You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?
To increase revenue using a recommendation system on an ecommerce website, utilizing the 'Frequently Bought Together' recommendation type is a best practice. This method enhances the shopping cart size by suggesting items that are commonly purchased along with the product that the user is viewing or has already added to their cart. This cross-selling strategy is effective in increasing the average order value by encouraging customers to buy additional items that complement their initial purchase.
Answer should be B https://cloud.google.com/recommendations-ai/docs/placements#rps
ANS:B https://cloud.google.com/recommendations-ai/docs/placements#fbt Frequently bought together (shopping cart expansion) The "Frequently bought together" recommendation predicts items frequently bought together for a specific product within the same shopping session. If a list of products is being viewed, then it predicts items frequently bought with that product list. This recommendation is useful when the user has indicated an intent to purchase a particular product (or list of products) already, and you are looking to recommend complements (as opposed to substitutes). This recommendation is commonly displayed on the "add to cart" page, or on the "shopping cart" or "registry" pages (for shopping cart expansion).
the correct answer should be C there is a diagram on the webpage, discuss how it works https://cloud.google.com/recommendations
I think B is the correct answer instead of C, since B directly contributes to increasing revenue.
I don't know the correct answer, but it seems C and D are not correct: - "Do not record user events for product items that have not been imported yet."; i.e., import your product catalog first and then your user events. - "Make sure that all required catalog information is included and correct. Do not use dummy or placeholder values." https://cloud.google.com/retail/recommendations-ai/docs/upload-catalog#catalog_import_best_practices I think the correct answer is B, because the "default optimization objective" for FBT is "revenue per order", whereas the "default optimization objective" for OYML is "click-through rate". https://cloud.google.com/retail/recommendations-ai/docs/placements#fbt
Community vote
Sense is B
Event Data is important along with product data but I am not sure if there is a catch here, what goes first https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/blob/master/retail/recommendation-system/bqml/bqml_retail_recommendation_system.ipynb
Correct answer is "B"
B directly impact the revenue
https://cloud.google.com/recommendations-ai/docs/overview
Went with B
Req: ML Recommendations + increase revenue + best practices A. Use the ג€Other Products You May Likeג€ recommendation type to increase the click-through rate. : You may like ? No B. Use the ג€Frequently Bought Togetherג€ recommendation type to increase the shopping cart size for each order. : Viable with companies purchase information. Also this is the basic recommendation to get started with : cross sell and upsell C. Import your user events and then your product catalog to make sure you have the highest quality event stream. : Ensuring quality? This makes sure the data quality. Not bringing more sales much D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model. : dummy values to replace for now? No value added to sales.
B) To increase revenue, expand shopping cart with other items frequently bought together.
B) To increase revenue, expand shopping cart with other items frequently bought together.
ans is B