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, this article 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.
These filtering methods are based on the description of an item and a profile of the user’s preferred choices. In a content-based recommendation system, keywords are used to describe the items; besides, a user profile is built to state the type of item this user likes. In other words, the algorithms try to recommend products which are similar to the ones that a user has liked in the past. The idea of content-based filtering is that if you like an item you will also like a ‘similar’ item. For example, when we are recommending the same kind of item like a movie or song recommendation. This approach has its roots in information retrieval and information filtering research.
A major issue with content-based filtering is whether the system is able to learn user preferences from users actions about one content source and replicate them across other different content types. When it limits the system to recommending the content of the same type as the user is already using; the value from the recommendation system is significantly less when it recommends other content types from other services. For example, recommending news articles based on browsing of news is useful, but wouldn’t it be much more useful when it suggests music, videos from different services based on the news browsing.
This filtering method is usually based on collecting and analyzing information on user’s behaviors, their activities or preferences; thus, predicting what they will like based on the similarity with other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content; and then it is capable of accurately recommending complex items such as movies without requiring an “understanding” of the item itself. Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future. And that they will like similar kinds of items as they liked in the past. For example, if a person A likes item 1, 2, 3 and B like 2,3,4 then they have similar interests and A should like item 4 and B should 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 alike matrix, we can easily recommend alike 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.
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 shows that combining collaborative and content-based recommendations can be more effective. They can implement hybrid approaches by making content-based and collaborative-based predictions separately and then combining them. Further, by adding content-based capabilities to a collaborative-based approach and vice versa; or by unifying the approaches into one model.
Several studies focused on comparing the performance of the hybrid with the pure collaborative and content-based methods. They demonstrate that hybrid methods can provide more accurate recommendations than pure approaches. Such methods can be used to overcome the common problems in recommendation systems. For example cold start and the data paucity problem.
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.