Siggy Recommender
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Siggy Recommender

Let’s talk about our algorithm (and data)

Siggy uses a “content-based” recommendation algorithm. This means that we only need “content data” for the algorithm to work.

In the context of a product recommender, we only need product attributes such as (Product name, description, images, tags) to generate effective recommendations for the user.

There are three primary reasons we choose a “content-based” recommendation algorithm first:

  • No dependency on user-generated data.
    Siggy does not rely on user-generated data such as ratings to establish relationships between the products, unlike classic collaborative filtering algorithms.

How our algorithm performs

Generally, our algorithm performed well for shops that have a large number of products (e.g. 500+), consistent product images, and that are non-gender/age-specific.

A good example

One of the examples that work well is jewelry, specifically “Diamond” in this case. Our recommendation works well since:

  • Product images will have a white/transparent background for the algorithm to find products based on image similarities (Shape, color, patterns, etc).
(Jewelry example)

A Tough example

In this case, our recommendations are all over the place. Three of the four products are things for the opposite gender (men) and one for boys.

This example is especially tough because “Shorts” is something that can be worn by both genders, however, it is clear from the image this is a specific style targeted towards women.

Our algorithm failed to capture and weigh some more important product attributes such as the style “cut-off” and the length of the shorts. In the end, it was looking for “Shorts” regardless of the intended audience.

(Non-gender-specific example)

A better example

Dresses are better examples because they have a unique pattern/structure and consistent product image modeled in similar ways.

It also helps that there are no “dresses” for men or children in the product catalog.

(Gender-specific example)

Early observations

While it is too early to tweak our algorithm and while we are gathering data on its efficacy, we can notice that the algorithm works well when:

  • There’s a large product catalog.



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Chang Xiao

Starter, dev, digital consultant, cyclist, tennis player. Currently focused on data science and specifically recommendation systems.