Broad collaborative filtering with adjusted cosine similarity by fusing matrix completion

Collaborative filtering (CF) algorithms provide personalized recommendations based on user preferences and they are widely applied in various domains including social media and video platforms. Recently, the broad learning system (BLS) has been incorporated into CF. The combination of BLS and CF produces more accurate recommendation results due to the fact it can efficiently learn the non-linear relationship between items and users. However, the BLS-based CF adopts cosine similarity to seek several nearest neighbors when constructing features. This feature construction process generally leads to the difference of user rating scales and thus affects the calculation accuracy of the nearest neighbors. Furthermore, like many other CF algorithms, the BLS-based version cannot effectively handle with the data sparsity problem. The above two issues frequently result in poor recommendation performance for the BLS-based CF. To address these problems, a broad collaborative filtering with adjusted cosine similarity (ACOS), named MC-ABCF, is proposed by fusing matrix completion (MC). The matrix completion technique is first used to complete the rating matrix in order to relieve the phenomenon of data sparsity. Subsequently, the adjusted cosine similarity is utilized to find the nearest neighbors of a given user or item. As a result, the problem of rating scales difference between different users is avoided to some extent. The BLS is finally employed to establish complex nonlinear relationships between users and items. Extensive experiments on three benchmark datasets demonstrate that the proposed MC-ABCF model can mitigate the difficulties of data sparsity and user rating scales difference to a certain extent. Taking RMSE as an example, the MC-ABCF algorithm achieved an average improvement of 8.48 % compared to the BCF model across three datasets. In addition, ablation studies have shown the application of MC or ACOS is beneficial for the recommendation performance.