Recommender systems have been acknowledged as efficacious tools for managing information overload. Nevertheless, conventional algorithms adopted in such systems primarily emphasize precise recommendations and, consequently, overlook other vital aspects like the coverage, diversity, and novelty of items. This approach results in less exposure for long-tail items. In this paper, to personalize the recommendations and allocate recommendation resources more purposively, a method named PIM+RA is proposed. This method utilizes a bipartite network that incorporates self-connecting edges and weights. Furthermore, an improved Pearson correlation coefficient is employed for better redistribution. The evaluation of PIM+RA demonstrates a significant enhancement not only in accuracy but also in coverage, diversity, and novelty of the recommendation. It leads to a better balance in recommendation frequency by providing effective exposure to long-tail items, while allowing customized parameters to adjust the recommendation list bias.

翻译：推荐系统已被公认为管理信息过载的有效工具。然而，此类系统中采用的常规算法主要强调精确推荐，因此忽视了覆盖度、多样性和新颖性等其他关键方面。这种方法导致长尾物品曝光不足。为更定向地个性化推荐并分配推荐资源，本文提出了一种名为PIM+RA的方法。该方法利用包含自连接边和权重的二分网络，并采用改进的皮尔逊相关系数实现更优的重分配。对PIM+RA的评估表明，该方法不仅在准确性上显著提升，而且在推荐的覆盖度、多样性和新颖性方面也有明显改善。通过为长尾物品提供有效曝光，它在推荐频率上实现了更好的平衡，同时允许通过自定义参数调整推荐列表的偏向性。