Recommender Systems (RSs) are becoming increasingly popular in the last years. They collect reviews concerning several types of items (e.g., shops, professionals, services, songs or videos) in order to rank them according to a given criterion, and to suggest the most relevant ones to their users. However, most of the currently used RSs exhibit two main drawbacks: they are based on a centralized control model and they do not provide reward mechanisms to encourage the participation of users. To deal with these challenges, the architectures of current RSs could be enhanced through blockchain technology, thus providing novel solutions to decentralize them. As a matter of fact, the blockchain technology could be successfully adopted in this context because smart contracts would allow the decentralization of system control, while cryptocurrency and tokens could be used to implement the reward mechanism.

In the light of the above considerations, this manuscript presents a decentralized rating framework aimed to support the users of RSs based on blockchain technology, providing a token-based reward mechanism that remunerates users submitting their reviews to incentivize their participation. Moreover, the proposed system provides a flexible strategy to rank items, allowing users to choose among different functions to combine reviews to obtain item ranking. The performance and the cost of using the proposed system have been evaluated on the Ropsten Ethereum test network. For instance, our experiments have shown that the median time required to store a batch of 35 ratings is about 47 s, while the average time required to obtain the score of an item having 6000 ratings is less than 2.5 s.

推荐系统（RS）在最近几年变得越来越流行。他们收集有关几种类型项目的评论（例如商店，专业人士，服务，歌曲或视频），以便根据给定的标准对它们进行排名，并向其用户建议最相关的项目。但是，当前使用的大多数RS都有两个主要缺点：它们基于集中式控制模型，并且不提供奖励机制来鼓励用户参与。为了应对这些挑战，可以通过区块链技术来增强当前RS的体系结构，从而提供新颖的解决方案来分散它们。事实上，区块链技术可以在这种情况下成功采用，因为智能合约可以分散系统控制权，

鉴于以上考虑，本手稿提出了一个分散的评分框架，旨在支持基于区块链技术的RS用户，提供了一种基于令牌的奖励机制，该机制可以奖励提交评论的用户以激励他们的参与。此外，所提出的系统提供了一种对项目进行排名的灵活策略，允许用户在不同功能中进行选择以组合评论以获得项目排名。在Ropsten以太坊测试网络上已经评估了使用建议系统的性能和成本。例如，我们的实验表明，存储35个等级的批次所需的中值时间约为47 s，而获得具有6000等级的项目得分所需的平均时间小于2.5 s。