我该留下还是该走？改进社交网络中的事件推荐
Should I Stay or Should I Go? Improving Event Recommendation in the Social Web
摘要：
本文重点介绍了在社交网络中的事件的推荐，并解决的问题，如果发现，以及在何种程度上，某些功能，这是特有的事件，是相关的预测用户的兴趣，从而应考虑到在推荐。我们特别考虑三个“附加”的功能，通常显示给用户在社交网络环境中：从用户位置的可达性，在社区中的事件的声誉，和用户的朋友的参与。我们的研究旨在评估是否将这些信息添加到事件类型和主题的描述中，并在用户简档中包含这些因素的相关性信息，可以提高我们预测用户兴趣的能力。我们通过对用户进行两次调查来解决这个问题，他们被要求用一个分数来表达他们对一些事件的兴趣。然后，我们通过线性回归训练了一个定义为不同因素的线性组合的评分函数，其目标是预测用户得分。我们在对附加因素的不同假设下重复了这个实验，以便通过比较所得函数的预测能力来评估它们的相关性。实验结果表明，适当加权后的附加因子可以提高预测精度，误差减少4.1%。通过以约10：4的比例组合基于内容的因子和附加因子获得最佳结果。
英文摘要：
This paper focuses on the recommendation of events in the Social Web, and addresses the problem of finding if, and to which extent, certain features, which are peculiar to events, are relevant in predicting the users' interests and should thereby be taken into account in recommendation. We consider in particular three "additional" features that are usually shown to users within social networking environments: reachability from the user location, the reputation of the event in the community, and the participation of the user's friends. Our study is aimed at evaluating whether adding this information to the description of the event type and topic, and including in the user profile the information on the relevance of these factors, can improve our capability to predict the user's interest. We approached the problem by carrying out two surveys with users, who were asked to express %with a score their interest in a number of events. We then trained, by means of linear regression, a scoring function defined as a linear combination of the different factors, whose goal was to predict the user scores. We repeated this experiment under different hypotheses on the additional factors, in order to assess their relevance by comparing the predictive capabilities of the resulting functions. The compared results of our experiments show that additional factors, if properly weighted, can improve the prediction accuracy with an error reduction of 4.1%. The best results were obtained by combining content-based factors and additional factors in a proportion of approximately 10:4.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
