What is sparsity problem?

Problem definition. Data sparsity is the term used to describe the phenomenon of not observing enough data in a dataset. Dataset as used in this paper, includes many users and items. It is noticeable that the items rated by users are small in number according to the dataset.

Is sparsity an issue with collaborative filtering?

The sparsity problem occurs when available data are insufficient for identifying similar users (neighbors) and it is a major issue that limits the quality of recommendations and the applicability of collaborative filtering in general.

What is scalability problem in recommender systems?

The most popular recommender systems employ collaborative filtering algorithms. These methods require large amounts of training data, which cause scalability problems. One approach to solve the scalability problem is to use clustering algorithms.

How do you deal with sparsity?

The solution to representing and working with sparse matrices is to use an alternate data structure to represent the sparse data. The zero values can be ignored and only the data or non-zero values in the sparse matrix need to be stored or acted upon.

How do you solve cold start problems?

The cold start problem may be overcome by introducing an element of collaboration amongst agents assisting various users. This way, novel situations may be handled by requesting other agents to share what they have already learnt from their respective users.

What are problems with collaborative filtering?

Collaborative filtering systems suffer from the ‘sparsity’ and ‘new user’ problems. The former refers to the insufficiency of data about users’ preferences and the latter addresses the lack of enough information about the new-coming user.

What is cross domain recommender systems?

Cross domain recommender systems (CDRS) can assist recommendations in a target domain based on knowledge learned from a source domain. CDRS consists of three building blocks: domain, user-item overlap scenarios, and recommendation tasks. User-item overlaps were found to have equal contribution.

What is meant by sparsity in power system?

A matrix is characterized as sparse, whenever a sig- nificant percentage of it’s elements are equal to zero. The admittance or Y matrix of a power system is relatively sparse, whereas the Z of impedance matrix of the same system is proportionately full, i.e. very few zero elements.

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