Methods, computer readable media, and devices for modeling heterogeneous feature sets for use in personalized search are provided. The method may include generating a similarity factor for each of a plurality of personalization features. For each of the plurality of personalization features, a personalization feature weight is calculated. Each personalization feature weight is converted into a probability distribution and each similarity factor is scaled based on a corresponding probability distribution. Based on each scaled similarity factor, a most recently used affinity value is generated for each corresponding personalization feature. The most recently used affinity values are used to generate a ranking function for use as part of personalized search.