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    Community Question Answering (CQA) has emerged as a popular forum for users to pose questions for other users to answer. Over the last few years, CQA portals such as Naver and Yahoo! Answers have exploded in popularity, and now provide a viable alternative to general purpose Web search. At the same time, the answers to past questions submitted in CQA sites comprise a valuable knowledge repository which could be a gold mine for information retrieval and automatic question answering. Unfortunately, the quality of the submitted questions and answers varies widely - increasingly so that a large fraction of the content is not usable for answering queries. Previous approaches for retrieving relevant and high quality content have been proposed, but they require large amounts of manually labeled data – which limits the applicability of the supervised approaches to new sites and domains. In this paper we address this problem by developing a semi-supervised coupled mutual reinforcement framework for simultaneously calculating content quality and user reputation, that requires relatively few labeled examples to initialize the training process. Results of a large scale evaluation demonstrate that our methods are more effective than previous approaches for finding high-quality answers, questions, and users. More importantly, our quality estimation significantly improves the accuracy of search over CQA archives over the state-of-the-art methods.
    15 years ago by @dbenz
     
      attendedwww2009
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      We analyse the corpus of user relationships of the Slash- dot technology news site. The data was collected from the Slashdot Zoo feature where users of the website can tag other users as friends and foes, providing positive and negative en- dorsements. We adapt social network analysis techniques to the problem of negative edge weights. In particular, we con- sider signed variants of global network characteristics such as the clustering coefficient, node-level characteristics such as centrality and popularity measures, and link-level character- istics such as distances and similarity measures. We evaluate these measures on the task of identifying unpopular users, as well as on the task of predicting the sign of links and show that the network exhibits multiplicative transitivity which allows algebraic methods based on matrix multiplication to be used. We compare our methods to traditional methods which are only suitable for positively weighted edges.
      15 years ago by @dbenz
       
        attendedwww2009
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        Users of social networking services can connect with each other by forming communities for online interaction. Yet as the number of communities hosted by such websites grows over time, users have even greater need for effective commu- nity recommendations in order to meet more users. In this paper, we investigate two algorithms from very different do- mains and evaluate their effectiveness for personalized com- munity recommendation. First is association rule mining (ARM), which discovers associations between sets of com- munities that are shared across many users. Second is latent Dirichlet allocation (LDA), which models user-community co-occurrences using latent aspects. In comparing LDA with ARM, we are interested in discovering whether modeling low-rank latent structure is more effective for recommen- dations than directly mining rules from the observed data. We experiment on an Orkut data set consisting of 492, 104 users and 118, 002 communities. Our empirical comparisons using the top-k recommendations metric show that LDA performs consistently better than ARM for the community recommendation task when recommending a list of 4 or more communities. However, for recommendation lists of up to 3 communities, ARM is still a bit better. We analyze exam- ples of the latent information learned by LDA to explain this finding. To efficiently handle the large-scale data set, we parallelize LDA on distributed computers [1] and demon- strate our parallel implementation’s scalability with varying numbers of machines.
        15 years ago by @dbenz
         
          attendedwww2009
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          In this paper we give models and algorithms to describe and analyze the collaboration among authors of Wikipedia from a network analytical perspective. The edit network encodes who interacts how with whom when editing an article; it significantly extends previous network models that code author communities in Wikipedia. Several characteristics summarizing some aspects of the organization process and allowing the analyst to identify certain types of authors can be obtained from the edit network. Moreover, we propose several indicators characterizing the global network structure and methods to visualize edit networks. It is shown that the structural network indicators are correlated with quality labels of the associated Wikipedia articles.
          15 years ago by @dbenz
           
            attendedwww2009
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            Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the flexibility and conceptual comprehensibility inherent in tagging systems. In this paper we explore tagommenders, recommender algorithms that predict users’ preferences for items based on their inferred preferences for tags. We describe tag preference inference algorithms based on users’ interactions with tags and movies, and evaluate these algorithms based on tag preference ratings collected from 995 MovieLens users. We design and evaluate algorithms that predict users’ ratings for movies based on their inferred tag preferences. Our tag-based algorithms generate better recommendation rankings than state-of-the-art algorithms, and they may lead to flexible recommender systems that leverage the characteristics of items users find most important.
            15 years ago by @dbenz
             
              attendedwww2009
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              Fabrizio Silvestri, ISTI - CNR Ricardo Baeza-Yates, Yahoo! Research Abstract Web Search Engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this tutorial is to introduce to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that could be used to extract and to exploit useful knowledge from this (potentially) infinite source of information. Furthermore, participants to this tutorial will be given a unified view on the literature on query log analysis.
              15 years ago by @dbenz
               
                attendedquery_log_miningwww2009tutorial
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                With the advent of Web 2.0, Social Computing has emerged as one of the hot research topics recently. Social Computing involves the investigation of collective intelligence by using computational techniques such as machine learning, data mining, natural language processing, etc. on social behavior data collected from blogs, wikis, clickthrough data, query logs, tags, etc. from areas such as social networks, social search, social media, social bookmarks, social news, social knowledge sharing, and social games. In this tutorial, we will introduce Social Computing and elaborate on how the various characteristics and aspects are involved in the social platforms for collective intelligence. Moreover, we will discuss the challenging issues involved in Social Computing in the context of theory and models of social networks, mining of spatial and temporal social information, ways to deal with partial and incomplete information in the social context, scalability and algorithmic issues with social computational techniques, and security & privacy issues. Some potential social computing applications for discussion would include collaborative filtering, query log processing, learning to rank, large graph and link algorithms, etc.
                15 years ago by @dbenz
                 
                  attendedwww2009irwin_kingtutorialsocial_computing
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                publications  3