They expect the apps, news sites, social networks, and online stores they engage with to remember who they are and what they like, then make relevant, individualized, and accurate recommendations for new content and new products based on their previous activities. Any app or website that fails to deliver on these demands will quickly see its users flocking out the digital door. As a matter of fact, in this article Gritglobal will mention 4 necessary algorithms for a product recommendation system.
There are several types of product recommendation systems, each based on different machine learning algorithms to conduct the data filtering process. The main categories are content-based filtering (CBF), collaborative filtering (CF), complementary filtering, and hybrid recommendation systems, which use a combination of CBF and CF.
Content-based filtering
These filtering methods are fundamental components of product recommendation systems, relying on item descriptions and user preferences. In a content-based recommendation system, items are described using keywords, while user profiles outline their preferred choices. Essentially, algorithms aim to suggest products similar to those previously liked by a user. The concept behind content-based filtering is that if a user enjoys one item, they are likely to appreciate similar items. For instance, in movie or song recommendations, this approach draws from information retrieval and information filtering research.
A key challenge with content-based filtering is its ability to extrapolate user preferences from actions on one content source and apply them to different content types. When restricted to recommending content of the same type as what the user is already consuming, the value derived from the recommendation system diminishes significantly. For instance, while recommending news articles based on browsing history is helpful, suggesting music and videos from different services based on news consumption would provide even greater utility.
Collaborative filtering
This filtering method is typically employed in product recommendation systems and relies on gathering and analyzing user behaviors, activities, or preferences to predict their preferences based on similarities with other users. A significant advantage of the collaborative filtering approach is its independence from machine-analyzable content. Thus, it can accurately recommend complex items, such as movies, without requiring an understanding of the item itself. Collaborative filtering operates under the assumption that individuals who agreed in the past will likely agree in the future and have similar preferences. For instance, if person A likes items 1, 2, and 3, and person B likes items 2 and 3, they are deemed to have similar interests, suggesting that A may like item 4, while B may like item 1.
Types of Collaborative Filtering Algorithms
- User-User Collaborative Filtering: Here, we try to search for lookalike customers and offer products based on what his/her lookalike has chosen. This algorithm is very effective but takes a lot of time and resources. This type of filtering requires computing every customer pair information which takes time. So, for big base platforms, this algorithm is hard to put in place.
- Item-Item Collaborative Filtering: It is very similar to the previous algorithm. But instead of finding a customer look alike, we try finding items that look alike. Once we have an item look similar to matrix, we can easily recommend similar items to a customer who has purchased any item from the store. This algorithm requires far fewer resources than user-user collaborative filtering. Hence, for a new customer, the algorithm takes far less time than user-user collaboration, as we don’t need all similarity scores between customers. Amazon uses this approach in its recommendation engine to show related products which boost sales.
- Other simpler algorithms: There are other approaches like market basket analysis. Which generally do not have higher predictive power than the algorithms described above.
Complementary filtering
Here, the system learns the probability of two or more products to buy together. For example, when a user buys a smartphone from an ecommerce store; it is more probable that the same user will buy a set of headphones on a return visit, rather than another smartphone.
As such, the algorithms are based around recommending products that are complementary to other products. They are product-defined, as opposed to user-defined, as in CBF and CF. They most commonly use the Naïve Bayes algorithm in complementary filtering.
Hybrid recommendation systems
Recent research indicates that combining collaborative and content-based techniques can enhance the effectiveness of product recommendation systems. Hybrid approaches involve generating predictions separately using content-based and collaborative-based methods, then merging them. Additionally, enhancing collaborative-based models with content-based capabilities, and vice versa, or unifying both approaches into a single model are viable strategies.
Numerous studies have compared the performance of hybrid methods with pure collaborative and content-based approaches. Results consistently demonstrate that hybrid methods offer more accurate recommendations than pure methods. These approaches address common challenges in product recommendation systems, such as cold start and data paucity issues.
Conclusion
In conclusion, there are a lot of technical explanations that can be made on the types of product recommendation engines. All that the users or buyers mostly care about are the products and the quality of recommendations that the engine will give. Such cognitive computing methods can take the quality of your recommendations to the next level. If you have further question feel free to contact us!
