counts per category, or sequences of visited pages. These features are then converted into numerical vectors using techniques like count encoding or embeddings.
For user profiles, a common approach is to handle static and dynamic attributes separately. Static attributes (e.g., sign-up date) might be represented as time-since-registration in days, while dynamic attributes (e.g., “preferred_category”) could be one-hot encoded. For example, a user with “preferred_category: electronics” might become [0, 1, 0] if categories are ["clothing", "electronics", “books”]. Clickstreams require feature engineering: you might count how many times a user viewed a product page, calculate the average time between clicks, or use a sequence model (like an LSTM) to turn timestamped events into fixed-length vectors. Tools like TensorFlow Transform or scikit-learn’s CountVectorizer can help automate this.
Finally, combine the two vectors by concatenation or weighted summation. For instance, if the profile vector is [0.5, 0, 1] (normalized age, one-hot gender) and the clickstream vector is [12, 3, 0.8] (total clicks, unique pages, session duration ratio), concatenating them yields [0.5, 0, 1, 12, 3, 0.8]. If dimensions differ drastically, apply dimensionality reduction (e.g., PCA) first. Alternatively, use neural networks to project both into a shared space before merging. For example, train an autoencoder on clickstreams and a feedforward network on profiles, then combine their outputs. Validate the approach by testing downstream tasks like recommendation accuracy to ensure the combined vector captures meaningful patterns.