Content-boosted Collaborative filtering approach to reduce Cold Start and Data Sparsity problems

EasyChair Preprint 2325

Abstract

Recommendation systems suffer from problems related to scalability, data sparsity and cold starts, resulting in poor-quality predictions. Hybrid techniques, such as content-boosted collaborative filtering (CBCF) and/or combine collaborative filtering methods with other recommendation systems are highly essential to alleviate the drawbacks and to improve the overall prediction rate. Obviously, the combination of algorithms could make more accurate recommendations. CBCF could be used with a combination of a pure content-based predictor (pure CF) and user-based collaborative filtering (UBCF), which improves prediction quality and thus minimizes cold start and data sparsity problems. In this paper, a modified CBCF algorithm by implicitly collecting user ratings through a user-interest model has been developed. Experimental results were tabulated.

Keyphrases: Correlation Similarity, Data Sparsity, Mean Absolute Error, cold start, collaborative filtering

@booklet{EasyChair:2325,
  author    = {Raja Sarath Kumar Boddu},
  title     = {Content-boosted Collaborative filtering approach to reduce Cold Start and Data Sparsity problems},
  howpublished = {EasyChair Preprint 2325},
  year      = {EasyChair, 2020}}