Collaborative Filtering (CF) Recommender Systems are a subset of information filtering, which seeks to predict interests of users, and based on that, recommend products that quite likely are of interest to the users. These systems are designed with the objective of addressing the challenge of making accurate predictions, based on known user preferences. They can prove to be an indispensable tool in various application areas like movies, music, news, books, research articles and products in general.

Broad Application: CF Recommender Systems can be readily applied across a variety of domains such as e-commerce, media platforms, online advertising, social networks etc, making them versatile and adoptable.

Data Dependency: These systems rely heavily on the collection, interpretation and analysis of user behavioral data. The effectiveness is directly related to the quantity and quality of data collected.

Precision: Precision in predicting user's interests is one of the strongest suits of collaborative filtering systems. They use various algorithms to analyze existing user data to make accurate recommendations.

Dynamic Adaptable: The systems can learn and adapt with changing user preferences and behaviors, making continuous improvement in their recommendations.

Ease of Deployment: Such Recommender Systems can be implemented quickly, due to the pre-designed algorithms and models that power them. They require minimal customization, based strictly on the nature of items being recommended.

Enhanced User Experience: These recommender systems enable businesses to provide personalized recommendations to users, enhancing their experience and loyalty.

Increased Sales: They have proven effective in boosting sales through the "recommended items" feature that facilitates upselling and cross-selling.

Scalability: CF Systems, being model based, can efficiently cope with larger datasets; as the user base grows, the system evolves and improves in its capability to make recommendations.

Zero Knowledge: They require no knowledge about the items being recommended. This means they can make recommendations in diverse fields with equal effectiveness, irrespective of specific product knowledge.

Data Sparsity: Collaborative Filtering relies on data to make recommendations. When there is limited user-item interaction data, it's difficult to provide reliable recommendations.

Cold Start: A major challenge faced is the inability to provide recommendations to new users, due to lack of historical data for them.

Limited Diversification: The system may be biased towards recommending popular items and can develop a tendency to recommend items similar to those already rated by users, limiting diversification.

Privacy Concern: Since CF systems depend on user data, privacy remains an issue. Users might not want to share their data due to privacy concerns and this can impact the system's effectiveness.

Implementing a Collaborative Filtering Recommender System involves careful planning and analysis of user data, the identification of suitable filtering algorithm, extensive testing and evaluating to ensure accurate recommendations, followed by continuous monitoring and optimization. It's also crucial to pay attention to ethical issues regarding data privacy and to insure transparency with users regarding the use of their data.

In a nutshell, the Collaborative Filtering Recommender Systems, despite their constraints, provide a powerful method to generate recommendations and enhance user experience and they continue to evolve with technological advancements to overcome their limitations. It is an ever-evolving field of AI and Machine Learning which is expected to become more accurate and customized with more and more available user data.