The main objective of a Recommender System is to leverage user data and preferences to generate personalized recommendations that align with their interests and needs.

Collaborative Filtering approaches utilize user ratings and interactions to identify similar users or items and make recommendations based on these similarities.

Content-Based Filtering methods analyze the attributes and features of items to identify similarities and make recommendations based on these similarities.

Mean Average Precision (MAP) is a widely used metric for evaluating Recommender Systems. It considers both the precision and recall of the recommendations and provides a comprehensive measure of the system's performance.

One of the primary challenges in building a Recommender System for a new domain or application is the lack of sufficient user data and ratings. This can make it difficult to train and evaluate the system effectively.

Active learning is a technique used to address the cold start problem in Recommender Systems. It involves actively querying users for their preferences and feedback to gather more data and improve the accuracy of the recommendations.

In a Weighted Hybrid Recommender System, the predictions from Content-Based Filtering and Collaborative Filtering are combined using a weighted average, where each approach contributes to the final recommendation.

Social Filtering approaches utilize social network connections and interactions to identify similar users and make recommendations based on their preferences and behaviors.

Regularization techniques are employed in Recommender Systems to prevent overfitting and enhance the generalization performance of the models.

Holdout Evaluation is a widely adopted protocol for evaluating Recommender Systems. It involves splitting the dataset into training and test sets, training the model on the training set, and evaluating its performance on the test set.

In e-commerce, the primary objective of a Recommender System is to increase sales and revenue by providing personalized recommendations that are relevant to each customer's preferences and interests.

Deep Learning-Based Recommender Systems utilize deep neural networks to learn complex representations of users and items, enabling the generation of personalized recommendations.

One of the primary challenges in deploying a Recommender System in a production environment is ensuring scalability and performance to handle large volumes of data and users.

Imputation methods are often employed to address the sparsity problem in Recommender Systems. These methods aim to estimate missing values in the user-item interaction matrix based on various techniques.

Diversification techniques are used in Recommender Systems to increase the variety and novelty of the recommendations, ensuring that users are exposed to a broader range of items.