Preferring accuracy over computation time or vice versa is very challenging in the context of recommendation systems, which encourages many researchers to opt for hybrid recommendation systems. Currently, researchers are trying hard to produce correct and accurate recommendations by suggesting the use of ontology, but the lack of techniques renders to take its full advantage. One of the major issues in recommender systems bothering many researchers is pure new user cold-start problem which arises due to the absence of information in the system about the new user. Linked Open Data (LOD) initiative sets standards for interoperability among cross domains and has gathered enormous amount of data over the past years, which provides various ways by which recommender system’s performance can be improved by enriching user’s profile with relevant features. This research work focuses on solving pure new user cold-start problem by building user’s profile based on LOD, collaborative features, and social network-based features. Here, a new approach is devised to compute item similarity based on ontology, thus predicting the rating of nonrated item. A modified method to calculate user’s similarity based on collaborative features to deal with other issues such as accuracy and computation time is also proposed. The empirical results and comparative analysis of the proposed hybrid recommendation system dictate its better performance specifically for providing solution to pure new user cold-start problem.

中文翻译：

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基于链接开放数据和社交网络特征的推荐系统中纯粹的新用户冷启动问题的处理

在推荐系统的背景下，要在计算时间上优先考虑精度是非常有挑战性的，反之亦然，这鼓励了许多研究人员选择混合推荐系统。当前，研究人员正在努力通过建议使用本体论来提出正确而准确的建议，但是缺乏技术使得它无法充分利用它。推荐系统中困扰许多研究人员的主要问题之一是纯粹的新用户冷启动问题，这是由于系统中缺少有关新用户的信息而引起的。链接开放数据（LOD）计划为跨域之间的互操作性设定了标准，并且在过去几年中收集了大量数据，这提供了各种方法，可以通过丰富用户的个人资料和相关功能来提高推荐系统的性能。这项研究工作着重于通过基于LOD，协作功能和基于社交网络的功能来构建用户资料来解决纯粹的新用户冷启动问题。这里，设计了一种新的方法来基于本体计算项目相似度，从而预测未评级项目的等级。还提出了一种基于协作特征来计算用户相似度的改进方法，以解决诸如准确性和计算时间之类的其他问题。所提出的混合推荐系统的实证结果和比较分析表明，其更好的性能专门用于为纯新用户冷启动问题提供解决方案。设计了一种新的方法来基于本体计算项目相似度，从而预测未评级项目的等级。还提出了一种基于协作特征来计算用户相似度的改进方法，以解决诸如准确性和计算时间之类的其他问题。所提出的混合推荐系统的实证结果和比较分析表明，其更好的性能专门用于为纯新用户冷启动问题提供解决方案。设计了一种新的方法来基于本体计算项目相似度，从而预测未评级项目的等级。还提出了一种基于协作特征来计算用户相似度的改进方法，以解决诸如准确性和计算时间之类的其他问题。所提出的混合推荐系统的实证结果和比较分析表明，其更好的性能专门用于为纯新用户冷启动问题提供解决方案。