Large Language Models (LLMs) have been extensively applied in various recommendation scenarios, including bundle generation, thanks to their exceptional reasoning capabilities and comprehensive knowledge. However, exploiting large-scale LLMs for bundle generation introduces significant efficiency challenges—primarily high computational costs during fine-tuning and inference due to their massive parameterization. Knowledge Distillation (KD) offers a promising solution by transferring expertise from large teacher models to more compact student models. This study systematically investigates KD approaches for bundle generation with the goal of minimizing computational demands while preserving performance. Specifically, we explore three critical research questions: (1) how does the format of distilled knowledge impact bundle generation performance? (2) to what extent does the quantity of distilled knowledge influence the performance? and (3) how do different ways of utilizing the distilled knowledge affect the performance? To support this investigation, we propose a comprehensive KD framework that (i) progressively extracts knowledge from raw data in increasingly complex forms, i.e., frequent patterns \(\rightarrow\) formalized rules \(\rightarrow\) deep thoughts; (ii) captures varying quantities of distilled knowledge through different sampling strategies, multi-domain accumulation, and multi-format aggregation; and (iii) exploits complementary LLM adaptation techniques—in-context learning, supervised fine-tuning, and their combination—to leverage the distilled knowledge for domain-specific adaptation and enhanced efficiency in small student models. Through extensive experiments on multiple real-world datasets, we provide valuable insights into how knowledge format, quantity, and utilization methods collectively shape the performance of LLM-based bundle generation, which exhibits the significant potential of KD for more efficient yet effective LLM-based bundle generation.

Relation extraction methods are currently dominated by deep neural models, which capture complex statistical patterns while being brittle and vulnerable to perturbations in data and distribution. Explainability techniques offer a means for understanding such vulnerabilities, and thus represent an opportunity to mitigate future errors; yet, existing methods are limited to describing what the model 'knows', while totally failing at explaining what the model does not know. This paper presents a new method for diagnosing model predictions and detecting potential inaccuracies. Our approach involves breaking down the problem into two components: (i) determining the necessary knowledge the model should possess for accurate prediction, through human annotations, and (ii) assessing the actual knowledge possessed by the model, using explainable AI methods (XAI). We apply our method to several relation extraction tasks and conduct an empirical study leveraging human specifications of what a model should know and does not know. Results show that human workers are capable of accurately specifying the model should-knows, despite variations in the specification, that the alignment between what a model really knows and what it should know is indeed indicative of model accuracy, and that the unknowns identified through our methods allow to foresee future errors that may or may not have been observed otherwise.

Most existing bundle generation approaches fall short in generating fixed-size bundles. Furthermore, they often neglect the underlying user intents reflected by the bundles in the generation process, resulting in less intelligible bundles. This paper addresses these limitations through the exploration of two interrelated tasks, i.e., personalized bundle generation and the underlying intent inference, based on different user sessions. Inspired by the reasoning capabilities of large language models (LLMs), we propose an adaptive in-context learning paradigm, which allows LLMs to draw tailored lessons from related sessions as demonstrations, enhancing the performance on target sessions. Specifically, we first employ retrieval augmented generation to identify nearest neighbor sessions, and then carefully design prompts to guide LLMs in executing both tasks on these neighbor sessions. To tackle reliability and hallucination challenges, we further introduce (1) a self-correction strategy promoting mutual improvements of the two tasks without supervision signals and (2) an auto-feedback mechanism for adaptive supervision based on the distinct mistakes made by LLMs on different neighbor sessions. Thereby, the target session can gain customized lessons for improved performance by observing the demonstrations of its neighbor sessions. Experiments on three real-world datasets demonstrate the effectiveness of our proposed method.

Modern Knowledge Graphs (KGs) are inevitably noisy due to the nature of their construction process. Existing robust learning techniques for noisy KGs mostly focus on triple facts, where the fact-wise confidence is straightforward to evaluate. However, hyper-relational facts, where an arbitrary number of key-value pairs are associated with a base triplet, have become increasingly popular in modern KGs, but significantly complicate the confidence assessment of the fact. Against this background, we study the problem of robust link prediction over noisy hyper-relational KGs, and propose NYLON, a \underlineN oise-resistant h\underlineY per-re\underlineL ati\underlineON al link prediction technique via active crowd learning. Specifically, beyond the traditional fact-wise confidence, we first introduce element-wise confidence measuring the fine-grained confidence of each entity or relation of a hyper-relational fact. We connect the element- and fact-wise confidences via a "least confidence'' principle to allow efficient crowd labeling. NYLON is then designed to systematically integrate three key components, where a hyper-relational link predictor uses the fact-wise confidence for robust prediction, a cross-grained confidence evaluator predicts both element- and fact-wise confidences, and an effort-efficient active labeler selects informative facts for crowd annotators to label using an efficient labeling mechanism guided by the element-wise confidence under the "least confidence'' principle and further followed by data augmentation. We evaluate NYLON on three real-world KG datasets against a sizeable collection of baselines. Results show that NYLON achieves superior and robust performance in both link prediction and error detection tasks on noisy KGs, and outperforms best baselines by 2.42-10.93% and 3.46-10.65% in the two tasks, respectively.

Product bundling represents a prevalent marketing strategy in both offline stores and e-commerce systems. Despite its widespread use, previous studies on bundle recommendation face two significant limitations. Firstly, they rely on noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session. Secondly, they target specific tasks by holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. This paper proposes to take a step back and considers the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich metadata, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion and ranking, to explanation and auto-naming, whereby 19 research questions are raised correspondingly to guide the analysis. Finally, we conduct extensive experiments and analyses with representative recommendation models and large language models (LLMs), demonstrating the challenges and opportunities, especially with the emergence of LLMs. To summarize, our study contributes by introducing novel data sources, paving the way for new research avenues, and offering insights to guide product bundling in real e-commerce platforms.

The First Workshop on Personalized Generative AI1 aims to be a cornerstone event fostering innovation and collaboration in the dynamic field of personalized AI. Leveraging the potent capabilities of Large Language Models (LLMs) to enhance user experiences with tailored responses and recommendations, the workshop is designed to address a range of pressing challenges including knowledge gap bridging, hallucination mitigation, and efficiency optimization in handling extensive user profiles. As a nexus for academics and industry professionals, the event promises rich discussions on a plethora of topics such as the development and fine-tuning of foundational models, strategies for multi-modal personalization, and the imperative ethical and privacy considerations in LLM deployment. Through a curated series of keynote speeches, insightful panel discussions, and hands-on sessions, the workshop aspires to be a catalyst in the development of more precise, contextually relevant, and user-centric AI systems. It aims to foster a landscape where generative AI systems are not only responsive but also anticipatory of individual user needs, marking a significant stride in personalized experiences.

Evaluating design ideas is necessary to predict their success and assess their impact early on in the process. Existing methods rely either on metrics computed by systems that are effective but subject to errors and bias, or experts’ ratings, which are accurate but expensive and long to collect. Crowdsourcing offers a compelling way to evaluate a large number of design ideas in a short amount of time while being cost-effective. Workers’ evaluation is, however, less reliable and might substantially differ from experts’ evaluation.

Platforms such as Twitter are increasingly being used for real-world event detection. Recent work often leverages event-related keywords for training machine learning based event detection models. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation – and use it as a posterior regularization parameter during model training. Such approaches are, however, limited by the informativeness of the keywords and by the accuracy of the expectation estimation for keywords. In this work, we introduce a human-in-the-loop approach to jointly discover informative rules for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both rule-specific expectation and the disagreement between the crowd and the model in order to discover new rules that are most beneficial for model training. To identify such rules, we introduce a hybrid human-machine workflow that engages human workers in rule discovery through an interactive hypothesis creation and testing interface and leverages automatic methods for suggesting useful rules for human verification. We empirically demonstrate the merits of our approach, on multiple real-world datasets and show that our approach improves the state of the art by a margin of 25.63% in terms of AUC.

Noisy labels represent one of the key issues in supervised machine learning. Existing work for label noise reduction mainly takes a probabilistic approach that infers true labels from data distributions in low-level feature spaces. Such an approach is not only limited by its capability to learn high-quality data representations, but also by the low predictive power of data distributions in inferring true classes. To address those problems, we introduce Nessy, a neuro-symbolic system that integrates deep probabilistic modeling and symbolic knowledge for label noise reduction. Our deep probabilistic model infers the true classes of data instances with noisy labels by exploiting data distributions in an underlying latent feature representation space. For data instances where inference is not reliable enough, Nessy extracts symbolic rules and ranks them according to several utility metrics. Top-ranking rules are injected into the deep probabilistic model via expectation regularization, i.e., via a posterior regularization term constraining the class distribution in the objective function. In a real deployment over multiple relation extraction tasks, we demonstrate that Nessy is able to significantly improve the state of the art, by 7% accuracy and 10.7% AUC on average.

Graph embeddings have become a key paradigm to learn node representations and facilitate downstream graph analysis tasks. Many real-world scenarios such as online social networks and communication networks involve streaming graphs, where edges connecting nodes are continuously received in a streaming manner, making the underlying graph structures evolve over time. Such a streaming graph raises great challenges for graph embedding techniques not only in capturing the structural dynamics of the graph, but also in efficiently accommodating high-speed edge streams. Against this background, we propose SGSketch, a highly-efficient streaming graph embedding technique via incremental neighborhood sketching. SGSketch cannot only generate high-quality node embeddings from a streaming graph by gradually forgetting outdated streaming edges, but also efficiently update the generated node embeddings via an incremental embedding updating mechanism. Our extensive evaluation compares SGSketch against a sizable collection of state-of-the-art techniques using both synthetic and real-world streaming graphs. The results show that SGSketch achieves superior performance on different graph analysis tasks, showing 31.9% and 21.9% improvement on average over the best-performing static and dynamic graph embedding baselines, respectively. Moreover, SGSketch is significantly more efficient in both embedding learning and incremental embedding updating processes, showing 54x-1813x and 118x-1955x speedup over the baseline techniques, respectively.

Although data quality is a long-standing and enduring problem, it has recently received a resurgence of attention due to the fast proliferation of data analytics, machine learning, and decision-support applications built upon the wide-scale availability and accessibility of (big) data. The success of such applications heavily relies on not only the quantity, but also the quality of data. Data curation, which may include annotation, cleaning, transformation, integration, etc., is a critical step to provide adequate assurances on the quality of analytics and machine learning results. Such data preparation activities are recognised as time and resource intensive for data scientists as data often comes with a number of challenges that need to be tackled before it can be used in practice. Data re-purposing and the resulting distance between design and use intentions of the data, is a fundamental issue behind many of these challenges. These challenges include a variety of data issues such as noise and outliers, incompleteness, representativeness or biases, heterogeneity of format or semantics, etc. Mishandling these challenges can lead to negative and sometimes damaging effects, especially in critical domains like healthcare, transport, and finance. An observable distinct feature of data quality in these contexts is the increasingly important role played by humans, being often the source of data generation and the active players in data curation. This workshop will provide an opportunity to explore the interdisciplinary overlap between manual, automated, and hybrid human-machine methods of data curation.

Explaining the behaviour of Artificial Intelligence models has become a necessity. Their opaqueness and fragility are not tolerable in high-stakes domains especially. Although considerable progress is being made in the field of Explainable Artificial Intelligence, scholars have demonstrated limits and flaws of existing approaches: explanations requiring further interpretation, non-standardised explanatory format, and overall fragility. In light of this fragmentation, we turn to the field of philosophy of science to understand what constitutes a good explanation, that is, a generalisation that covers both the actual outcome and, possibly multiple, counterfactual outcomes. Inspired by this, we propose CHIME: a human-in-the-loop, post-hoc approach focused on creating such explanations by establishing the causal features in the input. We first elicit people's cognitive abilities to understand what parts of the input the model might be attending to. Then, through Causal Discovery we uncover the underlying causal graph relating the different concepts. Finally, with such a structure, we compute the causal effects different concepts have towards a model's outcome. We evaluate the Fidelity, Coherence, and Accuracy of the explanations obtained with CHIME with respect to two state-of-the-art Computer Vision models trained on real-world image data sets. We found evidence that the explanations reflect the causal concepts tied to a model's prediction, both in terms of causal strength and accuracy.

With recent advances in explainable artificial intelligence (XAI), researchers have started to pay attention to concept-level explanations, which explain model predictions with a high level of abstraction. However, such explanations may be difficult to digest for laypeople due to the potential knowledge gap and the concomitant cognitive load. Inspired by recent work, we argue that analogy-based explanations composed of commonsense knowledge may be a potential solution to tackle this issue. In this paper, we propose analogical inference as a bridge to help end-users leverage their commonsense knowledge to better understand the concept-level explanations. Specifically, we design an effective analogy-based explanation generation method and collect 600 analogy-based explanations from 100 crowd workers. Furthermore, we propose a set of structured dimensions for the qualitative assessment of analogy-based explanations and conduct an empirical evaluation of the generated analogies with experts. Our findings reveal significant positive correlations between the qualitative dimensions of analogies and the perceived helpfulness of analogy-based explanations. These insights can inform the design of future methods for the generation of effective analogy-based explanations. We also find that the understanding of commonsense explanations varies with the experience of the recipient user, which points out the need for further work on personalization when leveraging commonsense explanations.

Product bundling is a commonly-used marketing strategy in both offline retailers and online e-commerce systems. Current research on bundle recommendation is limited by: (1) noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session; and (2) specific tasks, holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. In this paper, we propose to take a step back and consider the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich meta information, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion, ranking, to explanation and auto-naming. Finally, we conduct extensive experiments and in-depth analysis that demonstrate the challenges of bundle recommendation, arising from the need for capturing complex relations among users, products, and bundles, as well as the research opportunities, especially in graph-based neural methods. To sum up, our study delivers new data sources, opens up new research directions, and provides useful guidance for product bundling in real e-commerce platforms. Our datasets are available at GitHub (\urlhttps://github.com/BundleRec/bundle_recommendation ).

Access to commonsense knowledge is receiving renewed interest for developing neuro-symbolic AI systems, or debugging deep learning models. Little is currently understood about the types of knowledge that can be gathered using existing knowledge elicitation methods. Moreover, these methods fall short of meeting the evolving requirements of several downstream AI tasks. To this end, collecting broad and tacit knowledge, in addition to negative or discriminative knowledge can be highly useful. Addressing this research gap, we developed a novel game with a purpose, ‘FindItOut’, to elicit different types of knowledge from human players through easily configurable game mechanics. We recruited 125 players from a crowdsourcing platform, who played 2430 rounds, resulting in the creation of more than 150k tuples of knowledge. Through an extensive evaluation of these tuples, we show that FindItOut can successfully result in the creation of plural knowledge with a good player experience. We evaluate the efficiency of the game (over 10 × higher than a reference baseline) and the usefulness of the resulting knowledge, through the lens of two downstream tasks — commonsense question answering and the identification of discriminative attributes. Finally, we present a rigorous qualitative analysis of the tuples’ characteristics, that informs the future use of FindItOut across various researcher and practitioner communities.

Unknown unknowns represent a major challenge in reliable image recognition. Existing methods mainly focus on unknown unknowns identification, leveraging human intelligence to gather images that are potentially difficult for the machine. To drive a deeper understanding of unknown unknowns and more effective identification and treatment, this paper focuses on unknown unknowns characterization. We introduce a human-in-the-loop, semantic analysis framework for characterizing unknown unknowns at scale. We engage humans in two tasks that specify what a machine should know and describe what it really knows, respectively, both at the conceptual level, supported by information extraction and machine learning interpretability methods. Data partitioning and sampling techniques are employed to scale out human contributions in handling large data. Through extensive experimentation on scene recognition tasks, we show that our approach provides a rich, descriptive characterization of unknown unknowns and allows for more effective and cost-efficient detection than the state of the art.

Location-Based Social Networks (LBSNs) have been widely used as a primary data source for studying the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users’ mobility homophily and social proximity, and show that mobility and social features can help friendship and location prediction tasks, respectively. However, these hand-crafted features not only require tedious human efforts, but also are difficult to generalize. Against this background, we propose in this paper LBSN2Vec++, a heterogeneous hypergraph embedding approach designed specifically for LBSN data for automatic feature learning. Specifically, LBSN data intrinsically forms a heterogeneous hypergraph including both user-user homogeneous edges (friendships) and user-time-POI-semantic heterogeneous hyperedges (check-ins). Based on this hypergraph, we first propose a random-walk-with-stay scheme to jointly sample user check-ins and social relationships, and then learn node embeddings from the sampled (hyper)edges by not only preserving the$n$-wise node proximity captured by the hyperedges, but also considering embedding space transformation between node domains to fully grasp the complex structural characteristics of the LBSN heterogeneous hypergraph. Using real-world LBSN datasets collected in six cities all over the world, our extensive evaluation shows that LBSN2Vec++ significantly and consistently outperforms both state-of-the-art graph embedding techniques by up to 68 percent and the best-performing hand-crafted features in the literature by up to 70.14 percent on friendship and location prediction tasks.

Music content annotation campaigns are common on paid crowdsourcing platforms. Crowd workers are expected to annotate complicated music artefacts, which can demand certain skills and expertise. Traditional methods of participant selection are not designed to capture these kind of domain-specific skills and expertise, and often domain-specific questions fall under the general demographics category. Despite the popularity of such tasks, there is a general lack of deeper understanding of the distribution of musical properties - especially auditory perception skills - among workers. To address this knowledge gap, we conducted a user study (N=100) on Prolific. We asked workers to indicate their musical sophistication through a questionnaire and assessed their music perception skills through an audio-based skill test. The goal of this work is to better understand the extent to which crowd workers possess higher perceptions skills, beyond their own musical education level and self reported abilities. Our study shows that untrained crowd workers can possess high perception skills on the music elements of melody, tuning, accent and tempo; skills that can be useful in a plethora of annotation tasks in the music domain.

Scientific peer review is pivotal to maintain quality standards for academic publication. The effectiveness of the reviewing process is currently being challenged by the rapid increase of paper submissions in various conferences. Those venues need to recruit a large number of reviewers of different levels of expertise and background. The submitted reviews often do not meet the conformity standards of the conferences. Such a situation poses an ever-bigger burden on the meta-reviewers when trying to reach a final decision.

Global interpretability is a vital requirement for image classification applications. Existing interpretability methods mainly explain a model behavior by identifying salient image patches, which require manual efforts from users to make sense of, and also do not typically support model validation with questions that investigate multiple visual concepts. In this paper, we introduce a scalable human-in-the-loop approach for global interpretability. Salient image areas identified by local interpretability methods are annotated with semantic concepts, which are then aggregated into a tabular representation of images to facilitate automatic statistical analysis of model behavior. We show that this approach answers interpretability needs for both model validation and exploration, and provides semantically more diverse, informative, and relevant explanations while still allowing for scalable and cost-efficient execution.

In online shopping quality is a key consideration when purchasing an item. Since customers cannot physically touch or try out an item before buying it, they must assess its quality from information gathered online. In a typical eCommerce setting, the customer is presented with seller-generated content from the product catalog, such as an image of the product, a textual description, and lists or comparisons of attributes. In addition to catalog attributes, customers often have access to customer-generated content such as reviews and product questions and answers. In a crowdsourced study, we asked crowd workers to compare product pairs from kitchen, electronics, home, beauty and office categories. In a side-by-side comparison, we asked them to choose the product that is higher quality, and further to identify the attributes that contributed to their judgment, where the attributes were both seller-generated and customer-generated. We find that customers tend to perceive more expensive items as higher quality but that their purchase decisions are uncorrelated with quality, suggesting that customers seek a trade-off between price and quality when making purchase decisions. Crowd workers placed a higher value on attributes derived from customer-generated content such as reviews than on catalog attributes. Among the catalog attributes, brand, item material and pack size were most often selected. Finally, attributes with a low correlation with perceived quality are nonetheless useful in predicting purchases in a machine-learned system.

With tremendous amount of recommendation algorithms proposed every year, one critical issue has attracted a considerable amount of attention: there are no effective benchmarks for evaluation, which leads to two major concerns, i.e., unreproducible evaluation and unfair comparison. This paper aims to conduct rigorous (i.e., reproducible and fair) evaluation for implicit-feedback based top-N recommendation algorithms. We first systematically review 85 recommendation papers published at eight top-tier conferences (e.g., RecSys, SIGIR) to summarize important evaluation factors, e.g., data splitting and parameter tuning strategies, etc. Through a holistic empirical study, the impacts of different factors on recommendation performance are then analyzed in-depth. Following that, we create benchmarks with standardized procedures and provide the performance of seven well-tuned state-of-the-arts across six metrics on six widely-used datasets as a reference for later study. Additionally, we release a user-friendly Python toolkit, which differs from existing ones in addressing the broad scope of rigorous evaluation for recommendation. Overall, our work sheds light on the issues in recommendation evaluation and lays the foundation for further investigation. Our code and datasets are available at GitHub (https://github.com/AmazingDD/daisyRec).

Finding social influencers is a fundamental task in many online applications ranging from brand marketing to opinion mining. Existing methods heavily rely on the availability of expert labels, whose collection is usually a laborious process even for domain experts. Using open-ended questions, crowdsourcing provides a cost-effective way to find a large number of social influencers in a short time. Individual crowd workers, however, only possess fragmented knowledge that is often of low quality.

Neural networks have recently been shown to be highly effective at predicting links for constructing knowledge graphs. Existing research has mainly focused on designing 1) deep neural network models that are expressive in capturing fine-grained semantics, e.g., NTN and ConvE, but that are however less scalable; or 2) shallow models that are scalable, e.g., TransE and DistMult, yet limited in capturing expressive semantic features. In this work, we demonstrate that we can get the best of both worlds while drastically reducing the amount of data needed to train a deep network by leveraging active learning.

Location Based Social Networks (LBSNs) have been widely used as a primary data source to study the impact of mobility and social relationships on each other. Traditional approaches manually define features to characterize users' mobility homophily and social proximity, and show that mobility and social features can help friendship and location prediction tasks, respectively. However, these hand-crafted features not only require tedious human efforts, but also are difficult to generalize. In this paper, by revisiting user mobility and social relationships based on a large-scale LBSN dataset collected over a long-term period, we propose LBSN2Vec, a hypergraph embedding approach designed specifically for LBSN data for automatic feature learning. Specifically, LBSN data intrinsically forms a hypergraph including both user-user edges (friendships) and user-time-POI-semantic hyperedges (check-ins). Based on this hypergraph, we first propose a random-walk-with-stay scheme to jointly sample user check-ins and social relationships, and then learn node embeddings from the sampled (hyper)edges by preserving n-wise node proximity (n = 2 or 4). Our evaluation results show that LBSN2Vec both consistently and significantly outperforms the state-of-the-art graph embedding methods on both friendship and location prediction tasks, with an average improvement of 32.95% and 25.32%, respectively. Moreover, using LBSN2Vec, we discover the asymmetric impact of mobility and social relationships on predicting each other, which can serve as guidelines for future research on friendship and location prediction in LBSNs.

This paper presents Scalpel-CD, a first-of-its-kind system that leverages both human and machine intelligence to debug noisy labels from the training data of machine learning systems. Our system identifies potentially wrong labels using a deep probabilistic model, which is able to infer the latent class of a high-dimensional data instance by exploiting data distributions in the underlying latent feature space. To minimize crowd efforts, it employs a data sampler which selects data instances that would benefit the most from being inspected by the crowd. The manually verified labels are then propagated to similar data instances in the original training data by exploiting the underlying data structure, thus scaling out the contribution from the crowd. Scalpel-CD is designed with a set of algorithmic solutions to automatically search for the optimal configurations for different types of training data, in terms of the underlying data structure, noise ratio, and noise types (random vs. structural). In a real deployment on multiple machine learning tasks, we demonstrate that Scalpel-CD is able to improve label quality by 12.9% with only 2.8% instances inspected by the crowd.

Crowdworker online communities — operating in fora like mTurkForum and TurkerNation — are an important actor in microwork markets. Albeit central to market dynamics, how the behavior of crowdworker communities and the dynamics of online marketplaces influence each other is yet to be understood. To provide quantitative evidence of such influence, we performed an analysis on 6-years worth of mTurk market activities and community discussions in six fora. We investigated the nature of the relationships that exist between activities in fora, tasks published in mTurk, requesters for such tasks, and task completion speed. We validate -- and expand upon — results from previous work by showing that (i) there are differences between market demand and community activities that are specific to fora and task types; (ii) the temporal progression of HIT availability in the market is predictive of the upcoming amount of crowdworker discussions, with significant differences across fora and discussion categories; (iii) activities in fora can have a significant positive impact on the completion speed of tasks available in the market.

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing methods mainly rely on hand-engineered features from KGs (e.g., meta paths), which requires domain knowledge. This paper presents RKGE, a KG embedding approach that automatically learns semantic representations of both entities and paths between entities for characterizing user preferences towards items. Specifically, RKGE employs a novel recurrent network architecture that contains a batch of recurrent networks to model the semantics of paths linking a same entity pair, which are seamlessly fused into recommendation. It further employs a pooling operator to discriminate the saliency of different paths in characterizing user preferences towards items. Extensive validation on real-world datasets shows the superiority of RKGE against state-of-the-art methods. Furthermore, we show that RKGE provides meaningful explanations for recommendation results.

This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, where multiple annotators with unknown expertise contribute an uncontrolled amount (often limited) of annotations. Our framework leverages the low-rank structure in annotations to learn individual annotator expertise, which then helps to infer the true labels from noisy and sparse annotations. It provides a unified Bayesian model to simultaneously infer the true labels and train the deep learning model in order to reach an optimal learning efficacy. Finally, our framework exploits the uncertainty of the deep learning model during prediction as well as the annotators» estimated expertise to minimize the number of required annotations and annotators for optimally training the deep learning model. We evaluate the effectiveness of our framework for intent classification in Alexa (Amazon»s personal assistant), using both synthetic and real-world datasets. Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches. We further discuss the potential of our proposed framework in bridging machine learning and crowdsourcing towards improved human-in-the-loop systems.

Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.

The "International Workshop on Recommender Systems for Citizens" (CitRec) is focused on a novel type of recommender systems both in terms of ownership and purpose: recommender systems run by citizens and serving society as a whole.

Complexity is crucial to characterize tasks performed by humans through computer systems. Yet, the theory and practice of crowdsourcing currently lacks a clear understanding of task complexity, hindering the design of effective and efficient execution interfaces or fair monetary rewards. To understand how complexity is perceived and distributed over crowdsourcing tasks, we instrumented an experiment where we asked workers to evaluate the complexity of 61 real-world re-instantiated crowdsourcing tasks. We show that task complexity, while being subjective, is coherently perceived across workers; on the other hand, it is significantly influenced by task type. Next, we develop a high-dimensional regression model, to assess the influence of three classes of structural features (metadata, content, and visual) on task complexity, and ultimately use them to measure task complexity. Results show that both the appearance and the language used in task description can accurately predict task complexity. Finally, we apply the same feature set to predict task performance, based on a set of 5 years-worth tasks in Amazon MTurk. Results show that features related to task complexity can improve the quality of task performance prediction, thus demonstrating the utility of complexity as a task modeling property.

Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent and in a same level. However, auxiliary features are often organized in rich knowledge structures (e.g. hierarchy) to describe their relationships. In this paper, we propose a novel matrix factorization framework with recursive regularization -- ReMF, which jointly models and learns the influence of hierarchically-organized features on user-item interactions, thus to improve recommendation accuracy. It also provides characterization of how different features in the hierarchy co-influence the modeling of user-item interactions. Empirical results on real-world data sets demonstrate that ReMF consistently outperforms state-of-the-art feature-based recommendation methods.