This document discusses approaches for fast retrieval of data from large datasets. It describes a two-stage approach where the first stage uses approximate procedures to retrieve the top-K items, and the second stage selects the final top-k using brute force evaluation on the K retrieved items. The key idea is to reduce the first stage to a standard information retrieval problem by representing each item as a sparse feature vector and using vector dot product to calculate relevance scores, which allows leveraging efficient retrieval techniques. The document claims this approach is model-agnostic and can provide improvements over baselines in computational cost versus accuracy.

1. How To Get Fast Retrieval Of Data

2. 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.

3. 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.

4. 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.

5. 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.
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6. Thank You !!!