近年来,社会化推荐作为推荐算法之一被广泛应用于各大平台.由于引入了用户的社交信息,社会化推荐可以较好地缓解数据稀疏问题.然而,大部分社会化推荐难以高效地从原始信息中提取用户的有效信息,导致引入社会信息的同时也会引入大量噪声...近年来,社会化推荐作为推荐算法之一被广泛应用于各大平台.由于引入了用户的社交信息,社会化推荐可以较好地缓解数据稀疏问题.然而,大部分社会化推荐难以高效地从原始信息中提取用户的有效信息,导致引入社会信息的同时也会引入大量噪声.为了解决上述问题,本文提出了SRBHL(Social Recommendation Based on Hypergraph embedding and Limited attention)模型,通过超图嵌入模块提取用户的历史行为信息和社交信息,以缓解原始目标用户数据稀疏问题,并结合有限注意力模块来过滤原始信息的噪声,最后将得到的有效好友信息用于推荐.在Yelp-Urbana、Yelp-Phoenix和Epinions3个真实数据集上的实验结果表明SRBHL模型相比其他的推荐算法表现更出色.此外,本文还对SRBHL模型进行了鲁棒性分析,并给出了模型最优参数的取值范围.展开更多
Several decades ago,Profs.Sean Meyn and Lei Guo were postdoctoral fellows at ANU,where they shared interest in recursive algorithms.It seems fitting to celebrate Lei Guo’s 60 th birthday with a review of the ODE Meth...Several decades ago,Profs.Sean Meyn and Lei Guo were postdoctoral fellows at ANU,where they shared interest in recursive algorithms.It seems fitting to celebrate Lei Guo’s 60 th birthday with a review of the ODE Method and its recent evolution,with focus on the following themes:The method has been regarded as a technique for algorithm analysis.It is argued that this viewpoint is backwards:The original stochastic approximation method was surely motivated by an ODE,and tools for analysis came much later(based on establishing robustness of Euler approximations).The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time,following by careful approximation to obtain a practical recursive algorithm.While these methods are usually presented in a stochastic setting,this is not a prerequisite.In fact,recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo.Subject to conditions,the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert.The conditions are not universal,but theory suggests alternatives to achieve acceleration.The theory is illustrated with applications to gradient-free optimization,and policy gradient algorithms for reinforcement learning.展开更多
文摘近年来,社会化推荐作为推荐算法之一被广泛应用于各大平台.由于引入了用户的社交信息,社会化推荐可以较好地缓解数据稀疏问题.然而,大部分社会化推荐难以高效地从原始信息中提取用户的有效信息,导致引入社会信息的同时也会引入大量噪声.为了解决上述问题,本文提出了SRBHL(Social Recommendation Based on Hypergraph embedding and Limited attention)模型,通过超图嵌入模块提取用户的历史行为信息和社交信息,以缓解原始目标用户数据稀疏问题,并结合有限注意力模块来过滤原始信息的噪声,最后将得到的有效好友信息用于推荐.在Yelp-Urbana、Yelp-Phoenix和Epinions3个真实数据集上的实验结果表明SRBHL模型相比其他的推荐算法表现更出色.此外,本文还对SRBHL模型进行了鲁棒性分析,并给出了模型最优参数的取值范围.
基金ARO W911NF1810334NSF under EPCN 1935389the National Renewable Energy Laboratory(NREL)。
文摘Several decades ago,Profs.Sean Meyn and Lei Guo were postdoctoral fellows at ANU,where they shared interest in recursive algorithms.It seems fitting to celebrate Lei Guo’s 60 th birthday with a review of the ODE Method and its recent evolution,with focus on the following themes:The method has been regarded as a technique for algorithm analysis.It is argued that this viewpoint is backwards:The original stochastic approximation method was surely motivated by an ODE,and tools for analysis came much later(based on establishing robustness of Euler approximations).The paper presents a brief survey of recent research in machine learning that shows the power of algorithm design in continuous time,following by careful approximation to obtain a practical recursive algorithm.While these methods are usually presented in a stochastic setting,this is not a prerequisite.In fact,recent theory shows that rates of convergence can be dramatically accelerated by applying techniques inspired by quasi Monte-Carlo.Subject to conditions,the optimal rate of convergence can be obtained by applying the averaging technique of Polyak and Ruppert.The conditions are not universal,but theory suggests alternatives to achieve acceleration.The theory is illustrated with applications to gradient-free optimization,and policy gradient algorithms for reinforcement learning.
