The way that users provide feedback on items regarding their satisfaction varies among systems: in some systems, only explicit ratings can be entered; in other systems textual reviews are accepted; and in some systems, both feedback types are accommodated. Recommender systems can readily exploit explicit ratings in the rating prediction and recommendation formulation process, however textual reviews -which in the context of many social networks are in abundance and significantly outnumber numeric ratings- need to be converted to numeric ratings. While numerous approaches exist that calculate a user's rating based on the respective textual review, all such approaches may introduce errors, in the sense that the process of rating calculation based on textual reviews involves an uncertainty level, due to the characteristics of the human language, and therefore the calculated ratings may not accurately reflect the actual ratings that the corresponding user would enter. In this work (1) we examine the features of textual reviews, which affect the reliability of the review-to-rating conversion procedure, (2) we compute a confidence level for each rating, which reflects the uncertainty level for each conversion process, (3) we exploit this metric both in the users’ similarity computation and in the prediction formulation phases in recommender systems, by presenting a novel rating prediction algorithm and (4) we validate the accuracy of the presented algorithm in terms of (i) rating prediction accuracy, using widely-used recommender systems datasets and (ii) recommendations generated for social network user satisfaction and precision, where textual reviews are abundant.

用户提供有关其满意度的项目反馈的方式因系统而异：在某些系统中，只能输入明确的评分；在其他系统中，接受文字评论；在某些系统中，两种反馈类型都适用。推荐系统可以在等级预测和推荐制定过程中轻松利用显式等级，但是文本评论（在许多社交网络中数量很多并且大大超过数字等级）需要转换为数字等级。尽管存在许多基于各自的文本评论来计算用户评分的方法，但是从某种意义上来说，由于人类语言的特性，所有基于文本评论的评分计算过程都存在不确定性的意义，因此计算出的评分可能无法准确反映相应用户输入的实际评分。在这项工作中（1）我们检查了文本评论的功能，这些特征会影响评论到评分转换程序的可靠性；（2）我们为每个评分计算一个置信度，这反映了每个转换过程的不确定性， （3）通过提出一种新颖的评分预测算法，我们在推荐者系统的用户相似度计算和预测制定阶段中都采用了该指标，并且（4）我们根据（i）评分来验证所提出算法的准确性使用广泛使用的推荐系统数据集和（ii）为社交网络用户满意度和准确性生成的推荐（文本评论丰富的地方）的预测准确性。