The benefits of using a recommendation system. Part #2

Recommendation systems are a large class of models, the purpose of which is to increase business and service performance due provision of relevant recommendations to the user at the right place, at the right time and through the right communication channel. Especially when you use the right software development company.

PRINCIPLE OF WORK OF RECOMMENDATION SYSTEMS AND AREAS OF APPLICATION

The tasks of the recommendation systems are simple and clear — they are used to offer those products or services, in which the client is most likely interested. Recommendation systems operate at two “levels”:

  • Level 1 — global assessments; features and preferences that change very slowly; interesting pages; dependence on characteristic features of the user, such as gender, place of residence, etc.;
  • Level 2 — short-term trends and rapid changes in interest over time.

To prepare quality recommendations, explicit or implicit profiling is used: in case of explicit profiling, it is necessary to receive completed questionnaires the user to identify his/her preferences. The disadvantage of the method is that it is rather difficult to make the user rate.

In the case of implicit profiling, the user’s actions are recorded: what the user looked at, what product was added to the basket, what he/she commented on, what purchase he/she made. The rating is created automatically. The disadvantage of the method is uncertainty: if the user looked at the product, it is not known whether he/she liked it or not; if the user has not bought the product, then it is again not known what caused him/her to take this decision.

It is also possible to combine two approaches: if there is no history of transactions — surveys are used, when it appears — account transactions are taken into account. The areas of application of recommendation systems are diverse: searching for movies, music, scientific articles, retail, social networks, e-commerce, online banking, etc.

Perhaps one of the most well-known examples of the implementation and use of recommendation systems is Netflix, a provider of video content on a rental basis as a streaming service. The company began its activities from sending VHS tapes and DVDs to customers by subscription. The user watched and sent disks back, he/she received the next ones. Currently, the Netflix (BellKor) system that is a combination of 27 recommendation algorithms is considered the most technologically advanced system in the world.

The opportunity to collect data has simplified and at the same time, complicated predictions regarding user behavior and preferences. Special attention is also required to ensure confidentiality in the work of recommendation algorithms because they can often predict such results or reveal patterns, of which the user did not have the least suspicion or did not want them to become known. A good recommendation system should not only manage this issue but also manifestations of unfair competition expressed in deliberately raising ratings of some products and undervaluing competing products, for example, with the help of negative reviews and comments.

Recommendation systems are a large class of models that can be helpful for almost every business. The purpose of the recommendation system is to assist the business in selling more through a timely provision of recommendations to the client at the right place, at the right time, and through the right communication channel.

However, there is a widespread stereotype that still prevents widespread use of recommendation systems in business activities. It seems to many people that, in reality, the introduction of recommendation algorithms is too complicated and requires a global restructuring of the entire process of collecting and processing data, as well as changes in business processes, logistics, etc. Many people have doubts and cannot estimate the ROI (return on investment) in such transformations. These doubts are completely unreasonable, because, in fact, recommendation systems can be useful to almost every business, and, data that is already being collected is ofter sufficient to begin recommending.

Relevant recommendations reduce the time required to search goods and services and significantly increase the likelihood that many items that may be of interest for the user occur within his/her line of sight. As a result, users’ loyalty and satisfaction with web services increases. As a rule, users also interact with a large number of products, and this leads to an increase in consumption and profit growth. In addition, newsletters, personalized advertising materials, and push notifications encourage users to return, increase the frequency of visits by regular users and reduce customer outflow.

The vivid example of using recommendation systems in real life, especially in the user’s experience is the Age Line, developed by the LaSoft team.

Age Line is a massive user portal that helps seniors find places that are free of ageism and are forward-thinking with regard to giving practical answers to problems faced by older adults. Age Line is intended to be an open-sourced set of solutions to an aging society’s challenges.

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