Ever found yourself endlessly scrolling through different movie categories on Netflix looking for that one movie that is of interest to you? Or browsing through the pages on ASOS until you see that one particular item you like? As the volume of Big Data today continues to expand, the countless choices on the Internet can be overwhelming. This is where recommender systems come into place.
Recommender (or recommendation) systems are information filtering systems that search through large amounts of data to collect and provide personalized content and services to its users.
Using these systems result in substantial benefits both to buyers:
- Timely access to customized and relevant data on the web.
- May lead to discovery of products that they didn’t know existed.
- Increased customer satisfaction.
- Higher sales and profits as customers tend to buy more.
- Increased cross-sell as some systems tend to suggest additional products that are bought together.
- Increased interaction and loyalty from the customers with an increase in customer satisfaction.
Today, recommender systems have become so popular among restaurants and in movie and travel industries, as well as in companies like Amazon, LinkedIn, Spotify and more.
Several search engine systems like Google have tried implementing similar algorithms to mimic recommender systems. Search Engine Optimization can be one such example. However, these algorithms lack prioritization and personalization. So how do these systems work? Artificial intelligence technologies like machine learning models work on various filtering techniques to generate reports summarizing the past behaviors of shoppers. This is done by actively tracking their history, be it in browsing, purchases or ratings even. The models consistently study how each user surfs the Internet, what they are looking for and how much they like the products they see online. They are continuously on the look for other users with similar profiles to track their activities as well. Recommender systems mainly depend on machine learning and its ability to learn, adapt and predict the importance that a user will attach to a product or service to make decisions and recommendations.
However, on understanding how recommender systems work, the question of risks regarding user privacy arise. The fact that large amounts of personal data is readily available online cannot be denied. With these systems having access to such data, it does pose a severe threat to an attacker inferring this information. The most common example of this data being misused is in targeted advertisements, where third parties have the potential to expose sensitive information for their benefit. In such cases, the user only has limited control over the privacy risks resulting from using these recommender systems. Instead, integrating privacy into the design of these systems may prove more effecting in safeguarding user privacy.
Therefore, considerable effort to develop practical recommendation solutions must be taken to provide adequate privacy guarantee while also ensuring high quality recommendations to users because recommender systems are the face of the new opportunities of retrieving information.