The problem of recommender system is very popular with myriad available solutions. A novel approach that uses the link prediction problem in social networks has been proposed in the literature that model the typical user-item information as a bipartite network in which link prediction would actually mean recommending an item to a user. The standard recommender system methods suffer from the problems of sparsity and scalability. Since link prediction measures involve computations pertaining to small neighborhoods in the network, this approach would lead to a scalable solution to recommendation. One of the issues in this conversion is that link prediction problem is modelled as a binary classification task whereas the problem of recommender systems is solved as a regression task in which the rating of the link is to be predicted. We overcome this issue by predicting top k links as recommendations with high ratings without predicting the actual rating. Our work extends similar approaches in the literature by focusing on exploiting the probabilistic measures for link prediction. Moreover, in the proposed approach, prediction measures that utilize temporal information available on the links prove to be more effective in improving the accuracy of prediction. This approach is evaluated on the benchmark 'Movielens' dataset. We show that the usage of temporal probabilistic measures helps in improving the quality of recommendations. Temporal random-walk based measure T_Flow improves recommendation accuracy by 4% and Temporal cooccurrence probability measure improves prediction accuracy by 10% over item-based collaborative filtering method in terms of AUROC score.

中文翻译：

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推荐系统的链接预测方法

推荐系统的问题在众多可用解决方案中非常流行。在文献中已经提出了一种在社交网络中使用链接预测问题的新颖方法，该方法将典型的用户项目信息建模为双向网络，其中链接预测实际上意味着向用户推荐商品。标准推荐器系统方法存在稀疏性和可伸缩性的问题。由于链路预测措施涉及与网络中较小邻域有关的计算，因此该方法将导致可扩展的推荐解决方案。此转换中的问题之一是将链接预测问题建模为二进制分类任务，而将推荐系统的问题解决为回归任务，在该回归任务中要预测链接的等级。我们通过预测排名靠前的k个链接作为具有较高评级的推荐来解决此问题，而无需预测实际评级。我们的工作通过集中于利用概率测度进行链接预测来扩展文献中的类似方法。此外，在所提出的方法中，利用链接上可用的时间信息的预测措施被证明在提高预测的准确性方面更为有效。在基准“电影”数据集上评估了这种方法。我们表明，使用时间概率测度有助于提高建议的质量。与基于项目的协作过滤方法相比，基于时间的随机游走量度T_Flow将推荐准确度提高了4％，而时间共现概率量度将预测准确度提高了10％。