31.What is Collaborative Filtering?
Collaborative filtering (CF) is a method used by some recommender systems. It consists of two senses, a narrow one and a more general one. In general, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources.
32.What are the challenges of Collaborative Filtering?
1. Scalability
2. Data sparsity
3. Synonyms
4. Grey sheep Data sparsity
5. Shilling attacks
6. Diversity and the Long Tail
33.What do you understand by Big data?
Big data is a buzzword, or catch-phrase, which describe a massive volume of both structured and unstructured data that is so large which is difficult to process using traditional database and software techniques.
34.What do you understand by Matrix factorization?
Matrix factorization is simply a mathematical tool for playing around with matrices, and is therefore applicable in many scenarios by which one would find out something hidden under the data.
35.What do you understand by term Singular Value Decomposition?
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It has many useful applications in signal processing and statistics.
36.What do you mean by Recommender systems?
Recommender systems or recommendation systems (sometimes replacing “system” with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the ‘rating’ or ‘preference’ that a user would give to an item.
37.What are the applications of Recommender Systems?
Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general.
38.What are the two ways of Recommender System?
Recommender systems typically produce a list of recommendations in one of two ways: Through collaborative or content-based filtering. Collaborative filtering approaches building a model from a user’s past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in. Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.
39.What are the factors to find the most accurate recommendation algorithms?
1. Diversity
2. Recommender Persistence
3. Privacy
4. User Demographics
5. Robustness
6. Serendipity
7. Trust
8. Labeling
40.What is K-Nearest Neighbor?
k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.
