Releva has completed an $870,000 financial round led by New Vision Fund 3 with participation by HR Capital AD, Verto Invest and the investment arm of private investors.
Releva завърши финансов рунд от $870 000, воден от Фонд Ню Вижън 3 с участието на Ейч Ар Капитал АД, Верто Инвест и подкрепата на частни инвеститори.
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Algorithms

Product-Based

Viewed this Viewed that – shows products that are most often viewed (by other users) after viewing the given product. 

Recommends items that are most often viewed in the same session that the specified item is viewed.

This logic returns other products people viewed after viewing this one; the specified product is not included in the results set.

This logic lets you create additional conversion opportunities or create a “surprise” by recommending items that other visitors who viewed an item also viewed. For example, visitors who view road bikes on your site might also look at other similar road bikes as well as bike helmets, cycling kits, locks, and so forth. You can create a recommendation using this logic that suggests other products help you increase revenue.

On the product page the recommender uses the product on the page to calculate recommendations. On the cart page the recommender uses all products in the customers’ cart.

Use this algorithm on product pages.

Viewed this bought that – shows products that are most often bought by users who viewed the current item 

Recommends items that are most often purchased in the same session that the specified item is viewed. This criteria returns other products people purchased after viewing this one, the specified product is not included in the results set.

This logic returns other products people purchased after viewing this one; the specified product is not included in the results set.

This logic lets you increase cross-selling opportunities by displaying a recommendation on a product page, for example, that displays items that other visitors who viewed the item purchased. For example if the visitor is viewing a fishing pole, the recommendation could show additional items other visitors purchased, such as tackle boxes, waders, and fishing lures. As visitors browse your site, you provide them with additional purchasing recommendations.

Use this algorithm on product pages.

Bought this bought that /those – delivers products which are frequently purchased together with the product customer has added to their cart

Recommends items that are most often purchased by customers at the same time as the specified item.

This logic returns other products people purchased after buying this one; the specified product is not included in the results set.

This logic lets you increase cross-selling opportunities by displaying a recommendation on a shopping cart summary page, for example, that displays items that other buyers also purchased. For example if the visitor is purchasing a suit, the recommendation could display additional items other visitors purchased along with the suit, such as ties, dress shoes, and cufflinks. As visitors review their purchases, you provide them with additional recommendations.

Use this algorithm on product and cart pages.

Bought this bought that/those with category co-occurrence  – delivers products which are from the categories frequently purchased together with the item customer is looking at.

Recommends items possessing categories that are most often purchased by customers at the same time as the specified item.
This logic returns other products people purchased after buying a product in the same category as the current product; the specified product is not included in the results set.

This logic lets you increase cross-selling opportunities by displaying a recommendation on a shopping cart summary page, for example, that displays items that other buyers also purchased. For example if the visitor is purchasing a suit, the recommendation could display additional items other visitors purchased along with the suit, such as ties, dress shoes, and cufflinks. As visitors review their purchases, you provide them with additional recommendations.

Use this algorithm on product and cart pages.

New products – delivers items which are marked as New in your store in order to spike the interest of returning customers

Use this algorithm on general pages such as home, cart, or category page.

Popularity-Based

The Popularity-Based algorithm type lets you make recommendations based on the overall popularity of an item across your site or based on the popularity of items within a user’s favorite or most-viewed category, brand, genre, and any property that your products have.

Popular products – delivers popular products which customers view, search and purchase often.

Use this algorithm on general pages, such as home, category or landing pages and offsite ads.

Trending products – delivers products ranked by the increase in their popularity (first derivative of popularity) 

Use this algorithm on general pages, such as home, category or landing pages and offsite ads.

Hot products – delivers products ranked by the rate of increase in their popularity (second derivative of popularity) 

Use this algorithm on general pages, such as home, category or landing pages and offsite ads.

Most Viewed –  delivers items which are frequently viewed across the website.

Top Sellers – delivers items which are frequently purchased across the website.

User-Based

The User-Based algorithm type lets you make recommendations based on the user’s behavior.

The following algorithms are available with the User-Based algorithm type:

Recently viewed items/Browsing history – shows products that customer has viewed ordered from most recently viewed to least recently viewed 

Uses the visitor’s history (spanning sessions) to present the last X number of items the visitor viewed, based on the number of slots in the design.

The Browsing history algorithm returns results specific to a given domain. If two sites belong to different domains and a visitor switches between the two sites, each site shows only recently viewed items from the appropriate site. If two sites are in the same environment and a visitor switches between the two sites, the visitor sees the same recently viewed items for both sites.

Use this algorithm on product, cart and general pages, such as home, category or landing pages and offsite ads.

Last cart products – shows items which customers currently have in their carts

Use this algorithm for abandoned cart retargeting.

Popular cart products – delivers items which are frequently added to cart by the given user 

Recommended for you – personalized recommendations based on customer behavior on the website, what products they have interacted with and how other users interact with these products i.e. views and adding to cart, purchases

Recommends items based off each visitor’s browsing, viewing, and purchasing history.

This algorithm lets you deliver personalized content and experiences to both new and returning visitors. The list of recommendations is weighted towards the visitor’s most-recent activity and is updated in-session and become more personalized as the user browses your site.

Use this algorithm on general pages, such as home, category or landing pages and offsite ads.

Recommended for you based on views – personalized recommendations based on customer behavior on the website limited only to views

Use this algorithm on general pages, such as home, category or landing pages and offsite ads.

High frequency purchased products – personalized recommendations based on products that the customer usually buys.

Use this algorithm on general pages such as home, category and the cart page.

In-cart personalized recommender -personalized recommendations that returns items the user usually buys but have not been bought in the last couple of baskets.

Last search products – personalized recommender that returns popular products re-ordered based on user search preferences.

Use this algorithm to re-target users that performed searches on your website.

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