OpenRaiser/Intern-Atlas huggingface dataset:
Collaborative personalized tweet recommendation
Authors: Kailong Chen, Tianqi Chen, Guoqing Zheng, Ou Jin, Enpeng Yao, Yong Yu
Venue: Conference SIGIR 2012
Year: 2012

Twitter has rapidly grown to a popular social network in recent years and provides a large number of real-time messages for users. Tweets are presented in chronological order and users scan the followees' timelines to find what they are interested in. However, an information overload problem has troubled many users, especially those with many followees and thousands of tweets arriving every day. In this paper, we focus on recommending useful tweets that users are really interested in personally to reduce the users' effort to find useful information. Many kinds of information on Twitter are available for helping recommendation, including the user's own tweet history, retweet history and social relations between users. We propose a method of making tweet recommendations based on collaborative ranking to capture personal interests. It can also conveniently integrate the other useful contextual information. Our final method considers three major elements on Twitter: tweet topic level factors, user social relation factors and explicit features such as authority of the publisher and quality of the tweet. The experiments show that all the proposed elements are important and our method greatly outperforms several baseline methods.
TFMAP: Optimizing map for top-n context-aware recommendation
Authors: Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha A. Larson, Alan Hanjalic, Nuria Oliver
Venue: Conference SIGIR 2012
Year: 2012

In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filtering (CF). Much of the past work on CF has not focused on evaluation metrics that lead to good top-N recommendation lists in designing recommendation models. In addition, previous work on context-aware recommendation has mainly focused on explicit feedback data, i.e., ratings. We propose TFMAP, a model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context. TFMAP uses tensor factorization to model implicit feedback data (e.g., purchases, clicks) with contextual information.
Sparsity induced similarity measure for label propagation
Authors: Hong Cheng 0002, Zicheng Liu 0001, Jie Yang 0001
Venue: Conference ICCV 2009
Year: 2009

Graph-based semi-supervised learning has gained considerable interests in the past several years thanks to its effectiveness in combining labeled and unlabeled data through label propagation for better object modeling and classification. A critical issue in constructing a graph is the weight assignment where the weight of an edge specifies the similarity between two data points. In this paper, we present a novel technique to measure the similarities among data points by decomposing each data point as an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> sparse linear combination of the rest of the data points. The main idea is that the coefficients in such a sparse decomposition reflect the point's neighborhood structure thus providing better similarity measures among the decomposed data point and the rest of the data points. The proposed approach is evaluated on four commonly-used data sets and the experimental results show that the proposed Sparsity Induced Similarity (SIS) measure significantly improves label propagation performance. As an application of the SIS-based label propagation, we show that the SIS measure can be used to improve the Bag-of-Words approach for scene classification.
Know Your Neighbors: Web Spam Detection using the Web Topology
Authors: Carlos Castillo, Debora Donato, Aristides Gionis, Vanessa Murdock, Fabrizio Silvestri
Venue: Conference SIGIR 2007
Year: 2007

Web spam can significantly deteriorate the quality of search engine results. Thus there is a large incentive for commercial search engines to detect spam pages efficiently and accurately. In this paper we present a spam detection system that combines link-based and content-based features, and uses the topology of the Web graph by exploiting the link dependencies among the Web pages. We find that linked hosts tend to belong to the same class: either both are spam or both are non-spam. We demonstrate three methods of incorporating the Web graph topology into the predictions obtained by our base classifier: (i) clustering the host graph, and assigning the label of all hosts in the cluster by majority vote, (ii) propagating the predicted labels to neighboring hosts, and (iii) using the predicted labels of neighboring hosts as new features and retraining the classifier. The result is an accurate system for detecting Web spam, tested on a large and public dataset, using algorithms that can be applied in practice to large-scale Web data.
Vote calibration in community question-answering systems
Authors: Bee-Chung Chen, Anirban Dasgupta, Xuanhui Wang, Jie Yang
Venue: Conference SIGIR 2012
Year: 2012

User votes are important signals in community question-answering (CQA) systems. Many features of typical CQA systems, e.g. the best answer to a question, status of a user, are dependent on ratings or votes cast by the community. In a popular CQA site, Yahoo! Answers, users vote for the best answers to their questions and can also thumb up or down each individual answer. Prior work has shown that these votes provide useful predictors for content quality and user expertise, where each vote is usually assumed to carry the same weight as others. In this paper, we analyze a set of possible factors that indicate bias in user voting behavior -- these factors encompass different gaming behavior, as well as other eccentricities, e.g., votes to show appreciation of answerers. These observations suggest that votes need to be calibrated before being used to identify good answers or experts. To address this problem, we propose a general machine learning framework to calibrate such votes. Through extensive experiments based on an editorially judged CQA dataset, we show that our supervised learning method of content-agnostic vote calibration can significantly improve the performance of answer ranking and expert ranking.
A User Browsing Model to Predict Search Engine Click Data from Past Observations
Authors: Georges Dupret, Benjamin Piwowarski
Venue: Conference SIGIR 2008
Year: 2008

Search engine click logs provide an invaluable source of relevance information but this information is biased because we ignore which documents from the result list the users have actually seen before and after they clicked. Otherwise, we could estimate document relevance by simple counting. In this paper, we propose a set of assumptions on user browsing behavior that allows the estimation of the probability that a document is seen, thereby providing an unbiased estimate of document relevance. To train, test and compare our model to the best alternatives described in the Literature, we gather a large set of real data and proceed to an extensive cross-validation experiment. Our solution outperforms very significantly all previous models. As a side effect, we gain insight into the browsing behavior of users and we can compare it to the conclusions of an eye-tracking experiments by Joachims et al. [12]. In particular, our findings confirm that a user almost always see the document directly after a clicked document. They also explain why documents situated just after a very relevant document are clicked more often.
Predicting web searcher satisfaction with existing community-based answers
Authors: Qiaoling Liu, Eugene Agichtein, Gideon Dror, Evgeniy Gabrilovich, Yoelle Maarek, Dan Pelleg, Idan Szpektor
Venue: Conference SIGIR 2011
Year: 2011

Community-based Question Answering (CQA) sites, such as Yahoo! Answers, Baidu Knows, Naver, and Quora, have been rapidly growing in popularity. The resulting archives of posted answers to questions, in Yahoo! Answers alone, already exceed in size 1 billion, and are aggressively indexed by web search engines. In fact, a large number of search engine users benefit from these archives, by finding existing answers that address their own queries. This scenario poses new challenges and opportunities for both search engines and CQA sites. To this end, we formulate a new problem of predicting the satisfaction of web searchers with CQA answers. We analyze a large number of web searches that result in a visit to a popular CQA site, and identify unique characteristics of searcher satisfaction in this setting, namely, the effects of query clarity, query-to-question match, and answer quality. We then propose and evaluate several approaches to predicting searcher satisfaction that exploit these characteristics. To the best of our knowledge, this is the first attempt to predict and validate the usefulness of CQA archives for external searchers, rather than for the original askers. Our results suggest promising directions for improving and exploiting community question answering services in pursuit of satisfying even more Web search queries.
Evaluating and predicting answer quality in community QA
Authors: Chirag Shah, Jeffrey Pomerantz
Venue: Conference SIGIR 2010
Year: 2010

Question answering (QA) helps one go beyond traditional keywords-based querying and retrieve information in more precise form than given by a document or a list of documents. Several community-based QA (CQA) services have emerged allowing information seekers pose their information need as questions and receive answers from their fellow users. A question may receive multiple answers from multiple users and the asker or the community can choose the best answer. While the asker can thus indicate if he was satisfied with the information he received, there is no clear way of evaluating the quality of that information. We present a study to evaluate and predict the quality of an answer in a CQA setting. We chose Yahoo! Answers as such CQA service and selected a small set of questions, each with at least five answers. We asked Amazon Mechanical Turk workers to rate the quality of each answer for a given question based on 13 different criteria. Each answer was rated by five different workers. We then matched their assessments with the actual asker’s rating of a given answer. We show that the quality criteria we used faithfully match with asker’s perception of a quality answer. We furthered our investigation by extracting various features from questions, answers, and the users who posted them, and training a number of classifiers to select the best answer using those features. We demonstrate a high predictability of our trained models along with the relative merits of each of the features for such prediction. These models support our argument that in case of CQA, contextual information such as a user’s profile, can be critical in evaluating and predicting content quality.
Quality-aware collaborative question answering: methods and evaluation
Authors: Maggy Anastasia Suryanto, Ee-Peng Lim, Aixin Sun, Roger H. L. Chiang
Venue: Conference WSDM 2009
Year: 2009

Community Question Answering (QA) portals contain questions and answers contributed by hundreds of millions of users. These databases of questions and answers are of great value if they can be used directly to answer questions from any user. In this research, we address this collaborative QA task by drawing knowledge from the crowds in community QA portals such as Yahoo! Answers. Despite their popularity, it is well known that answers in community QA portals have unequal quality. We therefore propose a quality-aware framework to design methods that select answers from a community QA portal considering answer quality in addition to answer relevance. Besides using answer features for determining answer quality, we introduce several other quality-aware QA methods using answer quality derived from the expertise of answerers. Such expertise can be question independent or question dependent. We evaluate our proposed methods using a database of 95K questions and 537K answers obtained from Yahoo! Answers. Our experiments have shown that answer quality can improve QA performance significantly. Furthermore, question dependent expertise based methods are shown to outperform methods using answer features only. It is also found that there are also good answers not among the best answers identified by Yahoo! Answers users.
Ranking community answers by modeling question-answer relationships via analogical reasoning
Authors: Xin-Jing Wang, Xudong Tu, Dan Feng, Lei Zhang
Venue: Conference SIGIR 2009
Year: 2009

The method of finding high-quality answers has significant impact on user satisfaction in community question answering systems. However, due to the lexical gap between questions and answers as well as spam typically existing in usergenerated content, filtering and ranking answers is very challenging. Previous solutions mainly focus on generating redundant features, or finding textual clues using machine learning techniques; none of them ever consider questions and their answers as relational data but instead model them as independent information. Moreover, they only consider the answers of the current question, and ignore any previous knowledge that would be helpful to bridge the lexical and semantic gap. We assume that answers are connected to their questions with various types of latent links, i.e. positive links indicating high-quality answers, negative links indicating incorrect answers or user-generated spam, and propose an analogical reasoning-based approach which measures the analogy between the new question-answer linkages and those of previous relevant knowledge which contains only positive links; the candidate answer which has the most analogous link is assumed to be the best answer. We conducted experiments based on 29.8 million Yahoo!Answer question-answer threads and showed the effectiveness of our approach.
Improving search relevance for short queries in community question answering
Authors: Haocheng Wu, Wei Wu, Ming Zhou, Enhong Chen, Lei Duan, Heung-Yeung Shum
Venue: Conference WSDM 2014
Year: 2014

Relevant question retrieval and ranking is a typical task in community question answering (CQA). Existing methods mainly focus on long and syntactically structured queries. However, when an input query is short, the task becomes challenging, due to a lack information regarding user intent. In this paper, we mine different types of user intent from various sources for short queries. With these intent signals, we propose a new intent-based language model. The model takes advantage of both state-of-the-art relevance models and the extra intent information mined from multiple sources. We further employ a state-of-the-art learning-to-rank approach to estimate parameters in the model from training data. Experiments show that by leveraging user intent prediction, our model significantly outperforms the state-of-the-art relevance models in question search.
Incorporating user preferences into click models
Authors: Qianli Xing, Yiqun Liu, Jian-Yun Nie, Min Zhang, Shaoping Ma, Kuo Zhang
Venue: Conference CIKM 2013
Year: 2013

Click models are developed to interpret clicks by making assumptions on how users browse the search result page. Most existing click models implicitly assume that all users are homogeneous and act in the same way when browsing the search results. However, a number of researches have shown that users have diverse behavioral patterns, which is also observed in this paper by eye-tracking experiments and click-through log analysis. As a uniform click model for all users can hardly capture the diverse click behavior, in this paper we incorporate user preferences into both a variety of existing click models and a novel click model. The experimental results on a large-scale click-through data set show consistent and significant performance improvement of the click models with user preferences integrated.
Predicting long-term impact of cqa posts: A comprehensive viewpoint
Authors: Yuan Yao, Hanghang Tong, Feng Xu, Jian Lu
Venue: Conference KDD 2014
Year: 2014

Community Question Answering (CQA) sites have become valuable platforms to create, share, and seek a massive volume of human knowledge. How can we spot an insightful question that would inspire massive further discussions in CQA sites? How can we detect a valuable answer that benefits many users? The long-term impact (e.g., the size of the population a post benefits) of a question/answer post is the key quantity to answer these questions. In this paper, we aim to predict the long-term impact of questions/answers shortly after they are posted in the CQA sites. In particular, we propose a family of algorithms for the prediction problem by modeling three key aspects, i.e., non-linearity, question/answer coupling, and dynamics. We analyze our algorithms in terms of optimality, correctness, and complexity. We conduct extensive experimental evaluations on two real CQA data sets to demonstrate the effectiveness and efficiency of our algorithms.
A low rank structural large margin method for cross-modal ranking
Authors: Xinyan Lu, Fei Wu, Siliang Tang, Zhongfei Zhang, Xiaofei He, Yueting Zhuang
Venue: Conference SIGIR 2013
Year: 2013

Cross-modal retrieval is a classic research topic in multimedia information retrieval. The traditional approaches study the problem as a pairwise similarity function problem. In this paper, we consider this problem from a new perspective as a listwise ranking problem and propose a general cross-modal ranking algorithm to optimize the listwise ranking loss with a low rank embedding, which we call Latent Semantic Cross-Modal Ranking (LSCMR). The latent low-rank embedding space is discriminatively learned by structural large margin learning to optimize for certain ranking criteria directly. We evaluate LSCMR on the Wikipedia and NUS-WIDE dataset. Experimental results show that this method obtains significant improvements over the state-of-the-art methods.
Collective prediction with latent graphs
Authors: Xiaoxiao Shi, Yao Li, Philip S. Yu
Venue: Conference CIKM 2011
Year: 2011

Collective classification in relational data has become an important and active research topic in the last decade. It exploits the dependencies of instances in a network to improve predictions. Related applications include hyperlinked document classification, social network analysis and collaboration network analysis. Most of the traditional collective classification models mainly study the scenario that there exists a large amount of labeled examples (labeled nodes). However, in many real-world applications, labeled data are extremely difficult to obtain. For example, in network intrusion detection, there may be only a limited number of identified intrusions whereas there are a huge set of unlabeled nodes. In this situation, most of the data have no connection to labeled nodes; hence, no supervision knowledge can be obtained from the local connections. In this paper, we propose to explore various latent linkages among the nodes and judiciously integrate the linkages to generate a latent graph. This is achieved by finding a graph that maximizes the linkages among the training data with the same label, and maximizes the separation among the data with different labels. The objective is further cast into an optimization problem and is solved with quadratic programming. Finally, we apply label propagation on the latent graph to make prediction. Experiments show that the proposed model LNP (Latent Network Propagation) can improve the learning accuracy significantly. For instance, when there are only 10% of labeled examples, the accuracies of all the comparison models are less than 63%, while that of the proposed model is 74%.
Group latent factor model for recommendation with multiple user behaviors
Authors: Jian Cheng, Ting Yuan, Jinqiao Wang, Hanqing Lu
Venue: Conference SIGIR 2014
Year: 2014

Recently, some recommendation methods try to relieve the data sparsity problem of Collaborative Filtering by exploiting data from users' multiple types of behaviors. However, most of the exist methods mainly consider to model the correlation between different behaviors and ignore the heterogeneity of them, which may make improper information transferred and harm the recommendation results. To address this problem, we propose a novel recommendation model, named Group Latent Factor Model (GLFM), which attempts to learn a factorization of latent factor space into subspaces that are shared across multiple behaviors and subspaces that are specific to each type of behaviors. Thus, the correlation and heterogeneity of multiple behaviors can be modeled by these shared and specific latent factors. Experiments on the real-world dataset demonstrate that our model can integrate users' multiple types of behaviors into recommendation better.
Clr: a collaborative location recommendation framework based on co-clustering
Authors: Kenneth Wai-Ting Leung, Dik Lun Lee, Wang-Chien Lee
Venue: Conference SIGIR 2011
Year: 2011

GPS data tracked on mobile devices contains rich information about human activities and preferences. In this paper, GPS data is used in location-based services (LBSs) to provide collaborative location recommendations. We observe that most existing LBSs provide location recommendations by clustering the User-Location matrix. Since the User-Location matrix created based on GPS data is huge, there are two major problems with these methods. First, the number of similar locations that need to be considered in computing the recommendations can be numerous. As a result, the identification of truly relevant locations from numerous candidates is challenging. Second, the clustering process on large matrix is time consuming. Thus, when new GPS data arrives, complete re-clustering of the whole matrix is infeasible. To tackle these two problems, we propose the Collaborative Location Recommendation (CLR) framework for location recommendation. By considering activities (i.e., temporal preferences) and different user classes (i.e., Pattern Users, Normal Users, and Travelers) in the recommendation process, CLR is capable of generating more precise and refined recommendations to the users compared to the existing methods. Moreover, CLR employs a dynamic clustering algorithm CADC to cluster the trajectory data into groups of similar users, similar activities and similar locations efficiently by supporting incremental update of the groups when new GPS trajectory data arrives. We evaluate CLR with a real-world GPS dataset, and confirm that the CLR framework provides more accurate location recommendations compared to the existing methods.
Prototype hierarchy based clustering for the categorization and navigation of web collections
Authors: Zhaoyan Ming, Kai Wang, Tat-Seng Chua
Venue: Conference SIGIR 2010
Year: 2010

This paper presents a novel prototype hierarchy based clustering (PHC) framework for the organization of web collections. It solves simultaneously the problem of categorizing web collections and interpreting the clustering results for navigation. By utilizing prototype hierarchies and the underlying topic structures of the collections, PHC is modeled as a multi-criterion optimization problem based on minimizing the hierarchy evolution, maximizing category cohesiveness and inter-hierarchy structural and semantic resemblance. The flexible design of metrics enables PHC to be a general framework for applications in various domains. In the experiments on categorizing 4 collections of distinct domains, PHC achieves 30% improvement in ¼F1 over the state-of-the-art techniques. Further experiments provide insights on performance variations with abstract and concrete domains, completeness of the prototype hierarchy, and effects of different combinations of optimization criteria.