针对传统的物化视图选择(materialized view selection,MVS)算法评价指标单一(仅评价物化时间,过度追求物化视图的查询命中率)会导致超高维度时的维度灾难以及物化视图集频繁抖动的问题,本文提出了一种基于带权图的多维大数据模型优化算...针对传统的物化视图选择(materialized view selection,MVS)算法评价指标单一(仅评价物化时间,过度追求物化视图的查询命中率)会导致超高维度时的维度灾难以及物化视图集频繁抖动的问题,本文提出了一种基于带权图的多维大数据模型优化算法(multi-dimensional big data model optimization,MMO),通过引入平均查询时延和膨胀率评价指标,基于带权图模型找出物化视图集的最优解。实验结果表明,本文算法在综合评分、平均查询时延、膨胀率方面均优于粒子群算法(particle swarm optimization,PSO),解决了超高维数据下的维度灾难问题,并且能够快速收敛。展开更多
Quality-of-Service （QoS） describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determine...Quality-of-Service （QoS） describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. A new ＇hybrid user-location-aware prediction based on weighted Adamic-Adar （WAA）＇ （HUWAA） was proposed. The implicit neighbor search was optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem was validated in experiments on public real world datasets. The new algorithm outperforms the existing of item-based pearson correlation coefficient （IPCC）, user-based pearson correlation coefficient （UPCC） and Web service recommender system （WSRec） algorithms.展开更多
With the rapid growth of location-based social networks （LBSNs）, point-of-interest （POI） recommendation has become an important research problem. As one of the most representative social media platforms, Twitter p...With the rapid growth of location-based social networks （LBSNs）, point-of-interest （POI） recommendation has become an important research problem. As one of the most representative social media platforms, Twitter provides various real-life information for POI recommendation in real time. Despite that POI recommendation has been actively studied, tweet images have not been well utilized for this research problem. State-of-the-art visual features like convolutional neural network （CNN） features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image＇s semantics. Unfortunately, they have not been employed for POI recommendation from social websites. Hence, how to make the most of tweet images to improve the performance of POI recommendation and visualization remains open In this paper, we thoroughly study the impact of tweet images on POI recommendation for different POI categories using various visual features. A novel topic model called social media Twitter-latent Dirichlet allocation （SM-TwitterLDA） which jointly models five Twitter features, （i.e., text, image, location, timestamp and hashtag） is designed to discover POIs from the sheer amount of tweets. Moreover, each POI is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.展开更多
Conventional outdoor navigation systems are usually based on orbital satellites, e.g., global positioning system （GPS） and global navigation satellite system （GLONASS）. The latest advances from wearable, e.g., Bai...Conventional outdoor navigation systems are usually based on orbital satellites, e.g., global positioning system （GPS） and global navigation satellite system （GLONASS）. The latest advances from wearable, e.g., BaiduEye and Google Glass, have enabled new approaches to leverage information from the surrounding environment. For example, they enable the change from passively receiving information to actively requesting information. Thus, such changes might inspire brand new application scenarios that were not possible before. In this work, we propose a vision-based navigation system based on wearable like Baidu Eye. We discuss the associated challenges and propose potential solutions for each of them. The system utilizes crowd sensing to collect and build a traffic signpost database for positioning reference. Then it leverages context information, such as cell identification （Cell ID）, signal strength, and altitude combined with traffic sign detection and recognition to enable real-time positioning. A hybrid cloud architecture is proposed to enhance the capability of sensing devices （SD） to realize the proposed vision.展开更多
The rapid development of location-based social networks（LBSNs） has provided an unprecedented opportunity for better location-based services through point-of-interest（POI） recommendation. POI recommendation is pers...The rapid development of location-based social networks（LBSNs） has provided an unprecedented opportunity for better location-based services through point-of-interest（POI） recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation（LDA） model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model（PMF） to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.展开更多
With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel o...With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.展开更多
文摘针对传统的物化视图选择(materialized view selection,MVS)算法评价指标单一(仅评价物化时间,过度追求物化视图的查询命中率)会导致超高维度时的维度灾难以及物化视图集频繁抖动的问题,本文提出了一种基于带权图的多维大数据模型优化算法(multi-dimensional big data model optimization,MMO),通过引入平均查询时延和膨胀率评价指标,基于带权图模型找出物化视图集的最优解。实验结果表明,本文算法在综合评分、平均查询时延、膨胀率方面均优于粒子群算法(particle swarm optimization,PSO),解决了超高维数据下的维度灾难问题,并且能够快速收敛。
基金supported by the National Key project of Scientific and Technical Supporting Programs of China (2013BAH10F01, 2013BAH07F02, 2014BAH26F02)the Research Fund for the Doctoral Program of Higher Education (20110005120007)+2 种基金Beijing Higher Education Young Elite Teacher Project (YETP0445)the Co-construction Program with Beijing Municipal Commission of EducationEngineering Research Center of Information Networks,Ministry of Education
文摘Quality-of-Service （QoS） describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. A new ＇hybrid user-location-aware prediction based on weighted Adamic-Adar （WAA）＇ （HUWAA） was proposed. The implicit neighbor search was optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem was validated in experiments on public real world datasets. The new algorithm outperforms the existing of item-based pearson correlation coefficient （IPCC）, user-based pearson correlation coefficient （UPCC） and Web service recommender system （WSRec） algorithms.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China(2014BAH26F02)
文摘With the rapid growth of location-based social networks （LBSNs）, point-of-interest （POI） recommendation has become an important research problem. As one of the most representative social media platforms, Twitter provides various real-life information for POI recommendation in real time. Despite that POI recommendation has been actively studied, tweet images have not been well utilized for this research problem. State-of-the-art visual features like convolutional neural network （CNN） features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the image＇s semantics. Unfortunately, they have not been employed for POI recommendation from social websites. Hence, how to make the most of tweet images to improve the performance of POI recommendation and visualization remains open In this paper, we thoroughly study the impact of tweet images on POI recommendation for different POI categories using various visual features. A novel topic model called social media Twitter-latent Dirichlet allocation （SM-TwitterLDA） which jointly models five Twitter features, （i.e., text, image, location, timestamp and hashtag） is designed to discover POIs from the sheer amount of tweets. Moreover, each POI is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China (2014BAK15B01)
文摘Conventional outdoor navigation systems are usually based on orbital satellites, e.g., global positioning system （GPS） and global navigation satellite system （GLONASS）. The latest advances from wearable, e.g., BaiduEye and Google Glass, have enabled new approaches to leverage information from the surrounding environment. For example, they enable the change from passively receiving information to actively requesting information. Thus, such changes might inspire brand new application scenarios that were not possible before. In this work, we propose a vision-based navigation system based on wearable like Baidu Eye. We discuss the associated challenges and propose potential solutions for each of them. The system utilizes crowd sensing to collect and build a traffic signpost database for positioning reference. Then it leverages context information, such as cell identification （Cell ID）, signal strength, and altitude combined with traffic sign detection and recognition to enable real-time positioning. A hybrid cloud architecture is proposed to enhance the capability of sensing devices （SD） to realize the proposed vision.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China (2014BAH26F00)
文摘The rapid development of location-based social networks（LBSNs） has provided an unprecedented opportunity for better location-based services through point-of-interest（POI） recommendation. POI recommendation is personalized, location-aware, and context depended. However, extreme sparsity of user-POI matrix creates a severe challenge. In this paper we propose a textual-geographical-social aware probabilistic matrix factorization method for POI recommendation. Our model is textual-geographical-social aware probabilistic matrix factorization called TGS-PMF, it exploits textual information, geographical information, social information, and incorporates these factors effectively. First, we exploit an aggregated latent Dirichlet allocation（LDA） model to learn the interest topics of users and infer the interest POIs by mining textual information associated with POIs and generate interest relevance score. Second, we propose a kernel estimation method with an adaptive bandwidth to model the geographical correlations and generate geographical relevance score. Third, we build social relevance through the power-law distribution of user social relations to generate social relevance score. Then, our exploit probabilistic matrix factorization model（PMF） to integrate the interest, geographical, social relevance scores for POI recommendation. Finally, we implement experiments on a real LBSN check-in dataset. Experimental results show that TGS-PMF achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China(2014BAK15B01)
文摘With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
