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Latent Semantic Indexing-Based Hybrid Collaborative Filtering for Recommender Systems

  • Research Article-Computer Engineering and Computer Science
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Abstract

Advances in information technologies increase the number and diversity of digital objects. This increase poses significant problems in reaching the target audience of digital products. Recommender systems (RS) that propose digital objects according to user profiles aim to deal with these problems. In collaborative recommender systems (CRS), recommendations are made considering similar digital objects. In this study, a hybrid model based on latent semantic indexing (LSI) is proposed for the CRS. User-based, item-based, and hybrid models have been developed by using the LSI, which is generally encountered in text analysis, information retrieval, and information access. These improved models were compared with the models based on the most commonly used Pearson correlation coefficient (PCC) in the CRS. Accordingly, it was observed that predictions were better in all models based on LSI. The developed models have lower computational complexity due to the dimension reduction process. Besides, the proposed hybrid model produced more accurate predictions than the user-based and the item-based models.

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  1. Department of Computer Engineering, Faculty of Engineering and Architecture, Kırıkkale University, Kırıkkale, Turkey

    Fahrettin Horasan

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Horasan, F. Latent Semantic Indexing-Based Hybrid Collaborative Filtering for Recommender Systems. Arab J Sci Eng 47, 10639–10653 (2022). https://doi.org/10.1007/s13369-022-06704-w

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