Microblogging is a new form of communication in which users can describe their current status in ... more Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.
Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06, 2006
In recent years, weblogs, or blogs for short, have become an important form of online content. Th... more In recent years, weblogs, or blogs for short, have become an important form of online content. The personal nature of blogs, online interactions between bloggers, and the temporal nature of blog entries, differentiate blogs from other kinds of Web content. Bloggers interact with each other by linking to each other's posts, thus forming online communities. Within these communities, bloggers engage in discussions of certain issues, through entries in their blogs. Since these discussions are often initiated in response to online or offline events, a discussion typically lasts for a limited time duration. We wish to extract such temporal discussions, or stories, occurring within blogger communities, based on some query keywords. We propose a Content-Community-Time model that can leverage the content of entries, their timestamps, and the community structure of the blogs, to automatically discover stories. Doing so also allows us to discover hot stories. We demonstrate the effectiveness of our model through several case studies using real-world data collected from the blogosphere.
There are many ways of capturing images to represent a detailed scene. Our motivation is to use i... more There are many ways of capturing images to represent a detailed scene. Our motivation is to use inexpensive digital cameras with little setup requirements and to allow photographers to differentially capture both low-resolution overviews and high-resolution details. We present the heterogeneous image pyramid as a non-uniform representation composed of multiple captured multi-resolution images. Each resolution image captures a specific portion
Microblogging is a new form of communication in which users describe their current status in shor... more Microblogging is a new form of communication in which users describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. We present our observations of the microblogging phenomena by studying the topological and geographical properties of the social network in Twitter, one of the most popular microblogging systems. We find that people use microblogging primarily to talk about their daily activities and to seek or share information. We present a taxonomy characterizing the ...
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis - WebKDD/SNA-KDD '07, 2007
Microblogging is a new form of communication in which users can describe their current status in ... more Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.
The paper citation network is a traditional social medium for the exchange of ideas and knowledge... more The paper citation network is a traditional social medium for the exchange of ideas and knowledge. In this paper we view citation networks from the perspective of information diffusion. We study the structural features of the information paths through the citation networks of publications in computer science, and analyze the impact of various citation choices on the subsequent impact of the article. We find that citing recent papers and papers within the same scholarly community garners a slightly larger number of citations on average. However, this correlation is weaker among well-cited papers implying that for high impact work citing within one's field is of lesser importance. We also study differences in information flow for specific subsets of citation networks: books versus conference and journal articles, different areas of computer science, and different time periods.
The blogosphere is a vast and dynamic complex network. Any examination of the structure of such a... more The blogosphere is a vast and dynamic complex network. Any examination of the structure of such a network is dependent on the selection of blogs sampled and the time frame of the sample. By comparing two large blog datasets, we demonstrate that samples may differ significantly in their coverage but still show consistency in their aggregate network properties. We further compare the structure of a blog dataset with and without spam blogs, which account for a majority of the links in one sample. We also show that properties such as degree distributions and clustering coefficients depend on the time frame over which the network is aggregated.
Proceedings of the 8th international workshop on Multimedia data mining (associated with the ACM SIGKDD 2007) - MDM '07, 2007
... objective is to understand the blogosphere and summarize the wis- dom of crowds. ... influenc... more ... objective is to understand the blogosphere and summarize the wis- dom of crowds. ... influence each other in all facets of life, including the wisdom of crowd in ...
Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06, 2006
The blogosphere-the totality of blog-related Web siteshas become a great source of trend analysis... more The blogosphere-the totality of blog-related Web siteshas become a great source of trend analysis in areas such as product survey, customer relationship, and marketing. Existing approaches are based on simple counts, such as the number of entries or the number of links. In this paper, we introduce a novel concept, coined eigen-trend, to represent the temporal trend in a group of blogs with common interests and propose two new techniques for extracting eigentrends in blogs. First, we propose a trend analysis technique based on the singular value decomposition. Extracted eigentrends provide new insights into multiple trends on the same keyword. Second, we propose another trend analysis technique based on a higher-order singular value decomposition. This analyzes the blogosphere as a dynamic graph structure and extracts eigen-trends that reflect the structural changes of the blogosphere over time. Experimental studies based on synthetic data sets and a real blog data set show that our new techniques can reveal a lot of interesting trend information and insights in the blogosphere that are not obtainable from traditional count-based methods.
2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004
ABSTRACT We describe a novel content transcoding middleware for accessing military geospatial int... more ABSTRACT We describe a novel content transcoding middleware for accessing military geospatial intelligence information in real-time. Intelligence information, including maps and location, category and properties of object targets, is adapted for various pervasive devices such as laptop, personal digital assistant (PDA), cellular phone, etc. The middleware is deployed as proxies on the Web using the IBM Websphere Transcoding Publisher (WTP) platform, which facilitates the middleware management. We developed several Java-based plug-ins and Extensible Stylesheet Language (XSL) stylesheets for content transcoding. A prototype has been established and real experiments have demonstrated the effectiveness of this novel middleware.
We exploit dynamic patterns from both documents' and users' aspects to build models for recommend... more We exploit dynamic patterns from both documents' and users' aspects to build models for recommendation. We propose a Community-Based Dynamic Recommendation (CBDR) scheme to make recommendations by taking content semantics, evolutionary patterns, and user communities into consideration. A Time-Sensitive Adaboost algorithm is proposed to build adaptive user models for ranking document candidates based on leveraging dynamic factors such as freshness, popularity, and other attributes. Our experimental results on a large online application system demonstrate the recommendation usefulness of the CBDR scheme is 259% better than the collaborative filtering, 126% better than the community-based static recommendation algorithm, and 106% better than the optimal global recommendation bound.
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07, 2007
Evolutionary clustering is an emerging research area essential to important applications such as ... more Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams. In evolutionary clustering, a good clustering result should fit the current data well, while simultaneously not deviate too dramatically from the recent history. To fulfill this dual purpose, a measure of temporal smoothness is integrated in the overall measure of clustering quality. In this paper, we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering. For both frameworks, we start with intuitions gained from the well-known k-means clustering problem, and then propose and solve corresponding cost functions for the evolutionary spectral clustering problems. Our solutions to the evolutionary spectral clustering problems provide more stable and consistent clustering results that are less sensitive to short-term noises while at the same time are adaptive to long-term cluster drifts. Furthermore, we demonstrate that our methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems. Performance experiments over a number of real and synthetic data sets illustrate our evolutionary spectral clustering methods provide more robust clustering results that are not sensitive to noise and can adapt to data drifts.
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management - CIKM '07, 2007
Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate the... more Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. Opinion leaders capture the most representative opinions in the social network, and consequently are important for understanding the massive and complex blogosphere. In this paper, we propose a novel algorithm called InfluenceRank to identify opinion leaders in the blogosphere. The InfluenceRank algorithm ranks blogs according to not only how important they are as compared to other blogs, but also how novel the information they can contribute to the network. Experimental results indicate that our proposed algorithm is effective in identifying influential opinion leaders.
Proceedings of the 16th international conference on World Wide Web - WWW '07, 2007
Information flows in a network where individuals influence each other. The diffusion rate capture... more Information flows in a network where individuals influence each other. The diffusion rate captures how efficiently the information can diffuse among the users in the network. We propose an information flow model that leverages diffusion rates for: (1) prediction -identify where information should flow to, and (2) ranking -identify who will most quickly receive the information. For prediction, we measure how likely information will propagate from a specific sender to a specific receiver during a certain time period. Accordingly a rate-based recommendation algorithm is proposed that predicts who will most likely receive the information during a limited time period. For ranking, we estimate the expected time for information diffusion to reach a specific user in a network. Subsequently, a DiffusionRank algorithm is proposed that ranks users based on how quickly information will flow to them. Experiments on two datasets demonstrate the effectiveness of the proposed algorithms to both improve the recommendation performance and rank users by the efficiency of information flow.
Proceeding of the 17th international conference on World Wide Web - WWW '08, 2008
We discover communities from social network data, and analyze the community evolution. These comm... more We discover communities from social network data, and analyze the community evolution. These communities are inherent characteristics of human interaction in online social networks, as well as paper citation networks. Also, communities may evolve over time, due to changes to individuals' roles and social status in the network as well as changes to individuals' research interests. We present an innovative algorithm that deviates from the traditional two-step approach to analyze community evolutions. In the traditional approach, communities are first detected for each time slice, and then compared to determine correspondences. We argue that this approach is inappropriate in applications with noisy data. In this paper, we propose FacetNet for analyzing communities and their evolutions through a robust unified process. In this novel framework, communities not only generate evolutions, they also are regularized by the temporal smoothness of evolutions. As a result, this framework will discover communities that jointly maximize the fit to the observed data and the temporal evolution. Our approach relies on formulating the problem in terms of non-negative matrix factorization, where communities and their evolutions are factorized in a unified way. Then we develop an iterative algorithm, with proven low time complexity, which is guaranteed to converge to an optimal solution. We perform extensive experimental studies, on both synthetic datasets and real datasets, to demonstrate that our method discovers meaningful communities and provides additional insights not directly obtainable from traditional methods.
Visual Communications and Image Processing 2001, 2000
ABSTRACT We present a novel whiteboard system that uses one or more active input devices. The sys... more ABSTRACT We present a novel whiteboard system that uses one or more active input devices. The system is especially suitable for situations in which several users provide input on a large writing surface which also serves as a projection surface. That surface can have general orientation. We have implemented a system that uses an inexpensive, infrared-emitting stylus and an off-the-shelf videoconferencing camera fitted with an IR transmitting/visible blocking filter to capture handwritten strokes. The system uses a calibration method that uses projective mapping thus allowing off-axis camera placement. We also propose a self-calibrating system in which the capture device shares its optical system with the projector. In a system with multiple local users the question of 'who wrote what' becomes relevant. Therefore, we address the question of attaching identify to stroke information.
Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12, 2012
Many vertical search tasks such as local search focus on specific domains. The meaning of relevan... more Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-defined aspects (e.g., text matching and distance in local search). Thus the overall relevance between a query and a document is a tradeoff between multiple relevance aspects. Such a tradeoff can vary for different types of queries or in different contexts. In this paper, we explore these vertical-specific aspects in the learning to rank setting. We propose a novel formulation in which the relevance between a query and a document is assessed with respect to each aspect, forming the multi-aspect relevance. In order to compute a ranking function, we study two types of learning-based approaches to estimate the tradeoff between these relevance aspects: a label aggregation method and a model aggregation method. Since there are only a few aspects, a minimal amount of training data is needed to learn the tradeoff. We conduct both offline and online test experiments on a local search engine and the experimental results show that our proposed multi-aspect relevance formulation is very promising. The two types of aggregation methods perform more effectively than a set of baseline methods including a conventional learning to rank method.
2002 IEEE Workshop on Multimedia Signal Processing., 2002
... Our adaptation transforms -consist of multiple transcoders, filters, and our VideoEd Editing ... more ... Our adaptation transforms -consist of multiple transcoders, filters, and our VideoEd Editing and Composition Tool ... VideoEd is a MF'EG system-layer compressed-domain editing tool that directly extracts video-audio information from original MPEG ... by automatic speech recognition ...
Microblogging is a new form of communication in which users can describe their current status in ... more Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.
Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06, 2006
In recent years, weblogs, or blogs for short, have become an important form of online content. Th... more In recent years, weblogs, or blogs for short, have become an important form of online content. The personal nature of blogs, online interactions between bloggers, and the temporal nature of blog entries, differentiate blogs from other kinds of Web content. Bloggers interact with each other by linking to each other's posts, thus forming online communities. Within these communities, bloggers engage in discussions of certain issues, through entries in their blogs. Since these discussions are often initiated in response to online or offline events, a discussion typically lasts for a limited time duration. We wish to extract such temporal discussions, or stories, occurring within blogger communities, based on some query keywords. We propose a Content-Community-Time model that can leverage the content of entries, their timestamps, and the community structure of the blogs, to automatically discover stories. Doing so also allows us to discover hot stories. We demonstrate the effectiveness of our model through several case studies using real-world data collected from the blogosphere.
There are many ways of capturing images to represent a detailed scene. Our motivation is to use i... more There are many ways of capturing images to represent a detailed scene. Our motivation is to use inexpensive digital cameras with little setup requirements and to allow photographers to differentially capture both low-resolution overviews and high-resolution details. We present the heterogeneous image pyramid as a non-uniform representation composed of multiple captured multi-resolution images. Each resolution image captures a specific portion
Microblogging is a new form of communication in which users describe their current status in shor... more Microblogging is a new form of communication in which users describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. We present our observations of the microblogging phenomena by studying the topological and geographical properties of the social network in Twitter, one of the most popular microblogging systems. We find that people use microblogging primarily to talk about their daily activities and to seek or share information. We present a taxonomy characterizing the ...
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis - WebKDD/SNA-KDD '07, 2007
Microblogging is a new form of communication in which users can describe their current status in ... more Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.
The paper citation network is a traditional social medium for the exchange of ideas and knowledge... more The paper citation network is a traditional social medium for the exchange of ideas and knowledge. In this paper we view citation networks from the perspective of information diffusion. We study the structural features of the information paths through the citation networks of publications in computer science, and analyze the impact of various citation choices on the subsequent impact of the article. We find that citing recent papers and papers within the same scholarly community garners a slightly larger number of citations on average. However, this correlation is weaker among well-cited papers implying that for high impact work citing within one's field is of lesser importance. We also study differences in information flow for specific subsets of citation networks: books versus conference and journal articles, different areas of computer science, and different time periods.
The blogosphere is a vast and dynamic complex network. Any examination of the structure of such a... more The blogosphere is a vast and dynamic complex network. Any examination of the structure of such a network is dependent on the selection of blogs sampled and the time frame of the sample. By comparing two large blog datasets, we demonstrate that samples may differ significantly in their coverage but still show consistency in their aggregate network properties. We further compare the structure of a blog dataset with and without spam blogs, which account for a majority of the links in one sample. We also show that properties such as degree distributions and clustering coefficients depend on the time frame over which the network is aggregated.
Proceedings of the 8th international workshop on Multimedia data mining (associated with the ACM SIGKDD 2007) - MDM '07, 2007
... objective is to understand the blogosphere and summarize the wis- dom of crowds. ... influenc... more ... objective is to understand the blogosphere and summarize the wis- dom of crowds. ... influence each other in all facets of life, including the wisdom of crowd in ...
Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06, 2006
The blogosphere-the totality of blog-related Web siteshas become a great source of trend analysis... more The blogosphere-the totality of blog-related Web siteshas become a great source of trend analysis in areas such as product survey, customer relationship, and marketing. Existing approaches are based on simple counts, such as the number of entries or the number of links. In this paper, we introduce a novel concept, coined eigen-trend, to represent the temporal trend in a group of blogs with common interests and propose two new techniques for extracting eigentrends in blogs. First, we propose a trend analysis technique based on the singular value decomposition. Extracted eigentrends provide new insights into multiple trends on the same keyword. Second, we propose another trend analysis technique based on a higher-order singular value decomposition. This analyzes the blogosphere as a dynamic graph structure and extracts eigen-trends that reflect the structural changes of the blogosphere over time. Experimental studies based on synthetic data sets and a real blog data set show that our new techniques can reveal a lot of interesting trend information and insights in the blogosphere that are not obtainable from traditional count-based methods.
2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004
ABSTRACT We describe a novel content transcoding middleware for accessing military geospatial int... more ABSTRACT We describe a novel content transcoding middleware for accessing military geospatial intelligence information in real-time. Intelligence information, including maps and location, category and properties of object targets, is adapted for various pervasive devices such as laptop, personal digital assistant (PDA), cellular phone, etc. The middleware is deployed as proxies on the Web using the IBM Websphere Transcoding Publisher (WTP) platform, which facilitates the middleware management. We developed several Java-based plug-ins and Extensible Stylesheet Language (XSL) stylesheets for content transcoding. A prototype has been established and real experiments have demonstrated the effectiveness of this novel middleware.
We exploit dynamic patterns from both documents' and users' aspects to build models for recommend... more We exploit dynamic patterns from both documents' and users' aspects to build models for recommendation. We propose a Community-Based Dynamic Recommendation (CBDR) scheme to make recommendations by taking content semantics, evolutionary patterns, and user communities into consideration. A Time-Sensitive Adaboost algorithm is proposed to build adaptive user models for ranking document candidates based on leveraging dynamic factors such as freshness, popularity, and other attributes. Our experimental results on a large online application system demonstrate the recommendation usefulness of the CBDR scheme is 259% better than the collaborative filtering, 126% better than the community-based static recommendation algorithm, and 106% better than the optimal global recommendation bound.
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07, 2007
Evolutionary clustering is an emerging research area essential to important applications such as ... more Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams. In evolutionary clustering, a good clustering result should fit the current data well, while simultaneously not deviate too dramatically from the recent history. To fulfill this dual purpose, a measure of temporal smoothness is integrated in the overall measure of clustering quality. In this paper, we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering. For both frameworks, we start with intuitions gained from the well-known k-means clustering problem, and then propose and solve corresponding cost functions for the evolutionary spectral clustering problems. Our solutions to the evolutionary spectral clustering problems provide more stable and consistent clustering results that are less sensitive to short-term noises while at the same time are adaptive to long-term cluster drifts. Furthermore, we demonstrate that our methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems. Performance experiments over a number of real and synthetic data sets illustrate our evolutionary spectral clustering methods provide more robust clustering results that are not sensitive to noise and can adapt to data drifts.
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management - CIKM '07, 2007
Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate the... more Opinion leaders are those who bring in new information, ideas, and opinions, then disseminate them down to the masses, and thus influence the opinions and decisions of others by a fashion of word of mouth. Opinion leaders capture the most representative opinions in the social network, and consequently are important for understanding the massive and complex blogosphere. In this paper, we propose a novel algorithm called InfluenceRank to identify opinion leaders in the blogosphere. The InfluenceRank algorithm ranks blogs according to not only how important they are as compared to other blogs, but also how novel the information they can contribute to the network. Experimental results indicate that our proposed algorithm is effective in identifying influential opinion leaders.
Proceedings of the 16th international conference on World Wide Web - WWW '07, 2007
Information flows in a network where individuals influence each other. The diffusion rate capture... more Information flows in a network where individuals influence each other. The diffusion rate captures how efficiently the information can diffuse among the users in the network. We propose an information flow model that leverages diffusion rates for: (1) prediction -identify where information should flow to, and (2) ranking -identify who will most quickly receive the information. For prediction, we measure how likely information will propagate from a specific sender to a specific receiver during a certain time period. Accordingly a rate-based recommendation algorithm is proposed that predicts who will most likely receive the information during a limited time period. For ranking, we estimate the expected time for information diffusion to reach a specific user in a network. Subsequently, a DiffusionRank algorithm is proposed that ranks users based on how quickly information will flow to them. Experiments on two datasets demonstrate the effectiveness of the proposed algorithms to both improve the recommendation performance and rank users by the efficiency of information flow.
Proceeding of the 17th international conference on World Wide Web - WWW '08, 2008
We discover communities from social network data, and analyze the community evolution. These comm... more We discover communities from social network data, and analyze the community evolution. These communities are inherent characteristics of human interaction in online social networks, as well as paper citation networks. Also, communities may evolve over time, due to changes to individuals' roles and social status in the network as well as changes to individuals' research interests. We present an innovative algorithm that deviates from the traditional two-step approach to analyze community evolutions. In the traditional approach, communities are first detected for each time slice, and then compared to determine correspondences. We argue that this approach is inappropriate in applications with noisy data. In this paper, we propose FacetNet for analyzing communities and their evolutions through a robust unified process. In this novel framework, communities not only generate evolutions, they also are regularized by the temporal smoothness of evolutions. As a result, this framework will discover communities that jointly maximize the fit to the observed data and the temporal evolution. Our approach relies on formulating the problem in terms of non-negative matrix factorization, where communities and their evolutions are factorized in a unified way. Then we develop an iterative algorithm, with proven low time complexity, which is guaranteed to converge to an optimal solution. We perform extensive experimental studies, on both synthetic datasets and real datasets, to demonstrate that our method discovers meaningful communities and provides additional insights not directly obtainable from traditional methods.
Visual Communications and Image Processing 2001, 2000
ABSTRACT We present a novel whiteboard system that uses one or more active input devices. The sys... more ABSTRACT We present a novel whiteboard system that uses one or more active input devices. The system is especially suitable for situations in which several users provide input on a large writing surface which also serves as a projection surface. That surface can have general orientation. We have implemented a system that uses an inexpensive, infrared-emitting stylus and an off-the-shelf videoconferencing camera fitted with an IR transmitting/visible blocking filter to capture handwritten strokes. The system uses a calibration method that uses projective mapping thus allowing off-axis camera placement. We also propose a self-calibrating system in which the capture device shares its optical system with the projector. In a system with multiple local users the question of 'who wrote what' becomes relevant. Therefore, we address the question of attaching identify to stroke information.
Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12, 2012
Many vertical search tasks such as local search focus on specific domains. The meaning of relevan... more Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-defined aspects (e.g., text matching and distance in local search). Thus the overall relevance between a query and a document is a tradeoff between multiple relevance aspects. Such a tradeoff can vary for different types of queries or in different contexts. In this paper, we explore these vertical-specific aspects in the learning to rank setting. We propose a novel formulation in which the relevance between a query and a document is assessed with respect to each aspect, forming the multi-aspect relevance. In order to compute a ranking function, we study two types of learning-based approaches to estimate the tradeoff between these relevance aspects: a label aggregation method and a model aggregation method. Since there are only a few aspects, a minimal amount of training data is needed to learn the tradeoff. We conduct both offline and online test experiments on a local search engine and the experimental results show that our proposed multi-aspect relevance formulation is very promising. The two types of aggregation methods perform more effectively than a set of baseline methods including a conventional learning to rank method.
2002 IEEE Workshop on Multimedia Signal Processing., 2002
... Our adaptation transforms -consist of multiple transcoders, filters, and our VideoEd Editing ... more ... Our adaptation transforms -consist of multiple transcoders, filters, and our VideoEd Editing and Composition Tool ... VideoEd is a MF'EG system-layer compressed-domain editing tool that directly extracts video-audio information from original MPEG ... by automatic speech recognition ...
Uploads
Papers by Belle Tseng