Recommendation System

Nowadays, interactions between consumers and firms are increasing in complexity and intensity. Companies need to learn how to deal with it in order to remain competitive.


Moreover, when looking at the world pyramid of ages, we can see that if a 26 years old male is looking forhis optimal female partner (assuming that “optimal” implies the girl is in the same age range and also that there is only one optimal choice), he has only one in 275,001,000 (- the number of females he already met) chance to meet her. His chances fall even lower if we consider that there is no reason why both partners should be in the same age range. Thus unless the guy is very lucky, he might need some help to find the right partner.

Fig. 1 as interaction

Here is where jumps in; this company managed to build its whole business around these interactions and around the fact that people need assistance for making optimal decisions in this overloading quantity of different choices. First the customers are actively involved in the process and are required to give information about them and their preferences to the company on both a compensatory and non-compensatory filtering process (yellow arrows on Fig1.). Based on this information, the company proposes different options (C1, C2 & C3) that are considered as the most optimal choices for the customer who made a request (C4 on Fig.1).

Finally, is a formidable opportunity to broaden the range of interactions among potential partners and thus increase the chances to meet a more optimally suited partner.

However, because data collection on consumers is based on their active contribution, there is still the risk of supplying false information:

A consumer thinks to ask for that…      … but in fact                        … he gets that.




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