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Starter-pack models


This section explains non-personalized, starter-pack, recommenders.

AI starter-pack recommender

Non-personalized recommenders, often referred to as general or global recommenders, offer suggestions without considering individual user preferences. Instead, they focus on overall popularity, trends, or item attributes. These systems provide recommendations that are the same for all users or a particular user group. For instance, a non-personalized movie recommender might suggest the most viewed or highest-rated movies to all users without taking into account an individual's movie taste. While they may lack the tailored precision of personalized recommenders, non-personalized recommenders are valuable for introducing users to popular or trending items and can be particularly useful in scenarios where a user's preferences are unknown or for new users joining a platform.

Best sellers

A best-sellers recommender system operates by suggesting the most popular and top-selling products to users. This recommendation approach is based on the assumption that products frequently purchased by a large number of customers are likely to be appealing to others as well. The system analyzes purchase data, often in real-time, to identify the products with the highest sales or most views. By showcasing these highly sought-after items, the recommender aims to guide users towards products that are already well-received by a broad audience. This strategy helps users discover items that are in high demand, making it particularly useful for those seeking widely appreciated and trusted options within a diverse range of products.

A trending products recommender system operates by identifying and recommending items that have recently gained significant popularity and are currently trending. By analyzing real-time data, customer engagement, social media activity, and other relevant metrics, this system highlights products that have experienced a sudden surge in interest or sales. These trending items reflect current consumer preferences and emerging market trends, making the recommendations highly relevant and timely. Users benefit by being exposed to the latest and most talked-about products, enabling them to stay up-to-date with what's popular and in demand within the market at any given moment.

Similar products

A similar products recommender system suggests items that closely resemble a given item based on various criteria such as features, attributes, category, or customer interactions. This type of recommender employs similarity measures and algorithms to calculate the likeness between products. By analyzing item characteristics and user behavior, it identifies comparable items that share commonalities and are likely to appeal to the same audience. For example, if a user is viewing a specific laptop, the system may recommend other laptops with similar specifications or features. This enables users to explore alternative options that align with their preferences, aiding in making informed decisions and broadening their choices within a specific product category.

A related products recommender system suggests items that are frequently purchased together with a given item. This recommendation approach is rooted in the concept of item association and co-occurrence in baskets or purchase histories. By analyzing vast amounts of transaction data and purchase patterns, the system identifies products that tend to appear together in users' baskets. For instance, if a customer is viewing a smartphone, the system may recommend accessories like phone cases or screen protectors that are commonly bought alongside smartphones. This assists users in discovering complementary products or items that enhance the usage or experience of the main product, offering a holistic shopping experience.

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