CGSC 1001 Lecture Notes - Recommender System, Netflix, Distributed Computing

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carletonpsychgrad

21 Mar 2022

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Disembodied software: how netflix works, cognitive system is the amazon recommender system, we ask it to do something, and therefore user input. Human memory of fact & perception: sensory memory: is like a scratch pad (for vision), or a bit or recording tape (for hearing), that can be overwritten with new things. It is rewritten every few seconds by new perceptions: short-term memory: is a temporary store. Some things from it end up becoming long term memories: long-term memory: probably stored here forever, though we might have trouble with retrieval. Sensory short term long term memory. Computer memory: hash tables, schemata, frames, scripts, logical sentences, disk, flash memory. Basic building of the computer: bitmaps, activation patterns, weights in links. Memory in distributed systems examples: books, brains, fingers, notes, arrows and signs. Learning: learning is changing memory with the purpose of preparing a system for better action in the future.

Related Questions

URGENT support required :

An energy company is required to report the average pressure of natural gas used over the past quarter. A PLC sends data to a historian server that creates the required reports.

Which of the following historian server locations will allow the business to get the required reports in an and IT environment?

Options: A In the environment, use a VPN from the IT environment into the environment.
B In the environment, allow IT traffic into the environment.
C In the IT environment, allow PLCs to send data from the environment to the IT environment.
D Use a screened subnet between the and IT environments.

aminul2005

A security engineer thinks the development team has been hard-coding sensitive environment variables in its code.

Which of the following would BEST secure the company's CI/CD pipeline?

Options:

A Utilizing a trusted secrets manager
B Performing DAST on a weekly basis
C Introducing the use of container orchestration
D Deploying instance tagging

aminul2005

Objective: Take a sample feed, parse it, augment it, and output individual JSON objects for each product in it.

product.txt

The file contains several fields of basic product information for 1000 products.
Data for each product is on one line, with fields separated by a vertical pipe character.

This particular retailer classifies products into a particular store, department, category, and subcategory.

Example >

A Blender might have the following "taxonomy" or category classification.

Home & Garden > Kitchen & Dining > Appliances > Blenders

Store

Department

Category

Subcategory

store_id

department_id

category_id

subcategory_id

These IDs are also represented in a sort of shorthand.

They should all be prefixed by the first three letters of their respective field name.

A store_id of 1 becomes sto1

A department_id of 2 becomes dep2

A category_id of 3 becomes cat3

A subcategory_id of 4 becomes sub4

targets.txt

A system to keep track of these various classifications
- Example
  - A store_id of sto40 maps to a “target value” of 187298 in PromoteIQ's system

Output Example:

The desired output for the first product is one line of JSON that when pretty-printed looks like:

{

"sku": "22067130",

"name": "Brainboxes ES-571 Brainboxes Es-571 Industrial Isolated Ethernet to Serial + Switch - 1 x Network (R",

"description": "Brainboxes ES-571 Brand New Includes Lifetime Warranty Product # ES-571 Factory floors can be harsh environments.",

"priceList": 387.95,

"taxonomy": [

"sto2",

"dep7",

"cat270",

"sub18532"

"targetValues": [

187167,

187177,

187950,

196404

]

}

The taxonomy IDs have been mapped to their long form and placed in the taxonomy property.

Each taxonomy ID has also been mapped to its corresponding target value ID from the targets.txt file
- and that result is stored in the targetValues
- The priceList property is the product price as a JSON float, not a string

coffeebear857

CGSC 1001 Lecture Notes - Recommender System, Netflix, Distributed Computing

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