WHAT ARE RECOMMENDATION SYSTEMS AND DT-AI?
We are living in an Era where social media and e-commerce play a pivotal role in the lives of common man. The rise of YouTube, Amazon, Netflix, Hulu and many other online platforms have taken a major place in one’s daily routine. Starting from Shopping (e-commerce) to Online advertisement (preferences and liking of a user) recommender systems are key to the growth of a product. So, when dealing with such recommender systems, trust should also be incorporated with the preference list as our preferences can be preserved and guarded from external breaches and leakages. This gives rise to the concept of Dynamic TrustTM which is offered by Revolutionary Integration Group (RIG).
Dynamic TrustTM along with Artificial Intelligence can assist the recommendation systems to build a recommendation engine which can analyze individual purchases and behavior and detect preference patterns and develop a similar suggestion engine. Also, DT-AI can help in keeping the recommendation engine secure by building a trust level according to user requirements. This approach allows the recommendations to be aligned across multiple clients, with common interests, emerging trends, and historical selections.
TYPES OF RECOMMENDATIONS
Considering a “smart” proposal motor to execute for your business and communicating with individuals who need to execute such a framework we have the following fields of recommendation systems:
- Related Product recommendations
A client has just bought a cap; why not recommend a scarf that coordinates with this cap so that the look will be finished? This action is about related item proposals, and this utilization case is well known among online stores. It is frequently actualized by AI calculation methods as “Complete the look” or “You may also like” areas in online design stores like ASOS, H&M, Pandora, and numerous others. 
- Location-based recommendations
With a location-based recommendation system, it is possible to detect customers who are nearby physical stores or restaurants and send them an invitation to come in. This approach certainly drives customer engagement and significantly increases the chances that users will finally make purchases. 
- Alternative product recommendations.
Now and again, it happens that a specific item is, as of now, unavailable. This shortage should not turn into motivation to let a client leave with no buy. To forestall this, organizations actualize a shrewd proposal framework that recommends elective alternatives.
Recommendation systems have an impact and benefit on numerous domains that we use in daily life. Recommendation systems make human tasks efficient and less time consuming. Like any other technological advancement, this also has certain threats, and thus incorporates the necessity to implement Dynamic Trust’sTM security level model as the recommender collects personal information for understanding our preferences. This leads to privacy-concerns of the public and can also lead to malicious activity leading to manipulate the recommendations. By implementing Dynamic TrustTM it becomes highly valuable as it helps to build a secure network for a great future.
For more information visit www.rigroup.co
- Wang, J., & Tang, Q. (n.d.). Recommender systems and their security concerns. Retrieved from https://eprint.iacr.org/2015/1108.pdf
- 5 use cases of AI based recommendation systems – ISS Art Blog | AI | Machine Learning | Computer Vision. (2020). Retrieved 8 December 2020, from https://blog.issart.com/5-use-cases-of-ai-based-recommendation-systems/