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Scalability and sparsity issues in recommender datasets: a survey

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Abstract

Recommender systems have been widely used in various domains including movies, news, music with an aim to provide the most relevant proposals to users from a variety of available options. Recommender systems are designed using techniques from many fields, some of which are: machine learning, information retrieval, data mining, linear algebra and artificial intelligence. Though in-memory nearest-neighbor computation is a typical approach for collaborative filtering due to its high recommendation accuracy; its performance on scalability is still poor given a huge user and item base and availability of only few ratings (i.e., data sparsity) in archetypal merchandising applications. In order to alleviate scalability and sparsity issues in recommender systems, several model-based approaches were proposed in the past. However, if research in recommender system is to achieve its potential, there is a need to understand the prominent techniques used directly to build recommender systems or for preprocessing recommender datasets, along with its strengths and weaknesses. In this work, we present an overview of some of the prominent traditional as well as advanced techniques that can effectively handle data dimensionality and data sparsity. The focus of this survey is to present an overview of the applicability of some advanced techniques, particularly clustering, biclustering, matrix factorization, graph-theoretic, and fuzzy techniques in recommender systems. In addition, it highlights the applicability and recent research works done using each technique.

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Notes

  1. LSI is an application of SVD in document retrieval [100].

  2. For measuring the accuracy of predictions, Mean Absolute Error (MAE) is a popular measure. MAE defines the deviation of predictions given by recommender from the actual preferences given by the user.

  3. A rating matrix of n users and m items is represented by n rows and m columns. Cell entries or elements in the rating matrix depict the rating of users on items. The rating rui shows rating of a user u on an item i.

  4. Demographic filtering creates demographic profile of a user based on his attributes such as age, gender, occupation etc.

  5. http://www.netflixprize.com/.

  6. https://en.wikipedia.org/wiki/Stochastic_gradient_descent.

  7. Available at http://www.grouplens.org/data.

  8. An algorithm that orders the items in I (set of all items) for a user u \( \in {\text{U}} \) according to some similarities between vertex u and the vertices in I, which are defined by the structure of graph G is known as a scoring algorithm [26].

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    Monika Singh

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Singh, M. Scalability and sparsity issues in recommender datasets: a survey. Knowl Inf Syst 62, 1–43 (2020). https://doi.org/10.1007/s10115-018-1254-2

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