The Web Recommendation System

The Web Recommendation System
Name
Institutional Affiliation
The Web Recommendation System
Background and Purpose
Recommendation systems, also known as recommendation engines, predict what users or customers want by analyzing their behaviors based on their past preferences. Almost every business would benefit from such systems based on the breadth and depth of data. In terms of breadth, businesses serving a handful of clients who behave in different ways might not receive maximum benefit from automation because humans are considered to be better compared to computers in terms of learning. In this respect, employees will want to apply logic, quantitative, and qualitative understanding of clients in making accurate recommendations. However, other businesses would benefit from recommendation systems based on the depth of data because having a single data point for every client might not be important to recommendation systems. Such businesses would require deep data about their clients’ online activities as well as offline purchases to guide precise recommendations. This understanding helps identify the industries that would largely benefit from recommendation systems such as e-commerce, retail, media, banking, telecom, and utilities. Recommendation systems are critical for increasing sales or conversions, increasing user satisfaction, increasing loyalty and share of mind, and reducing churn. Automated systems have helped companies get recurring sales effortlessly and increase their users’ satisfaction. The systems also increase customers’ loyalty and share of mind as they spend more time on businesses’ websites. This increases their familiarity with individual brands and chances of making future purchases. Another important aspect of recommendation systems is that they reduce churn, and one of the important channels of re-engaging customers is through emails powered by recommendation systems. Providing customers with coupon codes or discounts can be one of the best ways of re-engaging clients but could be costly. However, coupling discounts and coupons with recommendation systems can significantly increase the levels of conversions. Companies such as Amazon, Netflix, Spotify, and LinkedIn have greatly benefited from recommendation systems in their businesses. Personalization has always played a critical role in enhancing service levels in e-commerce settings. Besides, recommendation systems techniques also support critical personalization systems that can customize product recommendations and advertisement displays.
E-commerce is one of the industries that recommendation systems were first employed. Millions of customers use the internet to buy products and services, and through their online behaviors, e-commerce businesses can generate precise recommendations for individual customers. The second industry that benefits from recommendation systems are the retail sector. The most valuable data is the shopping data because it is the most direct data point that tells about a customer’s intent. Retailers who utilize shopping data are always at the forefront of businesses that make the most precise recommendations. Thirdly, the media industry benefits from recommendation systems the same way as e-commerce industries. The media industry embraced recommendation systems because it can understand users’ preferences based on their browsing history and offer relevant news to individual users. The fourth industry that benefits from recommendation systems are the banking sector. Banking is a market product that many people consume, and banking for masses and small and medium enterprises (SMEs) have largely attracted recommendation systems. Banks need to understand their clients’ financial situations, past preferences, and the data of their similar users. The telecom industry has also benefited from recommendation systems at the same margin as the banking sector. Telecommunication companies have access to thousands and millions of clients whose transactions are recorded. However, their range of products is limited compared to other sectors, making recommendation systems the best choice for this industry. Lastly, the utility industry also benefits from recommendation systems, just like the telecom sector, although this sector has a narrower range of products.
Humans have continued to evolve and develop certain traits of sophisticated thinking, developing tools, and using languages over the last 100,000 years (Sharma & Singh, 2016). This very idea has been seen among cavemen, ants, and other organisms. For example, ants can be spotted running around houses and walking in lines behind others that lead them to find food. About 4000 to 1200 BCE, there was a rapid advancement in ancient civilization. Around this time, people used recommendation systems to understand what crops they could cultivate, the best time to plant, and different religions to follow. At the same time, between the 11th and 18th centuries during colonization, recommender systems became transformed, and colonies could determine the territories to absorb based on manpower, land fertility, and availability of resources (Sharma & Singh, 2016). In marriages, families could arrange matches for couples based on the opinions of known family members. During these times, there were no computers or advanced technologies as seen today. However, recommender systems existed to hel...