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Recommender systems have been acknowledged as efficacious tools for managinginformation overload. Nevertheless, conventional algorithms adopted in suchsystems primarily emphasize precise recommendations and, consequently, overlookother vital aspects like the coverage, diversity, and novelty of items. Thisapproach results in less exposure for long-tail items. In this paper, topersonalize the recommendations and allocate recommendation resources morepurposively, a method named PIM+RA is proposed. This method utilizes abipartite network that incorporates self-connecting edges and weights.Furthermore, an improved Pearson correlation coefficient is employed for betterredistribution. The evaluation of PIM+RA demonstrates a significant enhancementnot only in accuracy but also in coverage, diversity, and novelty of therecommendation. It leads to a better balance in recommendation frequency byproviding effective exposure to long-tail items, while allowing customizedparameters to adjust the recommendation list bias.

A Network Resource Allocation Recommendation Method with An Improved Similarity Measure