Reviewerly/exHarmony huggingface dataset:
DFFM: Domain Facilitated Feature Modeling for CTR Prediction

CTR prediction is critical to industrial recommender systems. Recently, with the growth of business domains in enterprises, much attention has been focused on the multi-domain CTR recommendation. Numerous models have been proposed that attempt to use a unified model to serve multiple domains. Although much progress has been made, we argue that they ignore the importance of feature interactions and user behaviors when modeling cross-domain relations, which is a coarse-grained utilizing of domain information. To solve this problem, we propose Domain Facilitated Feature Modeling (DFFM) for CTR prediction. It incorporates domain-related information into the parameters of the feature interaction and user behavior modules, allowing for domain-specific learning of these two aspects. Extensive experiments are conducted on two public datasets and one industrial dataset to demonstrate the effectiveness of DFFM. We deploy the DFFM model in Huawei advertising platform and gain a 4.13% improvement of revenue on a two week online A/B test. Currently DFFM model has been used as the main traffic model, serving for hundreds of millions of people.
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange

Graph Neural Networks (GNNs) have already been widely used in various graph mining tasks. However, recent works reveal that the learned weights (channels) in well-trained GNNs are highly redundant, which inevitably limits the performance of GNNs. Instead of removing these redundant channels for efficiency consideration, we aim to reactivate them to enlarge the representation capacity of GNNs for effective graph learning. In this paper, we propose to substitute these redundant channels with other informative channels to achieve this goal. We introduce a novel GNN learning framework named AKE-GNN, which performs the Adaptive Knowledge Exchange strategy among multiple graph views generated by graph augmentations. AKE-GNN first trains multiple GNNs each corresponding to one graph view to obtain informative channels. Then, AKE-GNN iteratively exchanges redundant channels in the weight parameter matrix of one GNN with informative channels of another GNN in a layer-wise manner. Additionally, existing GNNs can be seamlessly incorporated into our framework. AKE-GNN achieves superior performance compared with various baselines across a suite of experiments on node classification, link prediction, and graph classification. In particular, we conduct a series of experiments on 15 public benchmark datasets, 8 popular GNN models, and 3 graph tasks and show that AKE-GNN consistently outperforms existing popular GNN models and even their ensembles. Extensive ablation studies and analyses on knowledge exchange methods validate the effectiveness of AKE-GNN.
Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs

Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality, graph structures are often incomplete and noisy, which hinders temporal graph networks (TGNs) from learning informative representations. Graph contrastive learning uses data augmentation to generate plausible variations of existing data and learn robust representations. However, rule-based augmentation approaches may be suboptimal as they lack learnability and fail to leverage rich information from downstream tasks. To address these issues, we propose a Time-aware Graph Structure Learning (TGSL) approach via sequence prediction on temporal graphs, which learns better graph structures for downstream tasks through adding potential temporal edges. In particular, it predicts time-aware context embedding based on previously observed interactions and uses the Gumble-Top-K to select the closest candidate edges to this context embedding. Additionally, several candidate sampling strategies are proposed to ensure both efficiency and diversity. Furthermore, we jointly learn the graph structure and TGNs in an end-to-end manner and perform inference on the refined graph. Extensive experiments on temporal link prediction benchmarks demonstrate that TGSL yields significant gains for the popular TGNs such as TGAT and GraphMixer, and it outperforms other contrastive learning methods on temporal graphs. We release the code at https://github.com/ViktorAxelsen/TGSL.
Batch-Mix Negative Sampling for Learning Recommendation Retrievers

Recommendation retrievers commonly retrieve user potentially preferred items from numerous items, where the query and item representation are learned according to the dual encoders with the log-softmax loss. Under real scenarios, the number of items becomes considerably large, making it exceedingly difficult to calculate the partition function with the whole item corpus. Negative sampling, which samples a subset from the item corpus, is widely used to accelerate the model training. Among different samplers, the in-batch sampling is commonly adopted for online recommendation retrievers, which regards the other items within the mini-batch as the negative samples for the given query, owing to its time and memory efficiency. However, the sample selection bias occurs due to the skewed feedback, harming the retrieval quality. In this paper, we propose a negative sampling approach named Batch-Mix Negative Sampling (BMNS), which adopts batch mixing operation to generate additional negatives for model training. Concretely, BMNS first generates new negative items with the sampled mix coefficient from the Beta distribution, after which a tailored correct strategy guided by frequency is designed to match the sampled softmax loss. In this way, the effort of re-encoding items out of the mini-batch is reduced while also improving the representation space of the negative set. The empirical experiments on four real-world datasets demonstrate BMNS is superior to the competitive negative inbatch sampling method.
Graph Contrastive Learning with Graph Info-Min

The complexity of the graph structure poses a challenge for graph representation learning. Contrastive learning offers a straightforward and efficient unsupervised framework for graph representation learning. It achieves unsupervised learning by augmenting the original views and comparing them with the augmented views. Several methods based on this framework have achieved significant progress in the field of graph representation learning. Despite its success, the factors contributing to good augmented views in graph contrast learning have received less attention. In order to address this issue, we introduce the graph info-min principle. We investigate the relationship between mutual information (MI) and good augmented views through experimental and theoretical analysis. Additionally, we present a new contrastive learning method called Info-min Contrastive Learning (IMCL). Specifically, The method comprises an adaptive graph augmentation generator and a pseudo-label generator. The graph augmentation generator ensures sufficient differentiation between the augmented and original views. The pseudo-label generator generates pseudo-labels as supervision signals, ensuring consistency between the classification results of augmented views and original views. Our method demonstrates excellent performance through extensive experimental results on various datasets.
Towards Deeper, Lighter and Interpretable Cross Network for CTR Prediction

Click Through Rate (CTR) prediction plays an essential role in recommender systems and online advertising. It is crucial to effectively model feature interactions to improve the prediction performance of CTR models. However, existing methods face three significant challenges. First, while most methods can automatically capture high-order feature interactions, their performance tends to diminish as the order of feature interactions increases. Second, existing methods lack the ability to provide convincing interpretations of the prediction results, especially for high-order feature interactions, which limits the trustworthiness of their predictions. Third, many methods suffer from the presence of redundant parameters, particularly in the embedding layer. This paper proposes a novel method called Gated Deep Cross Network (GDCN) and a Field-level Dimension Optimization (FDO) approach to address these challenges. As the core structure of GDCN, Gated Cross Network (GCN) captures explicit high-order feature interactions and dynamically filters important interactions with an information gate in each order. Additionally, we use the FDO approach to learn condensed dimensions for each field based on their importance. Comprehensive experiments on five datasets demonstrate the effectiveness, superiority and interpretability of GDCN. Moreover, we verify the effectiveness of FDO in learning various dimensions and reducing model parameters. The code is available on https://github.com/anonctr/GDCN.
Boosting Meta-Learning Cold-Start Recommendation with Graph Neural Network

Meta-learning methods have shown to be effective in dealing with cold-start recommendation. However, most previous methods rely on an ideal assumption that there exists a similar data distribution between source and target tasks, which are unsuitable for the scenario that only extremely limited number of new user or item interactions are available. In this paper, we propose to boost meta-learning cold-start recommendation with graph neural network (MeGNN). First, it utilizes the global neighborhood translation learning to obtain consistent potential interactions for all new user and item nodes, which can refine their representations. Second, it employs the local neighborhood translation learning to predict specific potential interactions for each node, thus guaranteeing the personalized requirement. In experiments, we combine MeGNN with two representative meta-learning models MeLU and TaNP. Extensive results on two widely-used datasets show the superiority of MeGNN in four different scenarios.
Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings

Knowledge Graphs (KGs) are a collection of facts describing entities connected by relationships. KG embeddings map entities and relations into a vector space while preserving their relational semantics. This enables effective inference of missing knowledge from the embedding space. Most KG embedding approaches focused on triple-shaped KGs. A great amount of real-world knowledge, however, cannot simply be represented by triples. In this tutorial, we give a systematic introduction to KG embeddings that go beyond the triple representation. In particular, the tutorial will focus on temporal facts where the triples are enriched with temporal information, hyper-relational facts where the triples are enriched with qualifiers, n-ary facts describing relationships between multiple entities, and also facts that are augmented with literal and text descriptions. During the tutorial, we will introduce both fundamental knowledge and advanced topics for understanding recent embedding approaches for beyond-triple representations.
On the Reliability of User Feedback for Evaluating the Quality of Conversational Agents

We analyse the reliability of users' explicit feedback for evaluating the quality of conversational agents. Using data from a commercial conversational system, we analyse how user feedback compares with human annotations; how well it aligns with implicit user satisfaction signals, such as retention; and how much user feedback is needed to reliably evaluate the quality of a conversational system.
Sequential Recommendation via an Adaptive Cross-domain Knowledge Decomposition

Cross-domain recommendation, as an intelligent machine to alleviate data sparsity and cold start problems, has attracted extensive attention from scholars. Existing cross-domain recommendation frameworks usually leverage overlapping entities for knowledge transfer, the most popular of which are information aggregation and consistency maintenance. Despite decent improvements, the neglect of dynamic perspectives, the presence of confounding factors, and the disparities in domain properties inevitably constrain model performance. In view of this, this paper proposes a sequential recommendation framework via adaptive cross-domain knowledge decomposition, namely ARISEN, which focuses on employing adaptive causal learning to improve recommendation performance. Specifically, in order to facilitate sequence transfer, we align the user's behaviour sequences in the source domain and target domain according to the timestamps, expecting to use the abundant semantics of the former to augment the information of the latter. Regarding confounding factor removal, we introduce the causal learning technique and promote it as an adaptive representation decomposition framework on the basis of instrumental variables. For the sake of alleviating the impact of domain disparities, this paper endeavors to employ two mutually orthogonal transformation matrices for information fusion. Extensive experiments and detailed analyzes on large industrial and public data sets demonstrate that our framework can achieve substantial improvements over state-of-the-art algorithms.
DCGNN: Dual-Channel Graph Neural Network for Social Bot Detection

The importance of social bot detection has been increasingly recognized due to its profound impact on information dissemination. Existing methodologies can be categorized into feature engineering and deep learning-based methods, which mainly focus on static features, e.g., post characteristics and user profiles.However, existing methods often overlook the burst phenomena when distinguishing social bots and genuine users, i.e, the sudden and intense activity or behavior of bots after prolonged inter. Through comprehensive analysis, we find that both burst behavior and static features play pivotal roles in social bot detection. To capture such properties, the dual-channel GNN (DCGNN) is proposed which consists of a burst-aware channel with an adaptive-pass filter and a static-aware channel with a low-pass filter to model user characteristics effectively. Experimental results demonstrate the superiority of this method over competitive baselines.
3MN: Three Meta Networks for Multi-Scenario and Multi-Task Learning in Online Advertising Recommender Systems

Recommender systems are widely applied on web. For example, online advertising systems rely on recommender systems to accurately estimate the value of display opportunities, which is critical to maximize the profits of advertisers. To reduce computational resource consumption, the core tactic of Multi-Scenario Multi-Task Learning (MSMTL) is to devise a single recommder system that is adapted to all contexts instead of implementing multiple scenario-oriented or task-oriented recommender systems. However, MSMTL is challenging because there are complicated task-task, scenario-scenario, and task-scenario interrelations; the characteristic of different tasks in different scenarios also largely varies; and samples of each context are often unevenly distributed. Previous MSMTL solutions focus on applying scenario knowledge to improve the performance of multi-task learning, while neglecting the complicated interrelations among tasks and scenarios. Moreover, samples derived from different scenarios are transferred into the latent embedding with the same dimension. This static embedding strategy impedes the practicality of model expressiveness, since the scenarios with sufficient samples are underrepresented and those with insufficient samples are over-represented.
Grammatical Errors of Google Translate in Translating Narrative Text in Indonesia to English

Many people have recently turned to machine translation to help them translate their documents. However, it is not widely used in the academic field, owing to worries about its trustworthiness in appropriately rendering the source text in the target language. This tool only translates on a word-by-word basis without considering the actual meaning of the text and grammar rules in English. It often applies to the Indonesian structure. So, when students are using Google Translate to translate a text, a new issue arises because it just transfers language without knowing the correct grammar. This research methodology is qualitative research with a content analysis method as the research design. From 5 narrative texts that have been translated by Google Translate from Indonesia to English found that there are two omission errors, four addition errors, six misinformation, and two misordering error and the most common error is misinformation. This study seeks to educate students about grammatical issues that can arise when using Google Translate, allowing them to avoid making the same mistakes and improve their knowledge.
Build Faster with Less: A Journey to Accelerate Sparse Model Building for Semantic Matching in Product Search

The semantic matching problem in product search seeks to retrieve all semantically relevant products given a user query. Recent studies have shown that extreme multi-label classification~(XMC) model enjoys both low inference latency and high recall in real-world scenarios. These XMC semantic matching models adopt TF-IDF vectorizers to extract query text features and use mainly sparse matrices for the model weights. However, limited availability of libraries for efficient parallel sparse modules may lead to tediously long model building time when the problem scales to hundreds of millions of labels. This incurs significant hardware cost and renders the semantic model stale even before it is deployed. In this paper, we investigate and accelerate the model building procedures in a tree-based XMC model. On a real-world semantic matching task with 100M labels, our enhancements achieve over 10 times acceleration (from 3.1 days to 6.7 hours) while reducing hardware cost by 25%.
Graph Enhanced Hierarchical Reinforcement Learning for Goal-oriented Learning Path Recommendation

Goal-oriented Learning path recommendation aims to recommend learning items (concepts or exercises) step-by-step to a learner to promote the mastery level of her specific learning goals. By formulating this task as a Markov decision process, reinforcement learning (RL) methods have demonstrated great power. Although extensive research efforts have been made, previous methods still fail to recommend effective goal-oriented paths due to the under-utilizing of goals. Specifically, it is mainly reflected in two aspects: (1)The lack of goal planning. When learners have multiple goals with different difficulties, the previous methods can't fully utilize the difficulties and dependencies between goal learning items to plan the sequence of achieving these goals, making the path chaotic and inefficient; (2)The lack of efficiency in goal achieving. When pursuing a single goal, the path may contain learning items unrelated to the goal, which makes realizing a certain goal inefficient. To address these challenges, we present a novel Graph Enhanced Hierarchical Reinforcement Learning (GEHRL) framework for goal-oriented learning path recommendation. The framework divides learning path recommendation into two parts: sub-goal selection(planning) and sub-goal achieving(learning item recommendation). Specifically, we employ a high-level agent as a sub-goal selector to select sub-goals for the low-level agent to achieve. The low-level agent in the framework is to recommend learning items to the learner. To make the path only contain goal-related learning items to improve the efficiency of achieving the goal, we develop a graph-based candidate selector to constrain the action space of the low-level agent based on the sub-goal and knowledge graph. We also develop test-based internal reward for low-level training so that the sparsity problem of external reward can be alleviated. Extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.
Understanding User Immersion in Online Short Video Interaction

Short video~(SV) online streaming has been one of the most popular Internet applications in recent years. When browsing SVs, users gradually immerse themselves and derive relaxation or knowledge. Whereas prolonged browsing will lead to a decline in positive feelings, users continue due to inertia, resulting in decreased satisfaction. Immersion is shown to be an essential factor for users' positive experience and highly related to users' interactions in film, games, and virtual reality. However, immersion in SV interaction is still unexplored, which differs from the previously studied scenarios essentially because SV delivery is fragmented, discrete, and with limited time for each video.
Leveraging Post-Click User Behaviors for Calibrated Conversion Rate Prediction Under Delayed Feedback in Online Advertising

Obtaining accurately calibrated conversion rate predictions is essential for the bidding and ranking process in online advertising systems. Nevertheless, the inherent latency between clicks and conversions leads to delayed feedback, which may introduce bias into the prediction models. Compared to indefinitely long conversion delays, post-click user behaviors manifest within a relatively brief time and have been empirically validated to exert a favorable influence on the precision of conversion rate estimates. In light of this, we propose a novel approach that leverages post-click user behaviors to calibrate conversion rate predictions. Specifically, we treat user behaviors as predictable targets to improve accuracy and enhance timeliness. An adaptive loss function based on task uncertainty is employed for multi-task learning. To further reduce calibration error, we integrate the modified prediction model with a parameterized scaling technique. Experiments conducted on two real-world datasets demonstrate that our proposed method outperforms existing models in providing more calibrated predictions.
Search-Efficient Computerized Adaptive Testing

Computerized Adaptive Testing (CAT) arises as a promising personalized test mode in online education, targeting at revealing students' latent knowledge state by selecting test items adaptively. The item selection strategy is the core component of CAT, which searches for the best suitable test item based on students' current estimated ability at each test step. However, existing selection strategies behave in a brute-force manner, which results in the time complexity being linear to the number of items (N) in the item pool, i.e., O(N). Thus, in reality, the search latency becomes the bottleneck for CAT with a large-scale item pool. To this end, we propose a Search-Efficient Computerized Adaptive Testing framework (SECAT), which aims at enhancing CAT with an efficient selection strategy. Specifically, SECAT contains two main phases: item pool indexing and item search. In the item pool indexing phase, we apply a student-aware spatial partition method on the item pool to divide the test items into many sub-spaces, considering the adaptability of test items. In the item search phase, we optimize the traditional single-round search strategy with the asymptotic theory and propose a multi-round search strategy that can further improve the time efficiency. Compared with existing strategies, the time complexity of SECAT decreases from O(N) to O(logN). Across two real-world datasets, SECAT achieves over 200x speed up with negligible accuracy degradation.