The recommendation problem involves the prediction of a set of items that maximize the utility for users. Numerous factors, such as the filtering method and similarity measure, affect the prediction accuracy. We propose a novel prediction mechanism that can be applied to collaborative filtering recommender systems. This prediction mechanism consists of a novel adaptable predictive model, called inheritance-based prediction (INH-BP), and a suitable heuristic search algorithm. INH-BP enables the customization of the predictor to suit the user context. It helps in defining a user interest print (UIP) matrix and employs an optimization algorithm such as a genetic algorithm. The UIP matrix should reflect the degree of user satisfaction based on the concept levels instead of the instance level. The optimization algorithm is used to determine the optimal predictor for each user. A set of experiments were conducted to compare INH-BP with Resnick’s well-known adjusted weighted sum. Two benchmark datasets, MovieLens-100K and MovieLens-Last, were used. Both prediction methods were employed using different collaborative filtering techniques. The results demonstrate the superiority of INH-BP and its capability to achieve an accurate prediction irrespective of the number of k-neighbors and their quality. In addition, the results show that INH-BP alleviates the cold start and sparsity issues.

推荐问题涉及预测一组项目，以使用户的效用最大化。诸如滤波方法和相似性度量之类的许多因素影响预测准确性。我们提出了一种新颖的预测机制，可以应用于协同过滤推荐系统。这种预测机制由称为适应性预测（INH-BP）的新型适应性预测模型和合适的启发式搜索算法组成。INH-BP支持自定义预测变量以适合用户上下文。它有助于定义用户兴趣打印（UIP）矩阵，并采用优化算法，例如遗传算法。UIP矩阵应基于概念级别而不是实例级别反映用户满意度。优化算法用于确定每个用户的最佳预测变量。进行了一组实验，以将INH-BP与Resnick众所周知的调整后加权总和进行比较。使用了两个基准数据集MovieLens-100K和MovieLens-Last。两种预测方法均使用了不同的协作过滤技术。结果证明了INH-BP的优越性及其实现精确预测的能力，而与k邻居的数量及其质量无关。此外，结果表明，INH-BP缓解了冷启动和稀疏性问题。两种预测方法均使用了不同的协作过滤技术。结果证明了INH-BP的优越性及其实现精确预测的能力，而与k邻居的数量及其质量无关。此外，结果表明，INH-BP缓解了冷启动和稀疏性问题。两种预测方法均使用了不同的协作过滤技术。结果证明了INH-BP的优越性及其实现精确预测的能力，而与k邻居的数量及其质量无关。此外，结果表明，INH-BP缓解了冷启动和稀疏性问题。