论文标题
使用懒惰的随机步行寻找当地专家进行动态建议
Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk
论文作者
论文摘要
基于统计的隐私意识推荐系统通过从社交联系人的交互日志中提取知识来提高建议，但不幸的是，它们是静态的。此外，当地专家的建议有效地在特定领域找到特定的业务类别。我们根据懒惰的随机步道提出了一种动态的推荐算法，该算法向潜在感兴趣的访客推荐了顶级购物场所。我们考虑地方当局和主题权威。在印度尼西亚5个城市的Foursquare购物数据集中测试的算法，其K-Steps为5,7,9（懒惰）随机步行，并将结果与其他最先进的排名技术进行了比较。结果表明，它可以达到高分精度（分别为0.5、0.37和0.26，精度为1，精度为3，对于k = 5时为5）。该算法还显示有关执行时间的可扩展性。动态性的优点是用于为推荐系统供电的数据库。无需经常更新即可提出一个很好的建议。
Statistics based privacy-aware recommender systems make suggestions more powerful by extracting knowledge from the log of social contacts interactions, but unfortunately, they are static. Moreover, advice from local experts effective in finding specific business categories in a particular area. We propose a dynamic recommender algorithm based on a lazy random walk that recommends top-rank shopping places to potentially interested visitors. We consider local authority and topical authority. The algorithm tested on FourSquare shopping data sets of 5 cities in Indonesia with k-steps of 5,7,9 of (lazy) random walks and compared the results with other state-of-the-art ranking techniques. The results show that it can reach high score precisions (0.5, 0.37, and 0.26 respectively on precision at 1, precision at 3, and precision at 5 for k=5). The algorithm also shows scalability concerning execution time. The advantage of dynamicity is the database used to power the recommender system; no need to be very frequently updated to produce a good recommendation.
