基于万有引力的个性化推荐算法

本文把物理学中的万有引力定律引入推荐系统，提出一种个性化推荐算法，即基于万有引力的个性化推荐算法。算法把用户使用的标签看作用户喜欢物体的组成颗粒，标注项目的标签被看作项目物体的组成颗粒，社会标签的类型就是颗粒的类型，由此构建了用户喜好物体模型和项目物体模型。喜好物体和项目物体间存在着万有引力，并且引力大小遵循万有引力定律。计算喜好物体和项目物体间的万有引力，并把该引力大小作为二者的相似度度量，引力越大，二者的相似度就越高，对应的项目物体就越有可能被用户喜欢。实验结果证明本文提出的算法可以获得好的推荐性能。

Gravitation-based personalized recommendation algorithm

A recommendation algorithm is proposed by introducing the universal law of gravitation into a recommendation system. This new algorithm is named as the gravitation-based personalized recommendation ( GBPR) algorithm. In the algorithm, social tags used by users are regarded as particles that made up of their preference objects, social tags marking on items are considered as parti-cles that made up of item objects, and the user preference objects and item objects are taken as a user preference object model and an item object model, respectively. Gravitation exists between the user preference objects and item objects, and its strength obeys the universal law of gravitation. The strength of gravitation between the user preference objects and the item objects is computed, and it is regarded as their similarity. The bigger the strength is, the more similar they are, and the corresponding item objects are more proba-ble to be liked by users. Experimental results show that the proposed algorithm can get good performance.