The existing recommender systems faced a lot of challenges as a result of data sparsity problem, which decreases the recommendations accuracy to the target audience. Though there are numerous studies conducted to reduce the data sparsity problem. However, these studies fail to consider the subjectivity of human beings’ ratings based on lenient and stringent, which lead to unsuitable recommendations. Similarly, it is unable to compute the similarity between pair of users when the ratings among them did not overlap leading to inaccurate recommendations. This work is intended to enhance the adaptability of the system to sparse data by proposing a User Based Collaborative Filtering Recommender System incorporated with data refinement method that utilised a subjective function to identify lenient and stringent ratings in a rating matrix. The function identifies the user’s lenient and stringent ratings to take care of human subjectivity with regard to rating of the items. It also employed a subjective similarity function that used the subjective function for computing the similarity between users. The top similar neighbors obtained from the similarity computations are used to perform prediction for the target user. The experimental results show that compared with other representative systems the proposed system has better recommendations quality.
Key words: Collaborative Filtering, User-based, Data Sparsity, Subjective Function, Subjective Similarity Function.
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