OpenRaiser/Intern-Atlas huggingface dataset:
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
Authors: Yihong Tang, Ao Qu, Andy H. F. Chow, William H. K. Lam, Sze Chun Wong, Wei Ma
Venue: CIKM
Year: 2022

Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies indicate that the accuracy improvement by developing new model structures is becoming marginal. Instead, we envision that the improvement can be achieved by transferring the ''forecasting-related knowledge" across cities with different data distributions and network topologies. To this end, this paper aims to propose a novel transferable traffic forecasting framework: Domain Adversarial Spatial-Temporal Network (DASTNet). DASTNet is pre-trained on multiple source networks and fine-tuned with the target network's traffic data. Specifically, we leverage the graph representation learning and adversarial domain adaptation techniques to learn the domain-invariant node embeddings, which are further incorporated to model the temporal traffic data. To the best of our knowledge, we are the first to employ adversarial multi-domain adaptation for network-wide traffic forecasting problems. DASTNet consistently outperforms all state-of-the-art baseline methods on three benchmark datasets. The trained DASTNet is applied to Hong Kong's new traffic detectors, and accurate traffic predictions can be delivered immediately (within one day) when the detector is available. Overall, this study suggests an alternative to enhance the traffic forecasting methods and provides practical implications for cities lacking historical traffic data. Source codes of DASTNet are available at https://github.com/YihongT/DASTNet.
A Dual Channel Intent Evolution Network for Predicting Period-Aware Travel Intentions at Fliggy
Authors: Wanjie Tao, Zhang-Hua Fu, Liangyue Li, Zulong Chen, Hong Wen, Yuanyuan Liu, Qijie Shen, Peilin Chen
Venue: CIKM
Year: 2022

Fliggy of Alibaba group is one of the largest online travel platform (OTPs) in China, which provides travel products and travel experiences for tens of millions of online users by the personalized recommendation system (RS). User's future travel intent prediction is one key problem in travel scenario, which decides where and what to recommend, e.g., traveling to a surrounding city or a distant city. Such travel intent prediction problem has a lot of important applications, e.g., to push a notification with surrounding scenic spots recommendation to a user with intent to travel around, or to enable personalized promotion strategies to users with different intents. Existing studies on user's intent are largely sub-optimal for users' travel intent prediction at OTPs, since they rarely pay attentions to the characteristics of the travel industry, namely, user behavior sparsity due to low frequency of travel, spatial-temporal periodicity patterns, and the correlations between user's online and offline behaviors. In this paper, to address these challenges, we propose a dual channel intent evolution network based online-offline periodicity-aware network, DCIEN, for user's future travel intent prediction. In particular, it consists of two basic components including 1) Spatial-temporal Intent Patterns Network(ST-IPN), which exploits users' periodic intent patterns from offline data based on convolutional neural networks; 2) Periodicity-aware Intent Evolution Network(PA-IEN), which captures user's instant intent from online behaviors data and the interactions between online and offline intents. Extensive offline and online experiments on a real-world OTP demonstrate the superior performance of DCIEN over state-of-the-art methods.
How Hybrid Work Will Make Work More Intelligent
Authors: Jaime Teevan
Venue: CIKM
Year: 2022

We are in the middle of the most significant change to work practices in generations. For hundreds of years, physical space was the most important technology people used to get things done. The coming Hybrid Work Era, however, will be shaped by digital technology. The recent rapid shift to remote work accelerated the digital transformation already underway at many organizations, and new types of work-related data are now being generated at an unprecedented rate. For example, the average Microsoft Teams user spends 252% more time in the application now than they did in February 2020. During the early stages of the pandemic, we saw the direct impact of digital technology on work in its ability to help people sustain collaboration across time and space. But looking forward, the new digital knowledge captured in the Hybrid Work Era will allow us to reimagine work at an even more fundamental level. AI systems, for example, can now learn from the conversations people have to support knowledge re-use, and even learn how successful conversations happen to help drive more productive meetings. Historically, AI systems have been hindered in a work context by a lack of data; the development of foundation models is changing that, creating an opportunity to combine general world knowledge with the knowledge and behaviors currently locked up and siloed as we work. The CIKM community can shape the new future of work, but first must address the challenges surrounding workplace knowledge management that arise as we have more data, more sophisticated AI, and more human engagement. In this talk I will give an overview of what research tells us about emerging work practices, and explore how the CIKM community can build on these findings to help create a new – and better – future of work.
Temporal Contrastive Pre-Training for Sequential Recommendation
Authors: Changxin Tian, Zihan Lin, Shuqing Bian, Jinpeng Wang, Wayne Xin Zhao
Venue: CIKM
Year: 2022

Recently, pre-training based approaches are proposed to leverage self-supervised signals for improving the performance of sequential recommendation. However, most of existing pre-training recommender systems simply model the historical behavior of a user as a sequence, while lack of sufficient consideration on temporal interaction patterns that are useful for modeling user behavior. In order to better model temporal characteristics of user behavior sequences, we propose a Temporal Contrastive Pre-training method for Sequential Recommendation (TCPSRec for short). Based on the temporal intervals, we consider dividing the interaction sequence into more coherent subsequences, and design temporal pre-training objectives accordingly. Specifically, TCPSRec models two important temporal properties of user behavior, i.e., invariance and periodicity. For invariance, we consider both global invariance and local invariance to capture the long-term preference and short-term intention, respectively. For periodicity, TCPSRec models coarse-grained periodicity and fine-grained periodicity at the subsequence level, which is more stable than modeling periodicity at the item level. By integrating the above strategies, we develop a unified contrastive learning framework with four specially designed pre-training objectives for fusing temporal information into sequential representations. We conduct extensive experiments on six real-world datasets, and the results demonstrate the effectiveness and generalization of our proposed method.
A Context-Enhanced Generate-then-Evaluate Framework for Chinese Abbreviation Prediction
Authors: Hanwen Tong, Chenhao Xie, Jiaqing Liang, Qianyu He, Zhiang Yue, Jingping Liu, Yanghua Xiao, Wenguang Wang
Venue: CIKM
Year: 2022

As a popular form of lexicalization, abbreviation is widely used in both oral and written language and plays an important role in various Natural Language Processing applications. However, current approaches cannot ensure that the predicted abbreviation preserves the meaning of its full form and maintains fluency. In this paper, we introduce a fresh perspective to evaluate the quality of abbreviations within their textual contexts with pre-trained language model. To this end, we propose a novel two-stage generate-then-evaluate framework enhanced by context, which consists of a generation model to generate multiple candidate abbreviations and an evaluation model to evaluate their quality within their contexts. Experimental results show that our framework consistently outperforms all the existing approaches, achieving 53.2% Hit@1 performance with a 5.6 points improvement compared to its previous best result. Our code and data are publicly available at https://github.com/HavenTong/CEGE.
FeReD: Federated Reinforcement Learning in the DBMS
Authors: Sotirios Tzamaras, Radu Ciucanu, Marta Soare, Sihem Amer-Yahia
Venue: CIKM
Year: 2022

Federated learning enables clients to enrich their locally trained models via updates performed by a coordination server based on aggregates of local models. There are multiple advances in methods and applications of federated learning, in particular in cross-device federation, where clients having limited data and computational resources collaborate in a joint learning problem. Given the constraint of limited resources in cross-device federation, we study the potential benefits of embedded in-DBMS learning, illustrated in a federated reinforcement learning problem. We demonstrate FeReD, a system that contrasts the performance of cross-device federation using Q-learning, a popular reinforcement learning algorithm. FeReD offers step-by-step guidance for in-DBMS SQLite implementation challenges for both horizontal and vertical data partitioning. FeReD also allows to contrast the Q-learning implementations in SQLite vs a standard Python implementation, by highlighting their learning performance, computational efficiency, succinctness and expressiveness. A video of FeReD is available at https://www.youtube.com/watch?v=2kRIu_C5RZA and its open source code at https://github.com/sotostzam/FeReD.
ML-1M++: MovieLens-Compatible Additional Preferences for More Robust Offline Evaluation of Sequential Recommenders
Authors: Kazutoshi Umemoto
Venue: CIKM
Year: 2022

Sequential recommendation is the task of predicting the next interacted item of a target user, given his/her past interaction sequence. Conventionally, sequential recommenders are evaluated offline with the last item in each sequence as the sole correct (relevant) label for the testing example of the corresponding user. However, little is known about how this sparsity of preference data affects the robustness of the offline evaluation's outcomes. To help researchers address this, we collect additional preference data via crowdsourcing. Specifically, we propose an assessment interface tailored to the sequential recommendation task and ask crowd workers to assess the (potential) relevance of each candidate item in MovieLens 1M, a commonly used dataset. Toward establishing a more robust evaluation methodology, we release the collected preference data, which we call ML-1M++, as well as the code of the assessment interface.
Adaptive Multi-Source Causal Inference from Observational Data
Authors: Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong
Venue: CIKM
Year: 2022

We propose a new approach to estimate causal effects from observational data. We leverage multiple data sources which share similar causal mechanisms with the scarce target observations to help infer causal effects in the target domain. The data sources may be available in sequence or some unplanned order. Causal inference can be carried out without prior knowledge of the data discrepancy between the source and target observations. We introduce three levels of knowledge transfer through modelling the outcomes, treatments, and confounders to achieve consistent positive transfer. We incorporate parametric transfer factors to adaptively control the transfer strength, thus achieving a fair and balanced knowledge transfer between the sources and the target. We also empirically show the effectiveness of the proposed method as compared with recent baselines.
Towards a Learned Cost Model for Distributed Spatial Join: Data, Code & Models
Authors: Tin Vu, Alberto Belussi, Sara Migliorini, Ahmed Eldawy
Venue: CIKM
Year: 2022

Geospatial data comprise around 60% of all the publicly available data. One of the essential and most complex operations that brings together multiple geospatial datasets is the spatial join operation. Due to its complexity, there is a lot of partitioning techniques and parallel algorithms for the spatial join problem. This leads to a complex query optimization problem: which algorithm to use for a given pair of input datasets that we want to join? With the rise of machine learning, there is a promise in addressing this problem with the use of various learned models. However, one of the concerns is the lack of a standard and publicly available data to train and test on, as well as the lack of accessible baseline models. This resource paper helps the research community to solve this problem by providing synthetic and real datasets for spatial join, source code for constructing more datasets, and several baseline solutions that researchers can further extend and compare to.
Modeling Turn-Based Sequences for Player Tactic Applications in Badminton Matches
Authors: Wei-Yao Wang
Venue: CIKM
Year: 2022

In recent years, a growing body of research has started to explore applying artificial intelligence to the sports industry due to the availability of data and the advancement of techniques. Such applications not only play an important role during matches but also have a great influence on the training stage. However, applying deep learning techniques to sports analytics has several technical challenges, which also remain untouched in badminton analytics as there is no public dataset of stroke event records. Therefore, this dissertation intends to explore challenging research questions and application issues that have not been addressed for benefiting both the research and badminton communities. To achieve the objectives, we, for the first time, propose a unified badminton language to describe the process of the shot, which enables us to conduct downstream applications. Specifically, our first task is to measure the win probability of each shot in badminton matches by considering long-term and short-term dependencies. Second, we introduce a framework with two encoder-decoder extractors and a position-aware fusion network to forecast the possible tactics of players, which is still unexplored in turn-based sports. To provide the transparency of these models, we aim to design a post-hoc explainer by computing feature attributions with Shapley values as the third task. In this manner, researchers can investigate model behaviors for advanced improvement, and the badminton community benefits from coaching the players and determining the strategies. This dissertation is supervised by Wen-Chih Peng (wcpeng@cs.nycu.edu.tw).
ClozeSearch: A Collocation Retrieval Application to Assist in Scientific Writing
Authors: Mengru Wang, Omar Alonso
Venue: CIKM
Year: 2022

This paper presents a slot-filling retrieval application, ClozeSearch, for searching collocates to assist users in scientific writing. ClozeSearch suggests plausible collocates to fill in user-created slots within the query text. To ease the query formulation, we adapt the autocomplete feature to a slot-filling fashion of querying. Given a query prefix with slots, we select multiple valid terms to replace each slot and then provide complete suggestions based on such hypothetical prefixes. To reduce the search space for sampling the terms, we leveraged histogram pruning. Moreover, we propose two alternatives based on syntactic graph and deep language model for better flexibility in coping with long queries. Experimental results show that our proposed methods outperform the conventional pattern-based matching by a maximum of 0.18 points in F1-score.
Bandit Learning in Many-to-One Matching Markets
Authors: Zilong Wang, Liya Guo, Junming Yin, Shuai Li
Venue: CIKM
Year: 2022

The problem of two-sided matching markets is well-studied in social science and economics. Some recent works study how to match while learning the unknown preferences of agents in one-to-one matching markets. However, in many cases like the online recruitment platform for short-term workers, a company can select more than one agent while an agent can only select one company at a time. These short-term workers try many times in different companies to find the most suitable jobs for them. Thus we consider a more general bandit learning problem in many-to-one matching markets where each arm has a fixed capacity and agents make choices with multiple rounds of iterations. We develop algorithms in both centralized and decentralized settings and prove regret bounds of order O(log T) and Olog2 T) respectively. Extensive experiments show the convergence and effectiveness of our algorithms.
CTRL: Cooperative Traffic Tolling via Reinforcement Learning
Authors: Yiheng Wang, Hexi Jin, Guanjie Zheng
Venue: CIKM
Year: 2022

People have been working long to tackle the traffic congestion problem. Among the different measures, traffic tolling has been recognized as an effective way to mitigate citywide congestion. However, traditional tolling methods can not deal with the dynamic traffic flow in cities. Meanwhile, thanks to the development of traffic sensing technology, how to set appropriate dynamic tolling according to real time traffic observations has attracted research attention in recent years. In this paper, we put the dynamic tolling problem in a reinforcement learning setting and try to tackle the three key challenges of complex state representation, pricing action credit assignment, and route price relative competition. We propose a soft actor-critic method with (1) a route-level state attention, (2) an interpretable and provable reward design, and (3) a competition-aware Q attention. Extensive experiments on real datasets have shown the superior performance of our proposed method. In addition, interesting analysis on pricing actions and vehicle routes have demonstrated why the proposed method can outperform baselines.
Latent Coreset Sampling based Data-Free Continual Learning
Authors: Zhuoyi Wang, Dingcheng Li, Ping Li
Venue: CIKM
Year: 2022

Catastrophic forgetting poses a major challenge in continual learning where the old knowledge is forgotten when the model is updated on new tasks. Existing solutions tend to solve this challenge through generative models or exemplar-replay strategies. However, such methods may not alleviate the issue that the low-quality samples are generated or selected for the replay, which would directly reduce the effectiveness of the model, especially in the class imbalance, noise, or redundancy scenarios. Accordingly, how to select a suitable coreset during continual learning becomes significant in such setting. In this work, we propose a novel approach that leverages continual coreset sampling (CCS) to address these challenges. We aim to select the most representative subsets during each iteration. When the model is trained on new tasks, it closely approximates/matches the gradient of both the previous and current tasks with respect to the model parameters. This way, adaptation of the model to new datasets could be more efficient. Furthermore, different from the old data storage for maintaining the old knowledge, our approach choose to preserving them in the latent space. We augment the previous classes in the embedding space as the pseudo sample vectors from the old encoder output, strengthened by the joint training with selected new data. It could avoid data privacy invasions in a real-world application when we update the model. Our experiments validate the effectiveness of our proposed approach over various CV/NLP datasets under against current baselines, and we also indicate the obvious improvement of model adaptation and forgetting reduction in a data-free manner.
Multi-level Contrastive Learning Framework for Sequential Recommendation
Authors: Ziyang Wang, Huoyu Liu, Wei Wei, Yue Hu, Xian-Ling Mao, Shaojian He, Rui Fang, Dangyang Chen
Venue: CIKM
Year: 2022

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.
Explanation Guided Contrastive Learning for Sequential Recommendation
Authors: Lei Wang, Ee-Peng Lim, Zhiwei Liu, Tianxiang Zhao
Venue: CIKM
Year: 2022

Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions. Nevertheless, the existing contrastive learning-based methods fail to ensure that the positive (or negative) sequence obtained by some random augmentation (or sequence sampling) on a given anchor user sequence remains to be semantically similar (or different). When the positive and negative sequences turn out to be false positive and false negative respectively, it may lead to degraded recommendation performance. In this work, we address the above problem by proposing Explanation Guided Augmentations (EGA) and Explanation Guided Contrastive Learning for Sequential Recommendation (EC4SRec) model framework. The key idea behind EGA is to utilize explanation method(s) to determine items' importance in a user sequence and derive the positive and negative sequences accordingly. EC4SRec then combines both self-supervised and supervised contrastive learning over the positive and negative sequences generated by EGA operations to improve sequence representation learning for more accurate recommendation results. Extensive experiments on four real-world benchmark datasets demonstrate that EC4SRec outperforms the state-of-the-art sequential recommendation methods and two recent contrastive learning-based sequential recommendation methods, CL4SRec and DuoRec. Our experiments also show that EC4SRec can be easily adapted for different sequence encoder backbones (e.g., GRU4Rec and Caser), and improve their recommendation performance.