Proceedings of the Tenth ACM International Conference on Web Search and Data Mining最新文献

{"title":"Mining Medical Causality for Diagnosis Assistance","authors":"Sendong Zhao","doi":"10.1145/3018661.3022752","DOIUrl":"https://doi.org/10.1145/3018661.3022752","url":null,"abstract":"In the medical context, causal knowledge usually refers to causal relations between diseases and symptoms, living habits and diseases, symptoms which get better and therapy, drugs and side-effects, etc [3]. All these causal relations are usually in medical literature, forum and clinical cases and compose the core part of medical diagnosis. Therefore, mining these causal knowledge to predict disease and recommend therapy is of great value for assisting patients and professionals. The task of mining these causal knowledge for diagnosis assistance can be decomposed into four constitutes: (1) mining medical causality from text; (2) medical treatment effectiveness measurement; (3) disease prediction and (4) explicable medical treatment recommendation. However, these tasks have never been systemically studied before. For my PhD thesis, I plan to formally define the problem of mining medical domain causality for diagnosis assistance and propose methods to solve this problem. 1. Ming these textual causalities can be very useful for discovering new knowledge and making decisions. Many studies have been done for causal extraction from the text [1, 4, 5]. However, all these studies are based on pattern or causal triggers, which greatly limit their power to extract causality and rarely consider the frequency of co-occurrence and contextual semantic features. Besides, none of them take the transitivity rules of causality leading to reject those causalities which can be easily get by simple inference. Therefore, we formally define the task of mining causality via frequency of event co-occurrence, semantic distance between event pairs and transitivity rules of causality, and present a factor graph to combine these three resources for causality mining. 2. Treatment effectiveness analysis is usually taken as a subset of causal analysis on observational data. For such real observational data, PSM and RCM are two dominant methods. On one hand, it is usually difficult for PSM to find the matched cases due to the sparsity of symptom. On the other hand, we should check every possible (symptom, treatment) pair by exploiting RCM, leading to make the characteristic of exploding up, especially when we want to check the causal relation between a combination of symptoms and a combination of drugs. Besides, the larger number of symptom or treatment in the combination the less number of patient case retrieved, which lead to the lack of statistical significance. Specifically, patients tend to take tens of herbs as the treatment each time in Traditional Chinese Medicine (TCM). Therefore, how to evaluate the effectiveness of herbs separately and jointly is really a big challenge. This is also a very fundamental research topic supporting many downstream applications. 3. Both hospitals and on-line forums have accumulated sheer amount of records, such as clinical text data and online diagnosis Q&A pairs. The availability of such data in large volume enables automatic disease predic","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127235585","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}

{"title":"Probabilistic Social Sequential Model for Tour Recommendation","authors":"Vineeth Rakesh, Niranjan Jadhav, Alexander Kotov, C. Reddy","doi":"10.1145/3018661.3018711","DOIUrl":"https://doi.org/10.1145/3018661.3018711","url":null,"abstract":"The pervasive growth of location-based services such as Foursquare and Yelp has enabled researchers to incorpo- rate better personalization into recommendation models by leveraging the geo-temporal breadcrumbs left by a plethora of travelers. In this paper, we explore Travel path recommendation, which is one of the applications of intelligent urban navigation that aims in recommending sequence of point of interest (POIs) to tourists. Currently, travelers rely on a tedious and time-consuming process of searching the web, browsing through websites such as Trip Advisor, and reading travel blogs to compile an itinerary. On the other hand, people who do not plan ahead of their trip find it extremely difficult to do this in real-time since there are no automated systems that can provide personalized itinerary for travelers. To tackle this problem, we propose a tour recommendation model that uses a probabilistic generative framework to incorporate user's categorical preference, influence from their social circle, the dynamic travel transitions (or patterns) and the popularity of venues to recommend sequence of POIs for tourists. Through comprehensive experiments over a rich dataset of travel patterns from Foursquare, we show that our model is capable of outperforming the state-of-the-art probabilistic tour recommendation model by providing contextual and meaningful recommendation for travelers.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132901999","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}

{"title":"Beyond Query Logs: Recommendation and Evaluation","authors":"M. Mitsui","doi":"10.1145/3018661.3022747","DOIUrl":"https://doi.org/10.1145/3018661.3022747","url":null,"abstract":"Query recommendation in Web search is typically manifested in algorithms that 1) recommend previously issued queries from a query log or 2) make incremental changes to queries in a user's current session. While such approaches have been effective in improving retrieval, they either are limited to suggesting queries in a query log or fail to make appropriate leaps that are necessary for query recommendation. More crucially, these approaches only recommend queries that are a coarse approximation of the information a user needs to complete their goal. They do not directly attempt to model the need and generate recommendations from it. This work will propose a framework for generating novel yet focused queries for query recommendation.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130469557","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}

{"title":"Adapting Information Retrieval to User Signals via Stochastic Models","authors":"Maria Maistro","doi":"10.1145/3018661.3022753","DOIUrl":"https://doi.org/10.1145/3018661.3022753","url":null,"abstract":"To address the challenge of adapting Information Retrieval (IR) to the constantly evolving user tasks and needs and to adjust it to user interactions and preferences we develop a new model of user behavior based on Markov chains. We aim at integrating the proposed model into several aspects of IR, i.e. evaluation measures, systems and collections. Firstly, we studied IR evaluation measures and we propose a theoretical framework to describe their properties. Then, we presented a new family of evaluation measures, called Markov Precision (MP), based on the proposed model and able to explicitly link lab-style and on-line evaluation metrics. Future work will include the presented model into Learning to Rank (LtR) algorithms and will define a collection for evaluation and comparison of Personalized Information Retrieval (PIR) systems.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126328839","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}

{"title":"Statistical Spoken Dialogue Systems and the Challenges for Machine Learning","authors":"S. Young","doi":"10.1145/3018661.3022746","DOIUrl":"https://doi.org/10.1145/3018661.3022746","url":null,"abstract":"This talk will review the principal components of a spoken dialogue system and then discuss the opportunities for applying machine learning for building robust high performance open-domain systems. The talk will be illustrated by recent work at Cambridge University using machine learning for belief tracking, reward estimation, multi-domain policy learning and natural language generation. The talk will conclude by discussing some of the key challenges in scaling these solutions to work in practical systems.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115654380","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}

{"title":"Trustworthy Analysis of Online A/B Tests: Pitfalls, challenges and solutions","authors":"Alex Deng, Jiannan Lu, Jonthan Litz","doi":"10.1145/3018661.3018677","DOIUrl":"https://doi.org/10.1145/3018661.3018677","url":null,"abstract":"A/B tests (or randomized controlled experiments) play an integral role in the research and development cycles of technology companies. As in classic randomized experiments (e.g., clinical trials), the underlying statistical analysis of A/B tests is based on assuming the randomization unit is independent and identically distributed (iid). However, the randomization mechanisms utilized in online A/B tests can be quite complex and may render this assumption invalid. Analysis that unjustifiably relies on this assumption can yield untrustworthy results and lead to incorrect conclusions. Motivated by challenging problems arising from actual online experiments, we propose a new method of variance estimation that relies only on practically plausible assumptions, is directly applicable to a wide of range of randomization mechanisms, and can be implemented easily. We examine its performance and illustrate its advantages over two commonly used methods of variance estimation on both simulated and empirical datasets. Our results lead to a deeper understanding of the conditions under which the randomization unit can be treated as iid In particular, we show that for purposes of variance estimation, the randomization unit can be approximated as iid when the individual treatment effect variation is small; however, this approximation can lead to variance under-estimation when the individual treatment effect variation is large.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122593376","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}

{"title":"Anticipating Information Needs Based on Check-in Activity","authors":"Jan R. Benetka, K. Balog, K. Nørvåg","doi":"10.1145/3018661.3018679","DOIUrl":"https://doi.org/10.1145/3018661.3018679","url":null,"abstract":"In this work we address the development of a smart personal assistant that is capable of anticipating a user's information needs based on a novel type of context: the person's activity inferred from her check-in records on a location-based social network. Our main contribution is a method that translates a check-in activity into an information need, which is in turn addressed with an appropriate information card. This task is challenging because of the large number of possible activities and related information needs, which need to be addressed in a mobile dashboard that is limited in size. Our approach considers each possible activity that might follow after the last (and already finished) activity, and selects the top information cards such that they maximize the likelihood of satisfying the user's information needs for all possible future scenarios. The proposed models also incorporate knowledge about the temporal dynamics of information needs. Using a combination of historical check-in data and manual assessments collected via crowdsourcing, we show experimentally the effectiveness of our approach.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126267512","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}

{"title":"Quantifying and Bursting the Online Filter Bubble","authors":"Venkata Rama Kiran Garimella","doi":"10.1145/3018661.3024933","DOIUrl":"https://doi.org/10.1145/3018661.3024933","url":null,"abstract":"In this thesis, we develop methods to (i) detect and quantify the existence of filter bubbles in social media, (ii) monitor their evolution over time, and finally, (iii) devise methods to overcome the effects caused by filter bubbles. We are the first to propose an end-to-end system that solves the problem of filter bubbles completely algorithmically. We build on top of existing studies and ideas from social science with principles from graph theory to design algorithms which are language independent, domain agnostic and scalable to large number of users.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124771550","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}

{"title":"Summarizing Answers in Non-Factoid Community Question-Answering","authors":"Hongya Song, Z. Ren, Shangsong Liang, Piji Li, Jun Ma, M. de Rijke","doi":"10.1145/3018661.3018704","DOIUrl":"https://doi.org/10.1145/3018661.3018704","url":null,"abstract":"We aim at summarizing answers in community question-answering (CQA). While most previous work focuses on factoid question-answering, we focus on the non-factoid question-answering. Unlike factoid CQA, non-factoid question-answering usually requires passages as answers. The shortness, sparsity and diversity of answers form interesting challenges for summarization. To tackle these challenges, we propose a sparse coding-based summarization strategy that includes three core ingredients: short document expansion, sentence vectorization, and a sparse-coding optimization framework. Specifically, we extend each answer in a question-answering thread to a more comprehensive representation via entity linking and sentence ranking strategies. From answers extended in this manner, each sentence is represented as a feature vector trained from a short text convolutional neural network model. We then use these sentence representations to estimate the saliency of candidate sentences via a sparse-coding framework that jointly considers candidate sentences and Wikipedia sentences as reconstruction items. Given the saliency vectors for all candidate sentences, we extract sentences to generate an answer summary based on a maximal marginal relevance algorithm. Experimental results on a benchmark data collection confirm the effectiveness of our proposed method in answer summarization of non-factoid CQA, and moreover, its significant improvement compared to state-of-the-art baselines in terms of ROUGE metrics.","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125675375","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}

{"title":"Utilizing Knowledge Graphs in Text-centric Information Retrieval","authors":"Laura Dietz, Alexander Kotov, E. Meij","doi":"10.1145/3018661.3022756","DOIUrl":"https://doi.org/10.1145/3018661.3022756","url":null,"abstract":"The past decade has witnessed the emergence of several publicly available and proprietary knowledge graphs (KGs). The increasing depth and breadth of content in KGs makes them not only rich sources of structured knowledge by themselves but also valuable resources for search systems. A surge of recent developments in entity linking and retrieval methods gave rise to a new line of research that aims at utilizing KGs for text-centric retrieval applications, making this an ideal time to pause and report current findings to the community, summarizing successful approaches, and soliciting new ideas. This tutorial is the first to disseminate the progress in this emerging field to researchers and practitioners. All tutorial resources are available online at http://github.com/laura-dietz/tutorial-utilizing-kg","PeriodicalId":344017,"journal":{"name":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126721581","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}