An Online Recommendation System, often known as a recommendation engine, is a technology that offers relevant suggestions to users based on specific criteria. These systems exist with a simple mission – to offer personalized content to individual users drawn from a large pool of options.

• Broad Accessibility: Recommendation systems are predominantly found in online platforms, rendering them globally accessible whenever an Internet connection is available.

• Algorithmic Functionality: They employ sophisticated algorithms which track user behavior and patterns to predict and suggest relevant options.

• Customization: These systems are highly tailored to individual users, enhancing the user experience by providing personalized recommendations.

• Safety Measures: They integrate safety nets and measures to ensure users’ privacy and data security.

• Efficiency: Streamlining the process of product or content discovery, online recommendation systems can be surprisingly cost-effective.

Online Recommendation Systems are highly cherished in the digital sphere because of their extensive applications stretching across various industries including e-commerce, music streaming, online dating, and more.

Implementing an online recommendation system is a complex process that involves numerous steps. Initially, the organization has to identify the need for such a system and the specific objectives it wishes to accomplish. Following this, the selection of an appropriate recommendation algorithm is critical.

Furthermore, the organization must put a robust data management system to gather essential user data, which will drive the recommendation algorithm. To ensure user privacy, organizations should implement advanced security measures and ensure transparency in data usage.

The popularity of online recommendation systems has been motivated by several benefits they offer:

• Personalization: Probably the most crucial advantage is the degree of personalization that these systems provide. By tailoring recommendations to an individual's past behavior, preferences, and interaction history, users meet a more satisfying, personalized experience.

• Increased Engagement: Personalized recommendations can significantly boost user engagement on a platform which, in turn, can enhance user loyalty and augment revenues.

• Enhanced User Experience: Seamless discovery of new products or content significantly improves the user experience. Without a recommendation system, users might find it strenuous to discover new content or products.

• Efficiency: Online recommendation systems are incredibly efficient - their algorithmic functionality considerably reduces the time taken to discover new content or products.

• Visibility of Products: They assist in highlighting lesser-known products or content that users might appreciate but would not encounter in their standard browsing habits.

• Augmented Sales: Generally, personalized recommendations result in higher click-through rates, thus leading to increased sales and revenue.

While the merits of online recommendation systems are impressive, their utilization might present a few detriments:

• Privacy Concerns: These systems require access to users' data, which can raise privacy and data protection concerns.

• Limitations of Algorithms: Algorithms might fail to interpret complex human behaviors accurately, leading to inaccurate recommendations.

• Over-personalization: Too much personalization can potentially make users feel trapped in a repetitive content loop, denying exposure to diverse content.

• Potential Bias: These systems might cause platforms to favor specific content or products, resulting in an unfair advantage and potentially misleading recommendations.

In synopsis, online recommendation systems, when used strategically and with respect for user privacy, can significantly enhance the user experience and drive sales and engagement. It's equally crucial that the implementation process is methodical, with a focus on the selection of an appropriate recommendation algorithm, robust data management, and stringent security measures. Consequently, the organization can fully harness the power of online recommendation systems for their benefit.