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Collaborative Filtering (CF) is a widely used technique in recommendation systems. It provides personal recommendations for users based on their preferences. However, this technique suffers from the sparsity issue which occurs due to a high proportion of missing rating scores in a rating matrix. Several factorization approaches have been used to address the sparsity issue. Such techniques have also been considered to tackle other challenges such as the overfitted predicted scores. Nevertheless, they suffer from setbacks such as drift in user preferences and items’ popularity decay. These challenges can be solved by prediction approaches that accurately learn the longterm and short-term preferences integrated with factorization features. Nonetheless, the current temporal-based factorization approaches do not accurately learn the convergence of the assigned k clusters due to a lower number of short-term periods. Additionally, the use of optimization algorithms in the learning process to reduce prediction errors is time-consuming which necessitates a faster optimization algorithm. To address these issues, a new temporal-based approach named TWOCF is proposed in this paper. TWOCF utilizes the elbow clustering method to define the optimal number of clusters for the temporal activities of both users and items. This approach deploys the whale optimization algorithm to accurately learn short-term preferences within other factorization and temporal features. Experimental results indicate that TWOCF exhibits a superior CF prediction accuracy achieved within a shorter execution time when compared to the benchmark approaches. Keywords—Collaborative filtering; matrix factorization; temporal-based approaches; whale optimization

Temporal-based Optimization to Solve Data Sparsity in Collaborative Filtering