OpenRaiser/Intern-Atlas huggingface dataset:
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
Authors: Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
Venue: Conference SIGIR 2006
Year: 2006

Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large number of ratings from similar users or similar items are not available, due to the sparsity inherent to rating data. Consequently, prediction quality can be poor. This paper re-formulates the memory-based collaborative filtering problem in a generative probabilistic framework, treating individual user-item ratings as predictors of missing ratings. The final rating is estimated by fusing predictions from three sources: predictions based on ratings of the same item by other users, predictions based on different item ratings made by the same user, and, third, ratings predicted based on data from other but similar users rating other but similar items. Existing user-based and item-based approaches correspond to the two simple cases of our framework. The complete model is however more robust to data sparsity, because the different types of ratings are used in concert, while additional ratings from similar users towards similar items are employed as a background model to smooth the predictions. Experiments demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendations.
Ego-splitting framework: from non-overlapping to overlapping clusters
Authors: Alessandro Epasto, Silvio Lattanzi, Renato Paes Leme
Venue: Conference KDD 2017
Year: 2017

We propose ego-splitting, a new framework for detecting clusters in complex networks which leverage the local structures known as ego-nets (i.e. the subgraph induced by the neighborhood of each node) to de-couple overlapping clusters. Ego-splitting is a highly scalable and flexible framework, with provable theoretical guarantees, that reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (partitioning) problem. We can scale community detection to graphs with tens of billions of edges and outperform previous solutions based on ego-nets analysis.
Exact Age Prediction in Social Networks
Authors: Bryan Perozzi, Steven Skiena
Venue: Conference WWW 2015
Year: 2015

Predicting accurate demographic information about the users of information systems is a problem of interest in personalized search, ad targeting, and other related fields. Despite such broad applications, most existing work only considers age prediction as one of classification, typically into only a few broad categories.
A structured response to misinformation: Defining and annotating credibility indicators in news articles
Authors: Amy X. Zhang, Aditya Ranganathan, Sarah Emlen Metz, Scott Appling, Connie Moon Sehat, Norman Gilmore, Nick B. Adams, Emmanuel Vincent, Jennifer Lee, Martin Robbins, Ed Bice, Sandro Hawke, David R. Karger, An Xiao Mina
Venue: Conference WWW 2018
Year: 2018

The proliferation of misinformation in online news and its amplification by platforms are a growing concern, leading to numerous efforts to improve the detection of and response to misinformation. Given the variety of approaches, collective agreement on the indicators that signify credible content could allow for greater collaboration and data-sharing across initiatives. In this paper, we present an initial set of indicators for article credibility defined by a diverse coalition of experts. These indicators originate from both within an article's text as well as from external sources or article metadata. As a proof-of-concept, we present a dataset of 40 articles of varying credibility annotated with our indicators by 6 trained annotators using specialized platforms. We discuss future steps including expanding annotation, broadening the set of indicators, and considering their use by platforms and the public, towards the development of interoperable standards for content credibility.
The Size Conundrum: Why Online Knowledge Markets Can Fail at Scale
Authors: Himel Dev, Chase Geigle, Qingtao Hu, Jiahui Zheng, Hari Sundaram
Venue: Conference WWW 2018
Year: 2018

In this paper, we interpret the community question answering websites on the StackExchange platform as knowledge markets, and analyze how and why these markets can fail at scale. A knowledge market framing allows site operators to reason about market failures, and to design policies to prevent them. Our goal is to provide insights on large-scale knowledge market failures through an interpretable model. We explore a set of interpretable economic production models on a large empirical dataset to analyze the dynamics of content generation in knowledge markets. Amongst these, the Cobb-Douglas model best explains empirical data and provides an intuitive explanation for content generation through concepts of elasticity and diminishing returns. Content generation depends on user participation and also on how specific types of content (e.g. answers) depends on other types (e.g. questions). We show that these factors of content generation have constant elasticity---a percentage increase in any of the inputs leads to a constant percentage increase in the output. Furthermore, markets exhibit diminishing returns---the marginal output decreases as the input is incrementally increased. Knowledge markets also vary on their returns to scale---the increase in output resulting from a proportionate increase in all inputs. Importantly, many knowledge markets exhibit diseconomies of scale---measures of market health (e.g., the percentage of questions with an accepted answer) decrease as a function of number of participants. The implications of our work are two-fold: site operators ought to design incentives as a function of system size (number of participants); the market lens should shed insight into complex dependencies amongst different content types and participant actions in general social networks.
Uncovering the Dynamics of Crowdlearning and the Value of Knowledge
Authors: Utkarsh Upadhyay, Isabel Valera, Manuel Gomez-Rodriguez
Venue: Conference WSDM 2017
Year: 2017

Learning from the crowd has become increasingly popular in the Web and social media. There is a wide variety of crowdlearning sites in which, on the one hand, users learn from the knowledge that other users contribute to the site, and, on the other hand, knowledge is reviewed and curated by the same users using assessment measures such as upvotes or likes. In this paper, we present a probabilistic modeling framework of crowdlearning, which uncovers the evolution of a user's expertise over time by leveraging other users' assessments of her contributions. The model allows for both off-site and on-site learning and captures forgetting of knowledge. We then develop a scalable estimation method to fit the model parameters from millions of recorded learning and contributing events. We show the effectiveness of our model by tracing activity of ~25 thousand users in Stack Overflow over a 4.5 year period. We find that answers with high knowledge value are rare. Newbies and experts tend to acquire less knowledge than users in the middle range. Prolific learners tend to be also proficient contributors that post answers with high knowledge value.
Long Short Memory Process: Modeling Growth Dynamics of Microscopic Social Connectivity
Authors: Chengxi Zang, Peng Cui, Christos Faloutsos, Wenwu Zhu
Venue: Conference KDD 2017
Year: 2017

How do people make friends dynamically in social networks? What are the temporal patterns for an individual increasing its social connectivity? What are the basic mechanisms governing the formation of these temporal patterns? No matter cyber or physical social systems, their structure and dynamics are mainly driven by the connectivity dynamics of each individual. However, due to the lack of empirical data, little is known about the empirical dynamic patterns of social connectivity at microscopic level, let alone the regularities or models governing these microscopic dynamics.
Large scale multi-label classification via metalabeler
Authors: Lei Tang, Suju Rajan, Vijay K. Narayanan
Venue: Conference WWW 2009
Year: 2009

The explosion of online content has made the management of such content non-trivial. Web-related tasks such as web page categorization, news filtering, query categorization, tag recommendation, etc. often involve the construction of multi-label categorization systems on a large scale. Existing multi-label classification methods either do not scale or have unsatisfactory performance. In this work, we propose MetaLabeler to automatically determine the relevant set of labels for each instance without intensive human involvement or expensive cross-validation. Extensive experiments conducted on benchmark data show that the MetaLabeler tends to outperform existing methods. Moreover, MetaLabeler scales to millions of multi-labeled instances and can be deployed easily. This enables us to apply the MetaLabeler to a large scale query categorization problem in Yahoo!, yielding a significant improvement in performance.
gSparsify: Graph Motif Based Sparsification for Graph Clustering
Authors: Peixiang Zhao
Venue: Conference CIKM 2015
Year: 2015

Graph clustering is a fundamental problem that partitions vertices of a graph into clusters with an objective to optimize the intuitive notions of intra-cluster density and intercluster sparsity. In many real-world applications, however, the sheer sizes and inherent complexity of graphs may render existing graph clustering methods inefficient or incapable of yielding quality graph clusters. In this paper, we propose gSparsify, a graph sparsification method, to preferentially retain a small subset of edges from a graph which are more likely to be within clusters, while eliminating others with less or no structure correlation to clusters. The resultant simplified graph is succinct in size with core cluster structures well preserved, thus enabling faster graph clustering without a compromise to clustering quality. We consider a quantitative approach to modeling the evidence that edges within densely knitted clusters are frequently involved in small-size graph motifs, which are adopted as prime features to differentiate edges with varied cluster significance. Path-based indexes and path-join algorithms are further designed to compute graph-motif based cluster significance of edges for graph sparsification. We perform experimental studies in real-world graphs, and results demonstrate that gSparsify can bring significant speedup to existing graph clustering methods with an improvement to graph clustering quality.
Your browsing behavior for a big mac: Economics of personal information online
Authors: Juan Pablo Carrascal, Christopher J. Riederer, Vijay Erramilli, Mauro Cherubini, Rodrigo de Oliveira
Venue: Conference WWW 2013
Year: 2013

Most online service providers offer free services to users and in part, these services collect and monetize personally identifiable information (PII), primarily via targeted advertisements. Against this backdrop of economic exploitation of PII, it is vital to understand the value that users put to their own PII. Although studies have tried to discover how users value their privacy, little is known about how users value their PII while browsing, or the exploitation of their PII. Extracting valuations of PII from users is non-trivial - surveys cannot be relied on as they do not gather information of the context where PII is being released, thus reducing validity of answers. In this work, we rely on refined Experience Sampling - a data collection method that probes users to valuate their PII at the time and place where it was generated in order to minimize retrospective recall and hence increase measurement validity. For obtaining an honest valuation of PII, we use a reverse second price auction. We developed a web browser plugin and had 168 users - living in Spain - install and use this plugin for 2 weeks in order to extract valuations of PII in different contexts. We found that users value items of their online browsing history for about ∈7 (~10USD), and they give higher valuations to their offline PII, such as age and address (about 25∈ or ~36USD). When it comes to PII shared in specific online services, users value information pertaining to financial transactions and social network interactions more than activities like search and shopping. No significant distinction was found between valuations of different quantities of PII (e.g. one vs. 10 search keywords), but deviation was found between types of PII (e.g. photos vs. keywords). Finally, the users' preferred goods for exchanging their PII included money and improvements in service, followed by getting more free services and targeted advertisements.
Inferring individual attributes from search engine queries and auxiliary information
Authors: Luca Soldaini, Elad Yom-Tov
Venue: Conference WWW 2017
Year: 2017

Internet data has surfaced as a primary source for investigation of different aspects of human behavior. A crucial step in such studies is finding a suitable cohort (i.e., a set of users) that shares a common trait of interest to researchers. However, direct identification of users sharing this trait is often impossible, as the data available to researchers is usually anonymized to preserve user privacy. To facilitate research on specific topics of interest, especially in medicine, we introduce an algorithm for identifying a trait of interest in anonymous users. We illustrate how a small set of labeled examples, together with statistical information about the entire population, can be aggregated to obtain labels on unseen examples. We validate our approach using labeled data from the political domain. We provide two applications of the proposed algorithm to the medical domain. In the first, we demonstrate how to identify users whose search patterns indicate they might be suffering from certain types of cancer. This shows, for the first time, that search queries can be used as a screening device for diseases that are currently often discovered too late, because no early screening tests exists. In the second, we detail an algorithm to predict the distribution of diseases given their incidence in a subset of the population at study, making it possible to predict disease spread from partial epidemiological data.
Item Group Based Pairwise Preference Learning for Personalized Ranking
Authors: Shuang Qiu, Jian Cheng, Ting Yuan, Cong Leng, Hanqing Lu
Venue: Conference SIGIR 2014
Year: 2014

Collaborative filtering with implicit feedbacks has been steadily receiving more attention, since the abundant implicit feedbacks are more easily collected while explicit feedbacks are not necessarily always available. Several recent work address this problem well utilizing pairwise ranking method with a fundamental assumption that a user prefers items with positive feedbacks to the items without observed feedbacks, which also implies that the items without observed feedbacks are treated equally without distinction. However, users have their own preference on different items with different degrees which can be modeled into a ranking relationship. In this paper, we exploit this prior information of a user's preference from the nearest neighbor set by the neighbors' implicit feedbacks, which can split items into different item groups with specific ranking relations. We propose a novel PRIGP(Personalized Ranking with Item Group based Pairwise preference learning) algorithm to integrate item based pairwise preference and item group based pairwise preference into the same framework. Experimental results on three real-world datasets demonstrate the proposed method outperforms the competitive baselines on several ranking-oriented evaluation metrics.
Anne: Improving source code search using entity retrieval approach
Authors: Venkatesh Vinayakarao, Anita Sarma, Rahul Purandare, Shuktika Jain, Saumya Jain
Venue: Conference WSDM 2017
Year: 2017

Code search with natural language terms performs poorly because programming concepts do not always lexically match their syntactic forms. For example, in Java, the programming concept "array" does not match with its syntactic representation of "[ ]". Code search engines can assist developers more effectively over natural language queries if such mappings existed for a variety of programming languages. In this work, we present a programming language agnostic technique to discover such mappings between syntactic forms and natural language terms representing programming concepts. We use the questions and answers in Stack Overflow to create this mapping. We implement our approach in a tool called ANNE. To evaluate its effectiveness, we conduct a user study in an academic setting in which teaching assistants use ANNE to search for code snippets in student submissions. With the use of ANNE, we find that the participants are 29% quicker with no significant drop in correctness and completeness.
Collaboratively improving topic discovery and word embeddings by coordinating global and local contexts
Authors: Guangxu Xun, Yaliang Li, Jing Gao, Aidong Zhang
Venue: Conference KDD 2017
Year: 2017

A text corpus typically contains two types of context information -- global context and local context. Global context carries topical information which can be utilized by topic models to discover topic structures from the text corpus, while local context can train word embeddings to capture semantic regularities reflected in the text corpus. This encourages us to exploit the useful information in both the global and the local context information. In this paper, we propose a unified language model based on matrix factorization techniques which 1) takes the complementary global and local context information into consideration simultaneously, and 2) models topics and learns word embeddings collaboratively. We empirically show that by incorporating both global and local context, this collaborative model can not only significantly improve the performance of topic discovery over the baseline topic models, but also learn better word embeddings than the baseline word embedding models. We also provide qualitative analysis that explains how the cooperation of global and local context information can result in better topic structures and word embeddings.
Location and time aware social collaborative retrieval for new successive point-of-interest recommendation
Authors: Wei Zhang, Jianyong Wang
Venue: Conference CIKM 2015
Year: 2015

In location-based social networks (LBSNs), new successive point-of-interest (POI) recommendation is a newly formulated task which tries to regard the POI a user currently visits as his POI-related query and recommend new POIs the user has not visited before. While carefully designed methods are proposed to solve this problem, they ignore the essence of the task which involves retrieval and recommendation problem simultaneously and fail to employ the social relations or temporal information adequately to improve the results.
The tube over time: Characterizing popularity growth of youtube videos
Authors: Flavio Figueiredo, Fabrício Benevenuto, Jussara M. Almeida
Venue: Conference WSDM 2011
Year: 2011

Understanding content popularity growth is of great importance to Internet service providers, content creators and online marketers. In this work, we characterize the growth patterns of video popularity on the currently most popular video sharing application, namely YouTube. Using newly provided data by the application, we analyze how the popularity of individual videos evolves since the video's upload time. Moreover, addressing a key aspect that has been mostly overlooked by previous work, we characterize the types of the referrers that most often attracted users to each video, aiming at shedding some light into the mechanisms (e.g., searching or external linking) that often drive users towards a video, and thus contribute to popularity growth. Our analyses are performed separately for three video datasets, namely, videos that appear in the YouTube top lists, videos removed from the system due to copyright violation, and videos selected according to random queries submitted to YouTube's search engine. Our results show that popularity growth patterns depend on the video dataset. In particular, copyright protected videos tend to get most of their views much earlier in their lifetimes, often exhibiting a popularity growth characterized by a viral epidemic-like propagation process. In contrast, videos in the top lists tend to experience sudden significant bursts of popularity. We also show that not only search but also other YouTube internal mechanisms play important roles to attract users to videos in all three datasets.
Efficient multi-label classification with hypergraph regularization
Authors: Gang Chen 0003, Jianwen Zhang, Fei Wang 0001, Changshui Zhang, Yuli Gao
Venue: Conference CVPR 2009
Year: 2009

Many computer vision applications, such as image classification and video indexing, are usually multi-label classification problems in which an instance can be assigned to more than one category. In this paper, we present a novel multi-label classification approach with hypergraph regularization that addresses the correlations among different categories. First, a hypergraph is constructed to capture the correlations among different categories, in which each vertex represents one training instance and each hyperedge for one category contains all the instances belonging to the same category. Then, an improved SVM like learning system incorporating the hypergraph regularization, called Rank-HLapSVM, is proposed to handle the multi-label classification problems. We find that the corresponding optimization problem can be efficiently solved by the dual coordinate descent method. Many promising experimental results on the real datasets including ImageCLEF and MediaMill demonstrate the effectiveness and efficiency of the proposed algorithm.
Hypergraph partitioning for document clustering: A unified clique perspective
Authors: Tianming Hu, Hui Xiong, Wenjun Zhou, Sam Yuan Sung, Hangzai Luo
Venue: Conference SIGIR 2008
Year: 2008

Hypergraph partitioning has been considered as a promising method to address the challenges of high dimensionality in document clustering. With documents modeled as vertices and the relationship among documents captured by the hyperedges, the goal of graph partitioning is to minimize the edge cut. Therefore, the definition of hyperedges is vital to the clustering performance. While several definitions of hyperedges have been proposed, a systematic understanding of desired characteristics of hyperedges is still missing. To that end, in this paper, we first provide a unified clique perspective of the definition of hyperedges, which serves as a guide to define hyperedges. With this perspective, based on the concepts of hypercliques and shared (reverse) nearest neighbors, we propose three new types of clique hyperedges and analyze their properties regarding purity and size issues. Finally, we present an extensive evaluation using real-world document datasets. The experimental results show that, with shared (reverse) nearest neighbor based hyperedges, the clustering performance can be improved significantly in terms of various external validation measures without the need for fine tuning of parameters.