Despite years of evolution of recommender systems, improving prediction accuracy remains one of the core problems among researchers and industry. It is common to use side information to bolster the accuracy of recommender systems. In this work, we focus on using item categories, specifically movie genres, to improve the prediction accuracy as well as coverage, precision, and recall. We derive the user’s taste for an item using the ratings expressed. Similarly, using the collective ratings given to an item, we identify how much each item belongs to a certain genre. These two vectors are then combined to get a user-item-weight matrix. In contrast to the similarity-based weight matrix in memory-based collaborative filtering, we use user-item-weight to make predictions. The user-item-weights can be used to explain to users why certain items have been recommended. We evaluate our proposed method using three real-world datasets. The proposed model performs significantly better than the baseline methods. In addition, we use the user-item-weight matrix to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.

中文翻译：

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使用分类特征提高基于内存的协同过滤中的预测质量

尽管推荐系统已经发展了多年，但提高预测准确性仍然是研究人员和行业中的核心问题之一。通常使用辅助信息来增强推荐系统的准确性。在这项工作中，我们专注于使用项目类别（尤其是电影流派）来提高预测准确性以及覆盖率，准确性和召回率。我们使用表述的评分得出用户对某项商品的喜好。同样，使用对某项商品的集体评分，我们可以确定每件商品属于某个流派的数量。然后将这两个向量组合起来以获得用户项权重矩阵。与基于内存的协作过滤中基于相似度的权重矩阵相比，我们使用用户项权重进行预测。用户项目权重可用于向用户解释为什么建议使用某些项目。我们使用三个现实世界的数据集评估了我们提出的方法。所提出的模型的性能明显优于基线方法。另外，我们使用用户项权重矩阵来缓解与基于相关的相似性相关的稀疏性问题。除此之外，所提出的模型比k近邻法（kNN）具有更好的计算复杂度。