How To Get Fast Retrieval Of Data

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.

There are a two-stage approach where the first stage retrieves top-K items using our approximate procedures and the second stage selects the desired top-k using brute force model evaluation on the K retrieved items. The main idea of our approach is to reduce the first stage to a standard IR problem, where each item is represented by a sparse feature vector (a.k.a. the vector-space representation) and the query-item relevance score is given by vector dot product. The sparse item representation is learn to closely approximate the original machine-learned score by using retrospective data. Such a reduction allows leveraging extensive work in IR that resulted in highly efficient retrieval systems. Our approach is model-agnostic, relying only on data generated from the machine-learned model. We obtain significant improvements in the computational cost vs. accuracy tradeoff compared to several baselines in our empirical evaluation on both synthetic models and on a (CTR) model used in online advertising.

Fast Retrieval of View Data Using the ViewNavigator Cache - V8.52
Beginning with the R8.52 release of Notes/Domino there is a clear performance winner in the race to enumerate data from a View using the Backend View related classes. Significant performance work has been done on the ViewNavigator class to allow it perform well enough to serve as the underpinnings for XPage screen display. You can gain the benefits of these enhancements for your application whether it is written in Java, LotusScript, or JavaScript.

The Backend ViewNavigator cache reduces the number of server transactions and associated network overhead when navigating and reading Column Values information from the Documents and Entries in a View. Performance gains are most profound when accessing a View residing on a server from a client, however retrieval from local Views will also be greatly improved.

I hope this post will helpful for you but suggestions are still welcome from reader’s side.