面向不一致用户评价准则的在线服务推荐

Online service recommendation for inconsistent user evaluation criteria

客观上,用户的评价准则是由主观意识决定的,用户之间的评价准则不同导致多个用户对同一服务的评分不具备可比较性,不考虑不同用户评分的不可比较性所获得的服务推荐将难以满足用户个性偏好及其真实需求。为此,提出一种面向不一致用户评价准则的在线服务推荐方法,考虑用户偏好不一致时用户对在线服务的偏好关系,以偏好关系计算用户之间的相似度,并以此获得在线服务推荐结果。首先以用户-服务评分矩阵为基础建立用户对服务的偏好关系,其次根据偏好关系计算用户之间的相似度,然后以用户相似度为基础对用户未评分的服务进行评分预测,最后以预测评分的排序结果作为推荐结果。与经典的协同过滤推荐方法的比较实验,验证了本方法的有效性。实验表明,本方法获得的推荐结果能满足大多数用户的服务偏好,同时获得了比经典的协同过滤推荐方法更好的准确率。

Objectively, user evaluation criteria are determined by subjective consciousness. Different evaluation criteria between users result in that the scores of multiple users for the same service are incomparable. Service recommendations that do not consider the incomparability of different user ratings cannot meet user personal preferences and real needs. Therefore, we propose an online service recommendation method for inconsistent user evaluation criteria. The method calculates online service recommendation results for users by considering the user’s preference relationship with the online service when user preferences are inconsistent. Firstly, based on the user-service scoring matrix, the user's preference relationship with the service is established. Secondly, the similarity between users is calculated according to the preference relationship. Thirdly, based on user similarity, the user's unscoring service is scored and predicted. Finally, the ranking results of the predicted scores are used as the recommendation results. In the experiments, we compare the method with the classical collaborative filtering recommendation method to verify its effectiveness. Experimental results show that the recommendation results obtained by the proposed method can meet the service preferences of most users, and at the same time obtain better accuracy than the classic collaborative filtering recommendation method.