Knowledge-based
Posted: Wed Jan 22, 2025 4:32 am
Here, data in any area is used: about users, products, purchases, etc. – any information that will help to form recommendations. The preferences of other users are not taken into account here. There are case-based, demographic-based, utility-based, critique-based, whatever-you-want-based, etc. The number of reasons used to compile a rating is limited only by the imagination of the platform developers. Any feature can be used, depending on the specifics of the product and the target audience.
Thus, the Apple reStore online store recommends products to potential buyers based on their product viewing history. When buying a PS4 game console, the client is also offered to buy a virtual reality helmet, popular games, and other accessories. This way, the company manages to significantly increase the check per client.
Recommendation systems homeowner database are highly accurate in predicting the user's actual interests, generating truly useful advice. The algorithm used by M.Video can recommend an advanced stereo system when buying a TV, but not a gas stove. The disadvantage of the system is the need for a large amount of initial information and the difficulty of obtaining and systematizing it. However, the result is worth the investment and time.
Hybrid recommender systems
Hybrid
Perhaps, in any field of activity there are enthusiasts who try to synthesize one tool from several elements, combining the advantages and devoid of the disadvantages of their prototypes. Different types of recommender systems are no exception in this sense. Combined algorithms make it possible to use the advantages of different approaches.
Hybrid schemes are usually used by large companies, since the implementation of such systems is an extremely complex technical task that requires the highest qualifications of marketers and programmers.
Accordingly, there can be no universal recipe here. The capabilities of the system, its efficiency depend on the skills of the developers and the tasks set before them. If we recall the same Netflix, the recommendation system it uses consists of 27 different algorithms.
Most often, several methods of combination are used:
Separate use of collaborative and content algorithms, the data from which is then combined.
Implementation of individual content rules into a collaborative algorithm.
Implementation of individual collaborative elements in the content approach.
Creating a new algorithm that uses equally elements of both presented above.
As a rule, the described approaches form the basis into which other elements are introduced depending on the tasks and sphere of activity. Similar to the knowledge-based algorithm, the main disadvantage of hybrid approaches
Thus, the Apple reStore online store recommends products to potential buyers based on their product viewing history. When buying a PS4 game console, the client is also offered to buy a virtual reality helmet, popular games, and other accessories. This way, the company manages to significantly increase the check per client.
Recommendation systems homeowner database are highly accurate in predicting the user's actual interests, generating truly useful advice. The algorithm used by M.Video can recommend an advanced stereo system when buying a TV, but not a gas stove. The disadvantage of the system is the need for a large amount of initial information and the difficulty of obtaining and systematizing it. However, the result is worth the investment and time.
Hybrid recommender systems
Hybrid
Perhaps, in any field of activity there are enthusiasts who try to synthesize one tool from several elements, combining the advantages and devoid of the disadvantages of their prototypes. Different types of recommender systems are no exception in this sense. Combined algorithms make it possible to use the advantages of different approaches.
Hybrid schemes are usually used by large companies, since the implementation of such systems is an extremely complex technical task that requires the highest qualifications of marketers and programmers.
Accordingly, there can be no universal recipe here. The capabilities of the system, its efficiency depend on the skills of the developers and the tasks set before them. If we recall the same Netflix, the recommendation system it uses consists of 27 different algorithms.
Most often, several methods of combination are used:
Separate use of collaborative and content algorithms, the data from which is then combined.
Implementation of individual content rules into a collaborative algorithm.
Implementation of individual collaborative elements in the content approach.
Creating a new algorithm that uses equally elements of both presented above.
As a rule, the described approaches form the basis into which other elements are introduced depending on the tasks and sphere of activity. Similar to the knowledge-based algorithm, the main disadvantage of hybrid approaches