There are a variety of Internet of Things(IoT)applications that cover different aspects of daily life.Each of these applications has different criteria and sub-criteria,making it difficult for the user to choose.This ...There are a variety of Internet of Things(IoT)applications that cover different aspects of daily life.Each of these applications has different criteria and sub-criteria,making it difficult for the user to choose.This requires an automated approach to select IoT applications by considering criteria.This paper presents a novel recommendation system for presenting applications on the IoT.First,using the analytic hierarchy process(AHP),a multi-layer architecture of the criteria and sub-criteria in IoT applications is presented.This architecture is used to evaluate and rank IoT applications.As a result,finding the weight of the criteria and subcriteria requires a metaheuristic approach.In this paper,a sequential quadratic programming algorithm is used to find the optimal weight of the criteria and sub-criteria automatically.To the best of our knowledge,this is the first study to use an analysis of metaheuristic criteria and sub-criteria to design an IoT application recommendation system.The evaluations and comparisons in the experimental results section show that the proposed method is a comprehensive and reliable model for the construction of an IoT applications recommendation system.展开更多
Recommendation algorithms regard user-item interaction as a sequence to capture the user’s short-term preferences,but conventional algorithms cannot capture information of constantly-changing user interest in complex...Recommendation algorithms regard user-item interaction as a sequence to capture the user’s short-term preferences,but conventional algorithms cannot capture information of constantly-changing user interest in complex contexts.In these years,combining the knowledge graphwith sequential recommendation has gained momentum.The advantages of knowledge graph-based recommendation systems are that more semantic associations can improve the accuracy of recommendations,rich association facts can increase the diversity of recommendations,and complex relational paths can hence the interpretability of recommendations.But the information in the knowledge graph,such as entities and relations,often fails to be fully utilized and high-order connectivity is unattainable in graph modelling in knowledge graph-based sequential recommender systems.To address the above problems,a knowledge graph-based sequential recommendation algorithm that combines the gated recurrent unit and the graph neural network(KGSRGG)is proposed in the present work.Specifically,entity disambiguation in the knowledge graph is performed on the preprocessing layer;on the embedding layer,the TransR embedding technique is employed to process the user information,item information and the entities and relations in the knowledge graph;on the aggregation layer,the information is aggregated by graph convolutional neural networks and residual connections;and at last,on the sequence layer,a bi-directional gated recurrent unit(Bi-GRU)is utilized to model the user’s sequential preferences.The research results showed that this newalgorithm performed better than existing sequential recommendation algorithms on the MovieLens-1M and Book-Crossing datasets,as measured by five evaluation indicators.展开更多
Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items.Recent work has shown the effectiveness of incorporating textual or visual informati...Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items.Recent work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse problem.The data sparse problem now is attracting a lot of attention in both industry and academic community.However,considering interactions among modalities on a sequential scenario is an interesting yet challenging task because of multimodal heterogeneity.In this paper,we introduce a novel recommendation approach of considering both textual and visual information,namely Multimodal Interactive Network(MIN).The advantage of MIN lies in designing a learning framework to leverage the interactions among modalities from both the item level and the sequence level for building an efficient system.Firstly,an item-wise interactive layer based on the encoder-decoder mechanism is utilized to model the item-level interactions among modalities to select the informative information.Secondly,a sequence interactive layer based on the attention strategy is designed to capture the sequence-level preference of each modality.MIN seamlessly incorporates interactions among modalities from both the item level and the sequence level for sequential recommendation.It is the first time that interactions in each modality have been explicitly discussed and utilized in sequential recommenders.Experimental results on four real-world datasets show that our approach can significantly outperform all the baselines in sequential recommendation task.展开更多
The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on dif...The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation.To this end,we consider applying the popular predictability theory of human movement behavior to this recommendation context.Still,it would incur serious bias in the next moment measurement of the candidate set size,resulting in inaccurate predictability.Therefore,determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations.Here,different from the traditional approach that utilizes topological constraints,we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints.Then,we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior.Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors.Finally,a prediction rate between 64%and 80%has been obtained by testing on five classical datasets in three domains of the recommender system.This provides a guideline to optimize the recommendation algorithm for a given dataset.展开更多
针对短视频序列推荐中存在的点击数据稀疏性、观看时长反馈中的噪声以及偏差问题,提出了一种基于时长感知的短视频序列推荐模型(duration-aware for short video sequential recommendation,DASR)。该模型通过对用户观看时长反馈的深入...针对短视频序列推荐中存在的点击数据稀疏性、观看时长反馈中的噪声以及偏差问题,提出了一种基于时长感知的短视频序列推荐模型(duration-aware for short video sequential recommendation,DASR)。该模型通过对用户观看时长反馈的深入建模,有效地缓解了数据稀疏性问题。提出了一种无偏差的多语义观看时长反馈标签生成方法。该方法结合了K近邻算法和训练数据的百分位数分析,动态生成适应不同视频时长的标签阈值,有效地消除了视频时长偏差的影响。提出了一种基于强弱注意力网络的噪声提取方法,从观看时长中准确地提取正向和负向兴趣信号,从而解决了观看时长反馈中存在的噪声。在开源的短视频数据集上进行了广泛实验,结果表明该模型在多个评价指标上优于其他主流方法。展开更多
文摘There are a variety of Internet of Things(IoT)applications that cover different aspects of daily life.Each of these applications has different criteria and sub-criteria,making it difficult for the user to choose.This requires an automated approach to select IoT applications by considering criteria.This paper presents a novel recommendation system for presenting applications on the IoT.First,using the analytic hierarchy process(AHP),a multi-layer architecture of the criteria and sub-criteria in IoT applications is presented.This architecture is used to evaluate and rank IoT applications.As a result,finding the weight of the criteria and subcriteria requires a metaheuristic approach.In this paper,a sequential quadratic programming algorithm is used to find the optimal weight of the criteria and sub-criteria automatically.To the best of our knowledge,this is the first study to use an analysis of metaheuristic criteria and sub-criteria to design an IoT application recommendation system.The evaluations and comparisons in the experimental results section show that the proposed method is a comprehensive and reliable model for the construction of an IoT applications recommendation system.
文摘Recommendation algorithms regard user-item interaction as a sequence to capture the user’s short-term preferences,but conventional algorithms cannot capture information of constantly-changing user interest in complex contexts.In these years,combining the knowledge graphwith sequential recommendation has gained momentum.The advantages of knowledge graph-based recommendation systems are that more semantic associations can improve the accuracy of recommendations,rich association facts can increase the diversity of recommendations,and complex relational paths can hence the interpretability of recommendations.But the information in the knowledge graph,such as entities and relations,often fails to be fully utilized and high-order connectivity is unattainable in graph modelling in knowledge graph-based sequential recommender systems.To address the above problems,a knowledge graph-based sequential recommendation algorithm that combines the gated recurrent unit and the graph neural network(KGSRGG)is proposed in the present work.Specifically,entity disambiguation in the knowledge graph is performed on the preprocessing layer;on the embedding layer,the TransR embedding technique is employed to process the user information,item information and the entities and relations in the knowledge graph;on the aggregation layer,the information is aggregated by graph convolutional neural networks and residual connections;and at last,on the sequence layer,a bi-directional gated recurrent unit(Bi-GRU)is utilized to model the user’s sequential preferences.The research results showed that this newalgorithm performed better than existing sequential recommendation algorithms on the MovieLens-1M and Book-Crossing datasets,as measured by five evaluation indicators.
基金supported by the National Natural Science Foundation of China under Grant Nos.61802029,U1536121,and 61370195.
文摘Building an effective sequential recommendation system is still a challenging task due to limited interactions among users and items.Recent work has shown the effectiveness of incorporating textual or visual information into sequential recommendation to alleviate the data sparse problem.The data sparse problem now is attracting a lot of attention in both industry and academic community.However,considering interactions among modalities on a sequential scenario is an interesting yet challenging task because of multimodal heterogeneity.In this paper,we introduce a novel recommendation approach of considering both textual and visual information,namely Multimodal Interactive Network(MIN).The advantage of MIN lies in designing a learning framework to leverage the interactions among modalities from both the item level and the sequence level for building an efficient system.Firstly,an item-wise interactive layer based on the encoder-decoder mechanism is utilized to model the item-level interactions among modalities to select the informative information.Secondly,a sequence interactive layer based on the attention strategy is designed to capture the sequence-level preference of each modality.MIN seamlessly incorporates interactions among modalities from both the item level and the sequence level for sequential recommendation.It is the first time that interactions in each modality have been explicitly discussed and utilized in sequential recommenders.Experimental results on four real-world datasets show that our approach can significantly outperform all the baselines in sequential recommendation task.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.61960206008,62002294)the National Science Fund for Distinguished Young Scholars(61725205).
文摘The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation.To this end,we consider applying the popular predictability theory of human movement behavior to this recommendation context.Still,it would incur serious bias in the next moment measurement of the candidate set size,resulting in inaccurate predictability.Therefore,determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations.Here,different from the traditional approach that utilizes topological constraints,we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints.Then,we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior.Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors.Finally,a prediction rate between 64%and 80%has been obtained by testing on five classical datasets in three domains of the recommender system.This provides a guideline to optimize the recommendation algorithm for a given dataset.
文摘针对短视频序列推荐中存在的点击数据稀疏性、观看时长反馈中的噪声以及偏差问题,提出了一种基于时长感知的短视频序列推荐模型(duration-aware for short video sequential recommendation,DASR)。该模型通过对用户观看时长反馈的深入建模,有效地缓解了数据稀疏性问题。提出了一种无偏差的多语义观看时长反馈标签生成方法。该方法结合了K近邻算法和训练数据的百分位数分析,动态生成适应不同视频时长的标签阈值,有效地消除了视频时长偏差的影响。提出了一种基于强弱注意力网络的噪声提取方法,从观看时长中准确地提取正向和负向兴趣信号,从而解决了观看时长反馈中存在的噪声。在开源的短视频数据集上进行了广泛实验,结果表明该模型在多个评价指标上优于其他主流方法。
