查询推荐是一种帮助搜索引擎更好的理解用户检索需求的方法.基于查询的上下文片段训练词汇和查询之间的语义关系,同时结合查询和URL的点击图以及查询中的序列行为构建Term Query URL异构信息网络,采用重启动随机游走(Random Walk withR...查询推荐是一种帮助搜索引擎更好的理解用户检索需求的方法.基于查询的上下文片段训练词汇和查询之间的语义关系,同时结合查询和URL的点击图以及查询中的序列行为构建Term Query URL异构信息网络,采用重启动随机游走(Random Walk withRestart,RWR)进行查询推荐.综合利用语义信息和日志信息,提高了稀疏查询的推荐效果.基于概率语言模型构造查询的词汇向量,可以为新的查询进行查询推荐.在大规模商业搜索引擎查询日志上的实验表明本文方法相比传统的查询推荐方法性能提升约为3%~10%.展开更多
查询推荐的目的是发掘搜索引擎用户的查询意图,并给出相关查询推荐。传统的查询推荐方法主要依靠人工提取查询的相关特征,如查询频率、查询时间、用户点击次数和停留时间等,并使用统计学习算法或排序算法给出查询推荐。近年来,深度学习...查询推荐的目的是发掘搜索引擎用户的查询意图,并给出相关查询推荐。传统的查询推荐方法主要依靠人工提取查询的相关特征,如查询频率、查询时间、用户点击次数和停留时间等,并使用统计学习算法或排序算法给出查询推荐。近年来,深度学习方法在查询推荐问题上获得了广泛应用。现有的用于查询推荐的深度学习方法大多是基于循环神经网络,通过对查询日志中所有查询的语义特征进行建模以预测用户的下一查询。但是,现有的深度学习方法生成的查询推荐上下文感知能力较差,难以准确捕捉用户查询意图,且未充分考虑时间因素对查询推荐的影响,缺乏时效性和多样性。针对上述问题,文中提出了一种结合自编码器与强化学习的查询推荐模型(Latent Variable Hierarchical Recurrent Encoder-Decoder with Time Information of Query and Reinforcement Learning,VHREDT-RL)。VHREDT-RL引入了强化学习联合训练生成器和判别器,从而增强了生成查询推荐的上下文感知能力;利用融合查询时间信息的隐变量分层递归自编码器作为生成器,使得生成查询推荐有更好的时效性和多样性。AOL数据集上的实验结果表明,文中提出的VHREDT-RL模型获得了优于基准方法的精度、鲁棒性和稳定性。展开更多
Query suggestions help users refine their queries after they input an initial query.Previous work on query suggestion has mainly concentrated on approaches that are similarity-based or context-based,developing models ...Query suggestions help users refine their queries after they input an initial query.Previous work on query suggestion has mainly concentrated on approaches that are similarity-based or context-based,developing models that either focus on adapting to a specific user(personalization)or on diversifying query aspects in order to maximize the probability of the user being satisfied(diversification).We consider the task of generating query suggestions that are both personalized and diversified.We propose a personalized query suggestion diversification(PQSD)model,where a user's long-term search behavior is injected into a basic greedy query suggestion diversification model that considers a user's search context in their current session.Query aspects are identified through clicked documents based on the open directory project(ODP)with a latent dirichlet allocation(LDA)topic model.We quantify the improvement of our proposed PQSD model against a state-of-the-art baseline using the public america online(AOL)query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversification.The experimental results show that PQSD achieves its best performance when only queries with clicked documents are taken as search context rather than all queries,especially when more query suggestions are returned in the list.展开更多
文摘查询推荐是一种帮助搜索引擎更好的理解用户检索需求的方法.基于查询的上下文片段训练词汇和查询之间的语义关系,同时结合查询和URL的点击图以及查询中的序列行为构建Term Query URL异构信息网络,采用重启动随机游走(Random Walk withRestart,RWR)进行查询推荐.综合利用语义信息和日志信息,提高了稀疏查询的推荐效果.基于概率语言模型构造查询的词汇向量,可以为新的查询进行查询推荐.在大规模商业搜索引擎查询日志上的实验表明本文方法相比传统的查询推荐方法性能提升约为3%~10%.
文摘查询推荐的目的是发掘搜索引擎用户的查询意图,并给出相关查询推荐。传统的查询推荐方法主要依靠人工提取查询的相关特征,如查询频率、查询时间、用户点击次数和停留时间等,并使用统计学习算法或排序算法给出查询推荐。近年来,深度学习方法在查询推荐问题上获得了广泛应用。现有的用于查询推荐的深度学习方法大多是基于循环神经网络,通过对查询日志中所有查询的语义特征进行建模以预测用户的下一查询。但是,现有的深度学习方法生成的查询推荐上下文感知能力较差,难以准确捕捉用户查询意图,且未充分考虑时间因素对查询推荐的影响,缺乏时效性和多样性。针对上述问题,文中提出了一种结合自编码器与强化学习的查询推荐模型(Latent Variable Hierarchical Recurrent Encoder-Decoder with Time Information of Query and Reinforcement Learning,VHREDT-RL)。VHREDT-RL引入了强化学习联合训练生成器和判别器,从而增强了生成查询推荐的上下文感知能力;利用融合查询时间信息的隐变量分层递归自编码器作为生成器,使得生成查询推荐有更好的时效性和多样性。AOL数据集上的实验结果表明,文中提出的VHREDT-RL模型获得了优于基准方法的精度、鲁棒性和稳定性。
基金supported by the National Natural Science Foundation of China(Grant No.61702526)the National Advanced Research Project(6141B0801010b)。
文摘Query suggestions help users refine their queries after they input an initial query.Previous work on query suggestion has mainly concentrated on approaches that are similarity-based or context-based,developing models that either focus on adapting to a specific user(personalization)or on diversifying query aspects in order to maximize the probability of the user being satisfied(diversification).We consider the task of generating query suggestions that are both personalized and diversified.We propose a personalized query suggestion diversification(PQSD)model,where a user's long-term search behavior is injected into a basic greedy query suggestion diversification model that considers a user's search context in their current session.Query aspects are identified through clicked documents based on the open directory project(ODP)with a latent dirichlet allocation(LDA)topic model.We quantify the improvement of our proposed PQSD model against a state-of-the-art baseline using the public america online(AOL)query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversification.The experimental results show that PQSD achieves its best performance when only queries with clicked documents are taken as search context rather than all queries,especially when more query suggestions are returned in the list.