OpenRaiser/Intern-Atlas huggingface dataset:
Event detection in activity networks
Authors: Polina Rozenshtein, Aris Anagnostopoulos, Aristides Gionis, Nikolaj Tatti
Venue: Conference KDD 2014
Year: 2014

With the fast growth of smart devices and social networks, a lot of computing systems collect data that record different types of activities. An important computational challenge is to analyze these data, extract patterns, and understand activity trends. We consider the problem of mining activity networks to identify interesting events, such as a big concert or a demonstration in a city, or a trending keyword in a user community in a social network. We define an event to be a subset of nodes in the network that are close to each other and have high activity levels. We formalize the problem of event detection using two graph-theoretic formulations. The first one captures the compactness of an event using the sum of distances among all pairs of the event nodes. We show that this formulation can be mapped to the maxcut problem, and thus, it can be solved by applying standard semidefinite programming techniques. The second formulation captures compactness using a minimum-distance tree. This formulation leads to the prize-collecting Steiner-tree problem, which we solve by adapting existing approximation algorithms. For the two problems we introduce, we also propose efficient and effective greedy approaches and we prove performance guarantees for one of them. We experiment with the proposed algorithms on real datasets from a public bicycling system and a geolocation-enabled social network dataset collected from twitter. The results show that our methods are able to detect meaningful events.
Scalable distributed inference of dynamic user interests for behavioral targeting
Authors: Amr Ahmed, Yucheng Low, Mohamed Aly, Vanja Josifovski, Alexander J. Smola
Venue: Conference KDD 2011
Year: 2011

Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current.
A collective bayesian poisson factorization model for cold-start local event recommendation
Authors: Wei Zhang, Jianyong Wang
Venue: Conference KDD 2015
Year: 2015

Event-based social networks (EBSNs), in which organizers publish events to attract other users in local city to attend offline, emerge in recent years and grow rapidly. Due to the large volume of events in EBSNs, event recommendation is essential. A few recent works focus on this task, while almost all the methods need that each event to be recommended should have been registered by some users to attend. Thus they ignore two essential characteristics of events in EBSNs: (1) a large number of new events will be published every day which means many events have few participants in the beginning, (2) events have life cycles which means outdated events should not be recommended. Overall, event recommendation in EBSNs inevitably faces the cold-start problem.
Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining
Authors: Sara Hajian, Francesco Bonchi, Carlos Castillo
Venue: Conference KDD 2016
Year: 2016

Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily lives lives (offline and online), as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds such as gender, ethnicity, or health status. It turns out that the answer is positive: for instance, recent studies in the context of online advertising show that ads for high-income jobs are presented to men much more often than to women [Datta et al., 2015]; and ads for arrest records are significantly more likely to show up on searches for distinctively black names [Sweeney, 2013]. This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. These considerations call for the development of data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community.
Mooc dropout prediction: Lessons learned from making pipelines interpretable
Authors: Saurabh Nagrecha, John Z. Dillon, Nitesh V. Chawla
Venue: Conference WWW 2017
Year: 2017

Dropout prediction in MOOCs is a well-researched problem where we classify which students are likely to persist or drop out of a course. Most research into creating models which can predict outcomes is based on student engagement data. Why these students might be dropping out has only been studied through retroactive exit surveys. This helps identify an important extension area to dropout prediction- how can we interpret dropout predictions at the student and model level? We demonstrate how existing MOOC dropout prediction pipelines can be made interpretable, all while having predictive performance close to existing techniques. We explore each stage of the pipeline as design components in the context of interpretability. Our end result is a layer which longitudinally interprets both predictions and entire classification models of MOOC dropout to provide researchers with in-depth insights of why a student is likely to dropout.
Who to follow and why: link prediction with explanations
Authors: Nicola Barbieri, Francesco Bonchi, Giuseppe Manco
Venue: Conference KDD 2014
Year: 2014

User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty.
Who will follow you back?: reciprocal relationship prediction
Authors: John E. Hopcroft, Tiancheng Lou, Jie Tang
Venue: Conference CIKM 2011
Year: 2011

We study the extent to which the formation of a two-way relationship can be predicted in a dynamic social network. A two-way (called reciprocal) relationship, usually developed from a one-way (parasocial) relationship, represents a more trustful relationship between people. Understanding the formation of two-way relationships can provide us insights into the micro-level dynamics of the social network, such as what is the underlying community structure and how users influence each other. Employing Twitter as a source for our experimental data, we propose a learning framework to formulate the problem of reciprocal relationship prediction into a graphical model. The framework incorporates social theories into a machine learning model. We demonstrate that it is possible to accurately infer 90% of reciprocal relationships in a dynamic network. Our study provides strong evidence of the existence of the structural balance among reciprocal relationships. In addition, we have some interesting findings, e.g., the likelihood of two "elite" users creating a reciprocal relationships is nearly 8 times higher than the likelihood of two ordinary users. More importantly, our findings have potential implications such as how social structures can be inferred from individuals' behaviors.
Combining latent factor model with location features for event-based group recommendation
Authors: Wei Zhang, Jianyong Wang, Wei Feng
Venue: Conference KDD 2013
Year: 2013

Groups play an essential role in many social websites which promote users' interactions and accelerate the diffusion of information. Recommending groups that users are really interested to join is significant for both users and social media. While traditional group recommendation problem has been extensively studied, we focus on a new type of the problem, i.e., event-based group recommendation. Unlike the other forms of groups, users join this type of groups mainly for participating offline events organized by group members or inviting other users to attend events sponsored by them. These characteristics determine that previously proposed approaches for group recommendation cannot be adapted to the new problem easily as they ignore the geographical influence and other explicit features of groups and users.
Pin-trust: Fast trust propagation exploiting positive, implicit, and negative information
Authors: Min-Hee Jang, Christos Faloutsos, Sang-Wook Kim, U Kang, Jiwoon Ha
Venue: Conference CIKM 2016
Year: 2016

Given "who-trusts/distrusts-whom" information, how can we propagate the trust and distrust? With the appearance of fraudsters in social network sites, the importance of trust prediction has increased. Most such methods use only explicit and implicit trust information (e.g., if Smith likes several of Johnson's reviews, then Smith implicitly trusts Johnson), but they do not consider distrust.
Effective latent models for binary feedback in recommender systems
Authors: Maksims Volkovs, Guangwei Yu
Venue: Conference SIGIR 2015
Year: 2015

In many collaborative filtering (CF) applications, latent approaches are the preferred model choice due to their ability to generate real-time recommendations efficiently. However, the majority of existing latent models are not designed for implicit binary feedback (views, clicks, plays etc.) and perform poorly on data of this type. Developing accurate models from implicit feedback is becoming increasingly important in CF since implicit feedback can often be collected at lower cost and in much larger quantities than explicit preferences. The need for accurate latent models for implicit data was further emphasized by the recently conducted Million Song Dataset Challenge organized by Kaggle. In this challenge, the results for the best latent model were orders of magnitude worse than neighbor-based approaches, and all the top performing teams exclusively used neighbor-based models. We address this problem and propose a new latent approach for binary feedback in CF. In our model, neighborhood similarity information is used to guide latent factorization and derive accurate latent representations. We show that even with simple factorization methods like SVD, our approach outperforms existing models and produces state-of-the-art results.
Exploiting longer cycles for link prediction in signed networks
Authors: Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, Inderjit S. Dhillon
Venue: Conference CIKM 2011
Year: 2011

We consider the problem of link prediction in signed networks. Such networks arise on the web in a variety of ways when users can implicitly or explicitly tag their relationship with other users as positive or negative. The signed links thus created reflect social attitudes of the users towards each other in terms of friendship or trust. Our first contribution is to show how any quantitative measure of social imbalance in a network can be used to derive a link prediction algorithm. Our framework allows us to reinterpret some existing algorithms as well as derive new ones. Second, we extend the approach of Leskovec et al. (2010) by presenting a supervised machine learning based link prediction method that uses features derived from longer cycles in the network. The supervised method outperforms all previous approaches on 3 networks drawn from sources such as Epinions, Slashdot and Wikipedia. The supervised approach easily scales to these networks, the largest of which has 132k nodes and 841k edges. Most real-world networks have an overwhelmingly large proportion of positive edges and it is therefore easy to get a high overall accuracy at the cost of a high false positive rate. We see that our supervised method not only achieves good accuracy for sign prediction but is also especially effective in lowering the false positive rate.
Monocular 3d scene understanding with explicit occlusion reasoning
Authors: Christian Wojek, Stefan Walk, Stefan Roth 0001, Bernt Schiele
Venue: Conference CVPR 2011
Year: 2011

Scene understanding from a monocular, moving camera is a challenging problem with a number of applications including robotics and automotive safety. While recent systems have shown that this is best accomplished with a 3D scene model, handling of partial object occlusion is still unsatisfactory. In this paper we propose an approach that tightly integrates monocular 3D scene tracking-by-detection with explicit object-object occlusion reasoning. Full object and object part detectors are combined in a mixture of experts based on their expected visibility, which is obtained from the 3D scene model. For the difficult case of multi-people tracking, we demonstrate that our approach yields more robust detection and tracking of partially visible pedestrians, even when they are occluded over long periods of time. Our approach is evaluated on two challenging sequences recorded from a moving camera in busy pedestrian zones and outperforms several state-of-the-art approaches.
Immersive recommendation: News and event recommendations using personal digital traces
Authors: Cheng-Kang Hsieh, Longqi Yang, Honghao Wei, Mor Naaman, Deborah Estrin
Venue: Conference WWW 2016
Year: 2016

We propose a new user-centric recommendation model, called Immersive Recommendation, that incorporates cross-platform and diverse personal digital traces into recommendations. Our context-aware topic modeling algorithm systematically profiles users' interests based on their traces from different contexts, and our hybrid recommendation algorithm makes high-quality recommendations by fusing users' personal profiles, item profiles, and existing ratings. Specifically, in this work we target personalized news and local event recommendations for their utility and societal importance. We evaluated the model with a large-scale offline evaluation leveraging users' public Twitter traces. In addition, we conducted a direct evaluation of the model's recommendations in a 33-participant study using Twitter, Facebook and email traces. In the both cases, the proposed model showed significant improvement over the state-of-the-art algorithms, suggesting the value of using this new user-centric recommendation model to improve recommendation quality, including in cold-start situations.
Heteromf: recommendation in heterogeneous information networks using context dependent factor models
Authors: Mohsen Jamali, Laks V. S. Lakshmanan
Venue: Conference WWW 2013
Year: 2013

With the growing amount of information available online, recommender systems are starting to provide a viable alternative and complement to search engines, in helping users to find objects of interest. Methods based on Matrix Factorization (MF) models are the state-of-the-art in recommender systems. The input to MF is user feedback, in the form of a rating matrix. However, users can be engaged in interactions with multiple types of entities across different contexts, leading to multiple rating matrices. In other words, users can have interactions in a heterogeneous information network. Generally, in a heterogeneous network, entities from any two entity types can have interactions with a weight (rating) indicating the level of endorsement. Collective Matrix Factorization (CMF) has been proposed to address the recommendation problem in heterogeneous networks. However, a main issue with CMF is that entities share the same latent factor across different contexts. This is particularly problematic in two cases: Latent factors for entities that are cold-start in a context will be learnt mainly based on the data from other contexts where these entities are not cold-start, and therefore the factors are not properly learned for the cold-start context. Also, if a context has more data compared to another context, then the dominant context will dominate the learning process for the latent factors for entities shared in these two contexts. In this paper, we propose a context-dependent matrix factorization model, HeteroMF, that considers a general latent factor for entities of every entity type and context-dependent latent factors for every context in which the entities are involved. We learn a general latent factor for every entity and transfer matrices for every context to convert the general latent factors into a context-dependent latent factor. Experiments on two real life datasets from Epinions and Flixster demonstrate that HeteroMF substantially outperforms CMF, particularly for cold-start entities and for contexts where interactions in one contexts are dominated by other contexts.
Joint collaborative ranking with social relationships in top-n recommendation
Authors: Dimitrios Rafailidis, Fabio Crestani
Venue: Conference CIKM 2016
Year: 2016

With the advent of learning to rank methods, relevant studies showed that Collaborative Ranking (CR) models can produce accurate ranked lists in the top-N recommendation problem. However, in practice several real-world problems decrease their ranking performance, such as the sparsity and cold-start problems, which often occur in recommendation systems for inactive or new users. In this study, to account for the fact that the selections of social friends can improve the recommendation accuracy, we propose a joint CR model based on the users' social relationships. We propose two different CR strategies based on the notions of Social Reverse Height and Social Height, which consider how well the relevant and irrelevant items of users and their social friends have been ranked at the top of the list, respectively. We focus on the top of the list mainly because users see the top-N recommendations in real-world applications, and not the whole ranked list. Furthermore, we formulate a joint objective function to consider both CR strategies, and propose an alternating minimization algorithm to learn our joint CR model. Our experiments on benchmark datasets show that our proposed joint CR model outperforms other state-of-the-art models that either consider social relationships or focus on the ranking performance at the top of the list.
Learning personalized preference of strong and weak ties for social recommendation
Authors: Xin Wang, Steven C. H. Hoi, Martin Ester, Jiajun Bu, Chun Chen
Venue: Conference WWW 2017
Year: 2017

Recent years have seen a surge of research on social recommendation techniques for improving recommender systems due to the growing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despite the extensive studies, no existing work has attempted to distinguish and learn the personalized preferences between strong and weak ties, two important terms widely used in social sciences, for each individual in social recommendation. In this paper, we first highlight the importance of different types of ties in social relations originated from social sciences, and then propose anovel social recommendation method based on a new Probabilistic Matrix Factorization model that incorporates the distinction of strong and weak ties for improving recommendation performance. The proposed method is capable of simultaneously classifying different types of social ties in a social network w.r.t. optimal recommendation accuracy, and learning a personalized tie type preference for each user in addition to other parameters. We conduct extensive experiments on four real-world datasets by comparing our method with state-of-the-art approaches, and find encouraging results that validate the efficacy of the proposed method in exploiting the personalized preferences of strong and weak ties for social recommendation.
A gpu-tailored approach for training kernelized svms
Authors: Andrew Cotter, Nathan Srebro, Joseph Keshet
Venue: Conference KDD 2011
Year: 2011

We present a method for efficiently training binary and multiclass kernelized SVMs on a Graphics Processing Unit (GPU). Our methods apply to a broad range of kernels, including the popular Gaus- sian kernel, on datasets as large as the amount of available memory on the graphics card. Our approach is distinguished from earlier work in that it cleanly and efficiently handles sparse datasets through the use of a novel clustering technique. Our optimization algorithm is also specifically designed to take advantage of the graphics hardware. This leads to different algorithmic choices then those preferred in serial implementations. Our easy-to-use library is orders of magnitude faster then existing CPU libraries, and several times faster than prior GPU approaches.
Goal-directed inductive matrix completion
Authors: Si Si, Kai-Yang Chiang, Cho-Jui Hsieh, Nikhil Rao, Inderjit S. Dhillon
Venue: Conference KDD 2016
Year: 2016

Matrix completion (MC) with additional information has found wide applicability in several machine learning applications. Among algorithms for solving such problems, Inductive Matrix Completion(IMC) has drawn a considerable amount of attention, not only for its well established theoretical guarantees but also for its superior performance in various real-world applications. However, IMC based methods usually place very strong constraints on the quality of the features(side information) to ensure accurate recovery, which might not be met in practice. In this paper, we propose Goal-directed Inductive Matrix Completion(GIMC) to learn a nonlinear mapping of the features so that they satisfy the required properties. A key distinction between GIMC and IMC is that the feature mapping is learnt in a supervised manner, deviating from the traditional approach of unsupervised feature learning followed by model training. We establish the superiority of our method on several popular machine learning applications including multi-label learning, multi-class classification, and semi-supervised clustering.
Fact checking in heterogeneous information networks
Authors: Baoxu Shi, Tim Weninger
Venue: Conference WWW 2016
Year: 2016

Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and show that a new model of the top discriminative meta paths is able to understand the meaning of some statement and accurately determine its veracity. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using public, million node knowledge graphs extracted from Wikipedia and SemMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative path model is easily interpretable and provides sensible reasons for the final determination.