In-depth guide on working of recommender systems

With the arrival and rise of platforms such as YouTube, Amazon, and Netflix over the last few decades, recommender programs have become a convenience that users take for granted. However, these programs are an inescapable part of our everyday online ventures whether in e-commerce (e.g. suggesting items of interest to the shopper) or digital advertising (e.g. displaying relevant content based on user's preferences).

They are, in a broad sense, models that tend to propose related content (e.g. movies, texts and products) based on users’ history and interests. Thus, in a digital ocean of options, recommender programs are essential online search assistants.

Table of content

Recommender systems logic

Types of recommender systems

Collaborative filtering

Content-based filtering

Hybrid recommendation systems

Weighted hybrid recommender

Switch hybrid recommender

Mixed hybrid recommender

Analytical relationships in recommender systems

Data for recommender systems

Types of data

User behavior data

User demographic data

Product attribute data

Data collection methods

Explicit ratings

Implicit ratings

Similarity finding

User similarity (User-user filtering)

Product similarity (Item-item filtering)

Conclusion

Recommender systems logic

Having to explore the full variety of items some websites have to offer in order to find a single item can be frustrating. This is where a suggestion feature comes into play. It helps to create a significantly smoother user experience by facilitating the discovery of relevant inventory that may not have been found otherwise.

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There is also a recommender feature which predicts the rating the user may give to a product, and it is an essential part of both of the pathways above.

Types of recommender systems

1. Collaborative filtering

This is one of the most frequently used and well-established innovations in the sector today. Collaborative filtering sorts information by employing system interactions and data gathered from other users. It is predicated on the notion that individuals who agree on particular data points would most likely agree again on the exact ones in the future.

More generally, collaborative filtering is the prediction of the preferences of a consumer by analyzing the behavior of a user to identify behavioral patterns. For instance, a recommender system can anticipate what a consumer will be interested in by reviewing what they liked, disliked, skipped, and viewed.

A good example of collaborative filtering are the famous music recommendations on Spotify and Last.fm. They produce playlists of suggested genres and artists by taking into consideration the listener's daily history of music preferences and comparing it to other users' listening behavior.

2. Content-based filtering

To be able to recommend similar items with comparable qualities, content-based filter methods are implemented. They utilize keywords that define distinct characteristics of a product. A profile is then created for each user to identify the kind of item. In simpler terms, these algorithms attempt to recommend products comparable to what a user has enjoyed in the past.

Pandora, an online music recommendation website, utilizes just a song or an artist's attributes to generate a station that essentially plays music with similar characteristics. Feedback from users is also utilized to redefine the outcomes. If a user disfavors specific music, less attention is placed on some of its characteristics. On the other hand, if a user prefers certain music, the characteristics of this song will be emphasized and more similar songs will be presented in the future.

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3. Hybrid recommendation systems

a. Weighted hybrid recommender

Weighted recommendation systems allow you to specify several models that are capable of interpreting large datasets effectively and efficiently. Using the outcomes from each model, the weighted recommendation system will integrate them into static weightings that never alter across training and testing the datasets. Basically, this hybrid approach has the advantage of integrating multiple models to sustain the recommendation system processing in a logical manner. For instance, if you integrate a content-based model with an item-item collaborative model, each of the models will give 50% of its contribution for finalizing the predictions

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b. Switch hybrid recommender

The switching hybrid chooses a recommendation system based on the situation. As a result of this hybrid method, an extra layer is added to the recommendation algorithm to select which model to employ. Also, the constituent recommendation model's ups and downs are taken into consideration by the recommender system. However, you may adjust the recommender selector conditions depending on the consumer's profile or other characteristics.

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c. Mixed hybrid recommender

As part of the mixed hybrid method, users' profiles and characteristics are used to create a variety of potential datasets. To reach a user's preference, the recommendation system enters a new group of candidates into the recommendation model and combines their predictions. This approach generates a high number of suggestions concurrently, thus it is suitable to achieve competitive advantages for a partial dataset of an effective framework.

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Analytical relationships in recommender systems

  1. Product-product relationship

When items have identical features, they form product-product relationships, like books or songs of the same category, recipes from the same cuisine, or news stories on a specific event.

  1. User-user relationship

When two or more users have identical tastes in a product or service, they form user-user partnerships.

  1. User-product relationship

When specific consumers have an affinity or desire for particular items, this is known as the user-product relationship.

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Data for recommender systems

Types of data

In response to the abovementioned relationships, recommender systems consider several types of data:

  1. User behavior data

Users' behavioral data is considered as valuable knowledge on a user's involvement with a product. Ratings, clicks, timestamps on a specific product page, and transaction background all contribute to this type of data.

  1. User demographic data

Consumer demographic information is linked to personal details such as age, geographic location, education, gender, salary levels, and profession.

  1. Product attribute data

It refers to details about the product itself, such as the genre of a publication, the cast of a film, or the recipe of a meal.

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Data collection methods

Users' data is collected in several ways, however, there are two main methods in which recommender systems collect users' data.

  1. Explicit ratings

The consumer states his or her opinion by explicit ratings. Explicit ratings make assumptions about the user's preferences. Star scores, reviews, comments, likes, and followings, are only a few examples. Exact scores can be challenging to obtain since consumers do not necessarily evaluate products.

  1. Implicit ratings

When users communicate with an object, implicit ratings are given. Implicit ratings assume a user's actions and are easy to obtain when users click around the website subconsciously.

Similarity finding

  1. User similarity (User-user filtering)

The variance between two consumers' similarities is verified using user similarity. The system may presume two consumers have similar desires if they have similar product tastes. So, it's as if a peer is suggesting a product or service to a friend.

However, one drawback to user similarity is that it takes all of the user's data to recommend items. This means that a recently introduced e-commerce website, for example, that doesn't have many customers yet, may experience a ‘cold start’ because this method necessitates prior data from users.

Fortunately, the use of product similarity does not create this hurdle since it includes product detail and the user's preferences. For example, Netflix’s recommender solves this problem by reminding customers about their preferences as they sign up for a new subscription.

  1. Product similarity (Item-item filtering)

Item similarity seems to be the most effective method for recommending products depending on how well the consumer likes them. Related items can be shown to users who are surfing or shopping for a specific product. When a consumer clicks on a product, the system will display another comparable product. Whether the user purchases the product or not, the system will display ads or offers on a similar outcome. When there's no information about the consumers themselves but there's an understanding of what they're looking for, product resemblance comes in handy.

Conclusion

The recommendation system is the best example for predicting the users’ preferences. Various online platforms use this approach to effectively boost their sales by helping the users acquire what they want with ease.

The kind of recommender system you concentrate on initially will be determined by your business goals. For example, whether you want to increase engagement for currently active users or encourage infrequent consumers to become more engaged. But, for a small group of people, it's preferable to start with a simple recommender system then upgrade as the user base increases.

Also, with recommender systems, you should be able to establish the company’s objectives as well as evaluate and grasp the data produced by your website, providing that the system is effectively implemented.

Our next article will dive deeper into the topic of recommender systems. So, stay tuned to our blog.