Fast top-k retrieval for model based recommendation
Deepak Kumar Agarwal, Maxim Gurevich · 2012
A crucial task in many recommender problems like computational advertising, content optimization, and others is to retrieve a small set of items by scoring a large item inventory through some elaborate statistical/machine-learned model. This is challenging since the retrieval has to be fast (few milliseconds) to load the page quickly. Fast retrieval is well studied in the information retrieval (IR) literature, especially in the context of document retrieval for queries. When queries and documents have sparse representation and relevance is measured through cosine similarity (or some variant thereof), one could build highly efficient retrieval algorithms that scale gracefully to increasing item inventory. The key components exploited by such algorithms is sparse query-document representation and the special form of the relevance function. Many machine-learned models used in modern recommender problems do not satisfy these properties and since brute force evaluation is not an option with large item inventory, heuristics that filter out some items are often employed to reduce model computations at runtime.