Cosine Based Latent Factor Model for Precision Oriented Recommendation

Abstract

Abstract-Recommender systems suggest a list of interesting items to users based on their prior purchase or browsing behaviour on e-commerce platforms. The continuing research in recommender systems have primarily focused on developing algorithms for rating prediction task. However, most e-commerce platforms provide 'top-k' list of interesting items for every user. In line with this idea, the paper proposes a novel machine learning algorithm to predict a list of 'top-k' items by optimizing the latent factors of users and items with the mapped scores from ratings. The basic idea is to learn latent factors based on the cosine similarity between the users and items latent features which is then used to predict the scores for unseen items for every user. Comprehensive empirical evaluations on publicly available benchmark datasets reveal that the proposed model outperforms the state-of-the-art algorithms in recommending good items to a user.

Ant Collaborative Filtering Addressing Sparsity and Temporal Effects

“…Other techniques, such as Latent Semantic Indexing (LSI) from information retrieval, are also often adopted [23], [35]. One drawback of these approaches is that potentially useful information might be unavoidably removed during the reduction process.…”

Transfer Learning for Resolving Sparsity Problem in Recommender systems: Human Values Approach

“…Cross-validation (Kumar, Bala, & Srivastava, 2016) is used to assess the performance of proposed model over the classical neighborhood approach on various performance measures. The data set is partitioned into 5 equal disjoint sets with 4 datasets used for training and one left out dataset for testing the model.…”