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Personalized recommender system based on friendship strength in social network services

机译：基于社交网络服务中友谊强度的个性化推荐系统

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摘要

The rapid growth of social network services has produced a considerable amount of data, called big social data. Big social data are helpful for improving personalized recommender systems because these enormous data have various characteristics. Therefore, many personalized recommender systems based on big social data have been proposed, in particular models that use people relationship information. However, most existing studies have provided recommendations on special purpose and single-domain SNS that have a set of users with similar tastes, such as MovieLens and Last.fm; nonetheless, they have considered closeness relation. In this paper, we introduce an appropriate measure to calculate the closeness between users in a social circle, namely, the friendship strength. Further, we propose a friendship strength-based personalized recommender system that recommends topics or interests users might have in order to analyze big social data, using Twitter in particular. The proposed measure provides precise recommendations in multi-domain environments that have various topics. We evaluated the proposed system using one month's Twitter data based on various evaluation metrics. Our experimental results show that our personalized recommender system outperforms the baseline systems, and friendship strength is of great importance in personalized recommendation. (C) 2016 Elsevier Ltd. All rights reserved.
机译：社交网络服务的快速增长产生了大量的数据，称为大社交数据。大社交数据有助于改善个性化推荐系统，因为这些巨大的数据具有各种特征。因此，已经提出了许多基于大社交数据的个性化推荐系统，特别是使用人际关系信息的模型。但是，大多数现有研究已经针对特殊目的和单域SNS提供了建议，这些SNS具有一组具有相似品味的用户，例如MovieLens和Last.fm。尽管如此，他们还是考虑了亲密关系。在本文中，我们介绍了一种适当的方法来计算社交圈中用户之间的亲密程度，即友谊强度。此外，我们提出了一种基于友谊强度的个性化推荐系统，该系统推荐用户可能具有的主题或兴趣，以便特别是使用Twitter分析大型社交数据。拟议的措施在具有多个主题的多域环境中提供了精确的建议。我们根据各种评估指标，使用一个月的Twitter数据评估了提议的系统。我们的实验结果表明，我们的个性化推荐系统优于基线系统，而友谊强度在个性化推荐中至关重要。 （C）2016 Elsevier Ltd.保留所有权利。

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