This paper presents a novel genetic-based recommender system (BLI_GA) that depends on the semantic information and historical rating data. The main contribution of this research lies in evaluating the possible recommendation lists instead of evaluating items then forming the recommendation list. BLI_GA utilizes the genetic algorithm to find the best list of items to the active user. Thus, each individual represents a candidate recommendation list. BLI_GA hierarchically evaluates the individuals using three fitness functions. The first function uses semantic information about items to estimates the strength of the semantic similarity between items. The second function estimates the similarity in satisfaction level between users. The third function depends on the predicted ratings to select the best recommendation list.

BLI_GA results have been compared against recommendation results from alternative collaborative filtering methods. The results demonstrate the superiority of BLI_GA and its capability to achieve more accurate predictions than the alternative methods regardless of the number of K-neighbors.

本文提出了一种新颖的基于遗传的推荐系统（BLI _GA），该系统依赖于语义信息和历史评分数据。这项研究的主要贡献在于评估可能的推荐列表，而不是评估项目然后形成推荐列表。BLI _GA利用遗传算法为活跃用户找到最佳商品清单。因此，每个人代表一个候选推荐列表。BLI _GA使用三个适合度函数对个人进行分层评估。第一个功能使用有关项目的语义信息来估计项目之间的语义相似度。第二个函数估计用户之间满意度的相似度。第三个功能取决于预测的等级来选择最佳推荐列表。

已将BLI _GA结果与其他协作过滤方法的推荐结果进行了比较。结果证明了BLI _GA的优越性及其实现了比替代方法更准确的预测的能力，而与K邻居的数量无关。