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Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval最新文献

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Detecting outlier sections in us congressional legislation 检测美国国会立法中的异常部分
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2009951
Elif Aktolga, Irene Ros, Yannick Assogba
{"title":"Detecting outlier sections in us congressional legislation","authors":"Elif Aktolga, Irene Ros, Yannick Assogba","doi":"10.1145/2009916.2009951","DOIUrl":"https://doi.org/10.1145/2009916.2009951","url":null,"abstract":"Reading congressional legislation, also known as bills, is often tedious because bills tend to be long and written in complex language. In IBM Many Bills, an interactive web-based visualization of legislation, users of different backgrounds can browse bills and quickly explore parts that are of interest to them. One task users have is to be able to locate sections that don't seem to fit with the overall topic of the bill. In this paper, we present novel techniques to determine which sections within a bill are likely to be outliers by employing approaches from information retrieval. The most promising techniques first detect the most topically relevant parts of a bill by ranking its sections, followed by a comparison between these topically relevant parts and the remaining sections in the bill. To compare sections we use various dissimilarity metrics based on Kullback-Leibler Divergence. The results indicate that these techniques are more successful than a classification based approach. Finally, we analyze how the dissimilarity metrics succeed in discriminating between sections that are strong outliers versus those that are 'milder' outliers.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129936952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Probabilistic factor models for web site recommendation 网站推荐的概率因子模型
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2009955
Hao Ma, Chao Liu, Irwin King, Michael R. Lyu
{"title":"Probabilistic factor models for web site recommendation","authors":"Hao Ma, Chao Liu, Irwin King, Michael R. Lyu","doi":"10.1145/2009916.2009955","DOIUrl":"https://doi.org/10.1145/2009916.2009955","url":null,"abstract":"Due to the prevalence of personalization and information filtering applications, modeling users' interests on the Web has become increasingly important during the past few years. In this paper, aiming at providing accurate personalized Web site recommendations for Web users, we propose a novel probabilistic factor model based on dimensionality reduction techniques. We also extend the proposed method to collective probabilistic factor modeling, which further improves model performance by incorporating heterogeneous data sources. The proposed method is general, and can be applied to not only Web site recommendations, but also a wide range of Web applications, including behavioral targeting, sponsored search, etc. The experimental analysis on Web site recommendation shows that our method outperforms other traditional recommendation approaches. Moreover, the complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127264022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 80
System effectiveness, user models, and user utility: a conceptual framework for investigation 系统有效性、用户模型和用户效用:研究的概念框架
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010037
Ben Carterette
{"title":"System effectiveness, user models, and user utility: a conceptual framework for investigation","authors":"Ben Carterette","doi":"10.1145/2009916.2010037","DOIUrl":"https://doi.org/10.1145/2009916.2010037","url":null,"abstract":"There is great interest in producing effectiveness measures that model user behavior in order to better model the utility of a system to its users. These measures are often formulated as a sum over the product of a discount function of ranks and a gain function mapping relevance assessments to numeric utility values. We develop a conceptual framework for analyzing such effectiveness measures based on classifying members of this broad family of measures into four distinct families, each of which reflects a different notion of system utility. Within this framework we can hypothesize about the properties that such a measure should have and test those hypotheses against user and system data. Along the way we present a collection of novel results about specific measures and relationships between them.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127710342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 146
Learning for graphs with annotated edges 带注释边的图的学习
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010148
Fan Li
{"title":"Learning for graphs with annotated edges","authors":"Fan Li","doi":"10.1145/2009916.2010148","DOIUrl":"https://doi.org/10.1145/2009916.2010148","url":null,"abstract":"Automatic classification with graphs containing annotated edges is an interesting problem and has many potential applications. We present a risk minimization formulation that exploits the annotated edges for classification tasks. One major advantage of our approach compared to other methods is that the weight of each edge in the graph structures in our model, including both positive and negative weights, can be learned automatically from training data based on edge features. The empirical results show that our approach can lead to significantly improved classification performance compared to several baseline approaches.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122591121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhanced results for web search 增强的网络搜索结果
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010014
Kevin Haas, P. Mika, P. Tarjan, Roi Blanco
{"title":"Enhanced results for web search","authors":"Kevin Haas, P. Mika, P. Tarjan, Roi Blanco","doi":"10.1145/2009916.2010014","DOIUrl":"https://doi.org/10.1145/2009916.2010014","url":null,"abstract":"\"Ten blue links\" have defined web search results for the last fifteen years -- snippets of text combined with document titles and URLs. In this paper, we establish the notion of enhanced search results that extend web search results to include multimedia objects such as images and video, intent-specific key value pairs, and elements that allow the user to interact with the contents of a web page directly from the search results page. We show that users express a preference for enhanced results both explicitly, and when observed in their search behavior. We also demonstrate the effectiveness of enhanced results in helping users to assess the relevance of search results. Lastly, we show that we can efficiently generate enhanced results to cover a significant fraction of search result pages.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122849108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 48
No Free Lunch: Brute Force vs. Locality-Sensitive Hashing for Cross-lingual Pairwise Similarity 没有免费的午餐:蛮力vs.地域敏感哈希跨语言两两相似性
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010042
Ferhan Ture, T. Elsayed, Jimmy J. Lin
{"title":"No Free Lunch: Brute Force vs. Locality-Sensitive Hashing for Cross-lingual Pairwise Similarity","authors":"Ferhan Ture, T. Elsayed, Jimmy J. Lin","doi":"10.1145/2009916.2010042","DOIUrl":"https://doi.org/10.1145/2009916.2010042","url":null,"abstract":"This work explores the problem of cross-lingual pairwise similarity, where the task is to extract similar pairs of documents across two different languages. Solutions to this problem are of general interest for text mining in the multilingual context and have specific applications in statistical machine translation. Our approach takes advantage of cross-language information retrieval (CLIR) techniques to project feature vectors from one language into another, and then uses locality-sensitive hashing (LSH) to extract similar pairs. We show that effective cross-lingual pairwise similarity requires working with similarity thresholds that are much lower than in typical monolingual applications, making the problem quite challenging. We present a parallel, scalable MapReduce implementation of the sort-based sliding window algorithm, which is compared to a brute-force approach on German and English Wikipedia collections. Our central finding can be summarized as“no free lunch”: there is no single optimal solution. Instead, we characterize effectivenessefficiency tradeoffs in the solution space, which can guide the developer to locate a desirable operating point based on applicationand resource-specific constraints.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"9 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132406559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 46
Evaluating multi-query sessions 评估多查询会话
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010056
E. Kanoulas, Ben Carterette, Paul D. Clough, M. Sanderson
{"title":"Evaluating multi-query sessions","authors":"E. Kanoulas, Ben Carterette, Paul D. Clough, M. Sanderson","doi":"10.1145/2009916.2010056","DOIUrl":"https://doi.org/10.1145/2009916.2010056","url":null,"abstract":"The standard system-based evaluation paradigm has focused on assessing the performance of retrieval systems in serving the best results for a single query. Real users, however, often begin an interaction with a search engine with a sufficiently under-specified query that they will need to reformulate before they find what they are looking for. In this work we consider the problem of evaluating retrieval systems over test collections of multi-query sessions. We propose two families of measures: a model-free family that makes no assumption about the user's behavior over a session, and a model-based family with a simple model of user interactions over the session. In both cases we generalize traditional evaluation metrics such as average precision to multi-query session evaluation. We demonstrate the behavior of the proposed metrics by using the new TREC 2010 Session track collection and simulations over the TREC-9 Query track collection.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132631624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 88
Learning to rank under tight budget constraints 学习在预算紧张的情况下排名
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010105
Christian Pölitz, Ralf Schenkel
{"title":"Learning to rank under tight budget constraints","authors":"Christian Pölitz, Ralf Schenkel","doi":"10.1145/2009916.2010105","DOIUrl":"https://doi.org/10.1145/2009916.2010105","url":null,"abstract":"This paper investigates the influence of pruning feature lists to keep a given budget for the evaluation of ranking methods. We learn from a given training set how important the individual prefixes are for the ranking quality. Based on there importance we choose the best prefixes to calculate the ranking while keeping the budget.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131942825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Modeling subset distributions for verbose queries 为详细查询建模子集分布
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010085
Xiaobing Xue, W. Bruce Croft
{"title":"Modeling subset distributions for verbose queries","authors":"Xiaobing Xue, W. Bruce Croft","doi":"10.1145/2009916.2010085","DOIUrl":"https://doi.org/10.1145/2009916.2010085","url":null,"abstract":"Improving verbose (or long) queries poses a new challenge for search systems. Previous techniques mainly focused on two aspects, weighting the important words or phrases and selecting the best subset query. The former does not consider how words and phrases are used in actual subset queries, while the latter ignores alternative subset queries. Recently, a novel reformulation framework has been proposed to transform the original query as a distribution of reformulated queries, which overcomes the disadvantages of previous techniques. In this paper, we apply this framework to verbose queries, where a reformulated query is specified as a subset query. Experiments on TREC collections show that the query distribution based framework outperforms the state-of-the-art techniques.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130808539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Rating prediction using feature words extracted from customer reviews 使用从客户评论中提取的特征词进行评级预测
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval Pub Date : 2011-07-24 DOI: 10.1145/2009916.2010121
Masanao Ochi, Makoto Okabe, R. Onai
{"title":"Rating prediction using feature words extracted from customer reviews","authors":"Masanao Ochi, Makoto Okabe, R. Onai","doi":"10.1145/2009916.2010121","DOIUrl":"https://doi.org/10.1145/2009916.2010121","url":null,"abstract":"We developed a simple method of improving the accuracy of rating prediction using feature words extracted from customer reviews. Many rating predictors work well for a small and dense dataset of customer reviews. However, a practical dataset tends to be large and sparse, because it often includes too many products for each customer to buy and evaluate. Data sparseness reduces prediction accuracy. To improve accuracy, we reduced the dimension of the feature vector using feature words extracted by analyzing the relationship between ratings and accompanying review comments instead of using ratings. We applied our method to the Pranking algorithm and evaluated it on a corpus of golf course reviews supplied by a Japanese e-commerce company. We found that by successfully reducing data sparseness, our method improves prediction accuracy as measured using RankLoss.","PeriodicalId":356580,"journal":{"name":"Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127238069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
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