OpenRaiser/Intern-Atlas huggingface dataset:
Modeling Sequential Online Interactive Behaviors with Temporal Point Process
Authors: Renqin Cai, Xueying Bai, Zhenrui Wang, Yuling Shi, Parikshit Sondhi, Hongning Wang
Venue: Conference CIKM 2018
Year: 2018

The massively available data about user engagement with online information service systems provides a gold mine about users' latent intents. It calls for quantitative user behavior modeling. In this paper, we study the problem by looking into users' sequential interactive behaviors. Inspired by the concepts of episodic memory and semantic memory in cognitive psychology, which describe how users' behaviors are differently influenced by past experience, we propose a Long- and Short-term Hawkes Process model. It models the short-term dependency between users' actions within a period of time via a multi-dimensional Hawkes process and the long-term dependency between actions across different periods of time via a one dimensional Hawkes process. Experiments on two real-world user activity log datasets (one from an e-commerce website and one from a MOOC website) demonstrate the effectiveness of our model in capturing the temporal dependency between actions in a sequence of user behaviors. It directly leads to improved accuracy in predicting the type and the time of the next action. Interestingly, the inferred dependency between actions in a sequence sheds light on the underlying user intent behind direct observations and provides insights for downstream applications.
Deep multifaceted transformers for multi-objective ranking in large-scale e-commerce recommender systems
Authors: Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, Lixin Zou, Yiding Liu, Dawei Yin
Venue: Conference CIKM 2020
Year: 2020

Recommender Systems have been playing essential roles in e-commerce portals. Existing recommendation algorithms usually learn the ranking scores of items by optimizing a single task (e.g. Click-through rate prediction) based on users' historical click sequences, but they generally pay few attention to simultaneously modeling users' multiple types of behaviors or jointly optimize multiple objectives (e.g. both Click-through rate and Conversion rate), which are both vital for e-commerce sites. In this paper, we argue that it is crucial to formulate users' different interests based on multiple types of behaviors and perform multi-task learning for significant improvement in multiple objectives simultaneously. We propose Deep Multifaceted Transformers (DMT), a novel framework that can model users' multiple types of behavior sequences simultaneously with multiple Transformers. It utilizes Multi-gate Mixture-of-Experts to optimize multiple objectives. Besides, it exploits unbiased learning to reduce the selection bias in the training data. Experiments on JD real production dataset demonstrate the effectiveness of DMT, which significantly outperforms state-of-art methods. DMT has been successfully deployed to serve the main traffic in the commercial Recommender System in JD.com. To facilitate future research, we release the codes and datasets at https://github.com/guyulongcs/CIKM2020_DMT.
Neural survival recommender
Authors: How Jing, Alexander J. Smola
Venue: Conference WSDM 2017
Year: 2017

The ability to predict future user activity is invaluable when it comes to content recommendation and personalization. For instance, knowing when users will return to an online music service and what they will listen to increases user satisfaction and therefore user retention.
Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models
Authors: Quyu Kong, Marian-Andrei Rizoiu, Lexing Xie
Venue: Conference WSDM 2020
Year: 2020

Epidemic models and self-exciting processes are two types of models used to describe diffusion phenomena online and offline. These models were originally developed in different scientific communities, and their commonalities are under-explored. This work establishes, for the first time, a general connection between the two model classes via three new mathematical components. The first is a generalized version of stochastic Susceptible-Infected-Recovered (SIR) model with arbitrary recovery time distributions; the second is the relationship between the (latent and arbitrary) recovery time distribution, recovery hazard function, and the infection kernel of self-exciting processes; the third includes methods for simulating, fitting, evaluating and predicting the generalized process. On three large Twitter diffusion datasets, we conduct goodness-of-fit tests and holdout log-likelihood evaluation of self-exciting processes with three infection kernels --- exponential, power-law and Tsallis Q-exponential. We show that the modeling performance of the infection kernels varies with respect to the temporal structures of diffusions, and also with respect to user behavior, such as the likelihood of being bots. We further improve the prediction of popularity by combining two models that are identified as complementary by the goodness-of-fit tests.
Modeling interdependent and periodic real-world action sequences
Authors: Takeshi Kurashima, Tim Althoff, Jure Leskovec
Venue: Conference WWW 2018
Year: 2018

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions in the real world is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model, called TIPAS, for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million real-world actions (e.g., eating, sleep, and exercise) taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, TIPAS improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.
Neural Interactive Collaborative Filtering
Authors: Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao 0001, Weidong Liu 0001, Jimmy Xiangji Huang, Dawei Yin 0001
Venue: Conference SIGIR 2020
Year: 2020

In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile based on the interactive feedback. The most challenging problem in this scenario is how to suggest items when the user profile has not been well established, i.e., recommend for cold-start users or warm-start users with taste drifting. Existing approaches either rely on overly pessimistic linear exploration strategy or adopt meta-learning based algorithms in a full exploitation way. In this work, to quickly catch up with the user's interests, we propose to represent the exploration policy with a neural network and directly learn it from the feedback data. Specifically, the exploration policy is encoded in the weights of multi-channel stacked self-attention neural networks and trained with efficient Q-learning by maximizing users' overall satisfaction in the recommender systems. The key insight is that the satisfied recommendations triggered by the exploration recommendation can be viewed as the exploration bonus (delayed reward) for its contribution on improving the quality of the user profile. Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning. Extensive experiments and analysis conducted on three benchmark collaborative filtering datasets have demonstrated the advantage of our method over state-of-the-art methods.
Decoupled Weight Decay Regularization
Authors: Ilya Loshchilov, Frank Hutter
Venue: Conference ICLR 2019
Year: 2019

L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it "weight decay" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at https://github.com/loshchil/AdamW-and-SGDW
Positive-unlabeled learning in streaming networks
Authors: Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A. Hasegawa-Johnson, Thomas S. Huang
Venue: Conference KDD 2016
Year: 2016

Data of many problems in real-world systems such as link prediction and one-class recommendation share common characteristics. First, data are in the form of positive unlabeled (PU) measurements (e.g. Twitter "following", Facebook "like", etc.) that do not provide negative information, which can be naturally represented as networks. Second, in the era of big data, such data are generated temporally-ordered, continuously and rapidly, which determines its streaming nature. These common characteristics allow us to unify many problems into a novel framework -- PU learning in streaming networks. In this paper, a principled probabilistic approach SPU is proposed to leverage the characteristics of the streaming PU inputs. In particular, SPU captures temporal dynamics and provides real-time adaptations and predictions by identifying the potential negative signals concealed in unlabeled data. Our empirical results on various real-world datasets demonstrate the effectiveness of the proposed framework over other state-of-the-art methods in both link prediction and recommendation.
Reconciling Ontologies and the Web of Data
Authors: Ziawasch Abedjan, Johannes Lorey, Felix Naumann
Venue: Conference CIKM 2012
Year: 2012

To integrate Linked Open Data, which originates from various and heterogeneous sources, the use of well-defined ontologies is essential. However, oftentimes the utilization of these ontologies by data publishers differs from the intended application envisioned by ontology engineers. This may lead to unspecified properties being used ad-hoc as predicates in RDF triples or it may result in infrequent usage of specified properties. These mismatches impede the goals and propagation of the Web of Data as data consumers face difficulties when trying to discover and integrate domain-specific information. In this work, we identify and classify common misusage patterns by employing frequency analysis and rule mining. Based on this analysis, we introduce an algorithm to propose suggestions for a data-driven ontology re-engineering workflow, which we evaluate on two large-scale RDF datasets.
Integrating Product Data from Websites Offering Microdata Markup
Authors: Petar Petrovski, Volha Bryl, Christian Bizer
Venue: Conference WWW 2014
Year: 2014

Large numbers of websites have started to markup their content using standards such as Microdata, Microformats, and RDFa. The marked-up content elements comprise descriptions of people, organizations, places, events, products, ratings, and reviews. This development has accelerated in last years as major search engines such as Google, Bing and Yahoo! use the markup to improve their search results. Embedding semantic markup facilitates identifying content elements on webpages. However, the markup is mostly not as fine-grained as desirable for applications that aim to integrate data from large numbers of websites. This paper discusses the challenges that arise in the task of integrating descriptions of electronic products from several thousand e-shops that offer Microdata markup. We present a solution for each step of the data integration process including Microdata extraction, product classification, product feature extraction, identity resolution, and data fusion. We evaluate our processing pipeline using 1.9 million product offers from 9240 e-shops which we extracted from the Common Crawl 2012, a large public Web corpus.
Drn: A deep reinforcement learning framework for news recommendation
Authors: Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, Zhenhui Li
Venue: Conference WWW 2018
Year: 2018

In this paper, we propose a novel Deep Reinforcement Learning framework for news recommendation. Online personalized news recommendation is a highly challenging problem due to the dynamic nature of news features and user preferences. Although some online recommendation models have been proposed to address the dynamic nature of news recommendation, these methods have three major issues. First, they only try to model current reward (e.g., Click Through Rate). Second, very few studies consider to use user feedback other than click / no click labels (e.g., how frequent user returns) to help improve recommendation. Third, these methods tend to keep recommending similar news to users, which may cause users to get bored. Therefore, to address the aforementioned challenges, we propose a Deep Q-Learning based recommendation framework, which can model future reward explicitly. We further consider user return pattern as a supplement to click / no click label in order to capture more user feedback information. In addition, an effective exploration strategy is incorporated to find new attractive news for users. Extensive experiments are conducted on the offline dataset and online production environment of a commercial news recommendation application and have shown the superior performance of our methods.
The many faces of optimism: A unifying approach
Authors: Istvan Szita, András Lörincz
Venue: Conference ICML 2008
Year: 2008

The exploration-exploitation dilemma has been an intriguing and unsolved problem within the framework of reinforcement learning. "Optimism in the face of uncertainty" and model building play central roles in advanced exploration methods. Here, we integrate several concepts and obtain a fast and simple algorithm. We show that the proposed algorithm finds a near-optimal policy in polynomial time, and give experimental evidence that it is robust and efficient compared to its ascendants.
Neural symbolic reader: Scalable integration of distributed and symbolic representations for reading comprehension
Authors: Xinyun Chen, Chen Liang, Adams Wei Yu, Denny Zhou, Dawn Song, Quoc V. Le
Venue: Conference ICLR 2020
Year: 2020

Integrating distributed representations with symbolic operations is essential for reading comprehension requiring complex reasoning, such as counting, sorting and arithmetics, but most existing approaches are hard to scale to more domains or more complex reasoning. In this work, we propose the Neural Symbolic Reader (NeRd), which includes a reader, e.g., BERT, to encode the passage and question, and a programmer, e.g., LSTM, to generate a program that is executed to produce the answer. Compared to previous works, NeRd is more scalable in two aspects: (1) domain-agnostic, i.e., the same neural architecture works for different domains; (2) compositional, i.e., when needed, complex programs can be generated by recursively applying the predefined operators, which become executable and interpretable representations for more complex reasoning. Furthermore, to overcome the challenge of training NeRd with weak supervision, we apply data augmentation techniques and hard Expectation-Maximization (EM) with thresholding. On DROP, a challenging reading comprehension dataset that requires discrete reasoning, NeRd achieves 2.5%/1.8% absolute improvement over the state-of-the-art on EM/F1 metrics. With the same architecture, NeRd significantly outperforms the baselines on MathQA, a math problem benchmark that requires multiple steps of reasoning, by 25.5% absolute increment on accuracy when trained on all the annotated programs. More importantly, NeRd still beats the baselines even when only 20% of the program annotations are given.
Ctsum: extracting more certain summaries for news articles
Authors: Xiaojun Wan, Jianmin Zhang
Venue: Conference SIGIR 2014
Year: 2014

People often read summaries of news articles in order to get reliable information about an event or a topic. However, the information expressed in news articles is not always certain, and some sentences contain uncertain information about the event. Existing summarization systems do not consider whether a sentence in news articles is certain or not. In this paper, we propose a novel system called CTSUM to incorporate the new factor of information certainty into the summarization task. We first analyze the sentences in news articles and automatically predict the certainty levels of sentences by using the support vector regression method with a few useful features. The predicted certainty scores are then incorporated into a summarization system with a graph-based ranking algorithm. Experimental results on a manually labeled dataset verify the effectiveness of the sentence certainty prediction technique, and experimental results on the DUC2007 dataset shows that our new summarization system cannot only produce summaries with better content quality, but also produce summaries with higher certainty.
Open relation extraction for support passage retrieval: Merit and open issues
Authors: Amina Kadry, Laura Dietz
Venue: Conference SIGIR 2017
Year: 2017

Our goal is to complement an entity ranking with human-readable explanations of how those retrieved entities are connected to the information need. Relation extraction technology should aid in finding such support passages, especially in combination with entities and query terms. This work explores how the current state of the art in unsupervised relation extraction (OpenIE) contributes to a solution for the task, assessing potential, limitations, and avenues for further investigation.
Multi-task learning for boosting with application to web search ranking
Authors: Olivier Chapelle, Pannagadatta K. Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle L. Tseng
Venue: Conference KDD 2010
Year: 2010

In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing the specifics of each learning task with task-specific parameters and the commonalities between them through shared parameters. This enables implicit data sharing and regularization. We evaluate our learning method on web-search ranking data sets from several countries. Here, multitask learning is particularly helpful as data sets from different countries vary largely in size because of the cost of editorial judgments. Our experiments validate that learning various tasks jointly can lead to significant improvements in performance with surprising reliability.
Maximizing Marginal Utility per Dollar for Economic Recommendation
Authors: Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Shijie Geng, Zuohui Fu, Yongfeng Zhang
Venue: Conference WWW 2019
Year: 2019

Understanding the economic nature of consumer decisions in e-Commerce is important to personalized recommendation systems. Established economic theories claim that informed consumers always attempt to maximize their utility by choosing the items of the largest marginal utility per dollar (MUD) within their budgets. For example, gaining 5 dollars of extra benefit by spending 10 dollars makes a consumer much more satisfied than having the same amount of extra benefit by spending 20 dollars, although the second product may have higher absolute utility value. Meanwhile, making purchases online may be risky decisions that could cause dissatisfaction. For example, people may give low ratings towards purchased items that they thought they would like when placing the order. Therefore, the design of recommender systems should also take users' risk attitudes into consideration to better learn consumer behaviors. Motivated by the first consideration, in this paper, we propose a learning algorithm to maximize marginal utility per dollar for recommendations. With the second, economic theory shows that rational people can be arbitrarily close to risk neutral when stakes are arbitrarily small, and this is generally applicable to consumer online purchase behaviors because most people spend a small portion of their total wealth for a single purchase. To integrate this theory with machine learning, we propose to augment MUD optimization with approximate risk-neural constraint to generate personalized recommendations. Experiments on real-world e-Commerce datasets show that our approach is able to achieve better performance than many classical recommendation methods, in terms of both traditional recommendation measures such as precision and recall, as well as economic measures such as MUD.
Entire space multi-task model: An effective approach for estimating post-click conversion rate
Authors: Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai
Venue: Conference SIGIR 2018
Year: 2018

Estimating post-click conversion rate (CVR) accurately is crucial for ranking systems in industrial applications such as recommendation and advertising. Conventional CVR modeling applies popular deep learning methods and achieves state-of-the-art performance. However it encounters several task-specific problems in practice, making CVR modeling challenging. For example, conventional CVR models are trained with samples of clicked impressions while utilized to make inference on the entire space with samples of all impressions. This causes a sample selection bias problem. Besides, there exists an extreme data sparsity problem, making the model fitting rather difficult. In this paper, we model CVR in a brand-new perspective by making good use of sequential pattern of user actions, i.e., impression -> click -> conversion. The proposed Entire Space Multi-task Model (ESMM) can eliminate the two problems simultaneously by i) modeling CVR directly over the entire space, ii) employing a feature representation transfer learning strategy. Experiments on dataset gathered from Taobao's recommender system demonstrate that ESMM significantly outperforms competitive methods. We also release a sampling version of this dataset to enable future research. To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.