pwc-archive/papers-with-abstracts huggingface dataset:
Dimension Reduction and MARS
Authors: Yu Liu, Degui Li, Yingcun Xia
Date: 2023-02-11 00:00:00
Tasks: Dimensionality Reduction, regression

The multivariate adaptive regression spline (MARS) is one of the popular estimation methods for nonparametric multivariate regressions. However, as MARS is based on marginal splines, to incorporate interactions of covariates, products of the marginal splines must be used, which leads to an unmanageable number of basis functions when the order of interaction is high and results in low estimation efficiency. In this paper, we improve the performance of MARS by using linear combinations of the covariates which achieve sufficient dimension reduction. The special basis functions of MARS facilitate calculation of gradients of the regression function, and estimation of the linear combinations is obtained via eigen-analysis of the outer-product of the gradients. Under some technical conditions, the asymptotic theory is established for the proposed estimation method. Numerical studies including both simulation and empirical applications show its effectiveness in dimension reduction and improvement over MARS and other commonly-used nonparametric methods in regression estimation and prediction.
Confidence and Uncertainty Assessment for Distributional Random Forests
Authors: Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen
Date: 2023-02-11 00:00:00

The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions. Due to its general estimation procedure, it can be employed to estimate a wide range of targets such as conditional average treatment effects, conditional quantiles, and conditional correlations. However, only results about the consistency and convergence rate of the DRF prediction are available so far. We characterize the asymptotic distribution of DRF and develop a bootstrap approximation of it. This allows us to derive inferential tools for quantifying standard errors and the construction of confidence regions that have asymptotic coverage guarantees. In simulation studies, we empirically validate the developed theory for inference of low-dimensional targets and for testing distributional differences between two populations.
Windowed Fourier Analysis for Signal Processing on Graph Bundles
Authors: T. Mitchell Roddenberry, Santiago Segarra
Date: 2023-02-11 00:00:00
Tasks: Unity

We consider the task of representing signals supported on graph bundles, which are generalizations of product graphs that allow for "twists" in the product structure. Leveraging the localized product structure of a graph bundle, we demonstrate how a suitable partition of unity over the base graph can be used to lift the signal on the graph into a space where a product factorization can be readily applied. Motivated by the locality of this procedure, we demonstrate that bases for the signal spaces of the components of the graph bundle can be lifted in the same way, yielding a basis for the signal space of the total graph. We demonstrate this construction on synthetic graphs, as well as with an analysis of the energy landscape of conformational manifolds in stereochemistry.
SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes
Authors: Minghan Li, Sheng-Chieh Lin, Xueguang Ma, Jimmy Lin
Date: 2023-02-13 00:00:00
Tasks: Information Retrieval, Retrieval

This paper introduces Sparsified Late Interaction for Multi-vector (SLIM) retrieval with inverted indexes. Multi-vector retrieval methods have demonstrated their effectiveness on various retrieval datasets, and among them, ColBERT is the most established method based on the late interaction of contextualized token embeddings of pre-trained language models. However, efficient ColBERT implementations require complex engineering and cannot take advantage of off-the-shelf search libraries, impeding their practical use. To address this issue, SLIM first maps each contextualized token vector to a sparse, high-dimensional lexical space before performing late interaction between these sparse token embeddings. We then introduce an efficient two-stage retrieval architecture that includes inverted index retrieval followed by a score refinement module to approximate the sparsified late interaction, which is fully compatible with off-the-shelf lexical search libraries such as Lucene. SLIM achieves competitive accuracy on MS MARCO Passages and BEIR compared to ColBERT while being much smaller and faster on CPUs. To our knowledge, we are the first to explore using sparse token representations for multi-vector retrieval. Source code and data are integrated into the Pyserini IR toolkit.
Recommending to Strategic Users
Authors: Andreas Haupt, Dylan Hadfield-Menell, Chara Podimata
Date: 2023-02-13 00:00:00
Tasks: Recommendation Systems

Recommendation systems are pervasive in the digital economy. An important assumption in many deployed systems is that user consumption reflects user preferences in a static sense: users consume the content they like with no other considerations in mind. However, as we document in a large-scale online survey, users do choose content strategically to influence the types of content they get recommended in the future. We model this user behavior as a two-stage noisy signalling game between the recommendation system and users: the recommendation system initially commits to a recommendation policy, presents content to the users during a cold start phase which the users choose to strategically consume in order to affect the types of content they will be recommended in a recommendation phase. We show that in equilibrium, users engage in behaviors that accentuate their differences to users of different preference profiles. In addition, (statistical) minorities out of fear of losing their minority content exposition may not consume content that is liked by mainstream users. We next propose three interventions that may improve recommendation quality (both on average and for minorities) when taking into account strategic consumption: (1) Adopting a recommendation system policy that uses preferences from a prior, (2) Communicating to users that universally liked ("mainstream") content will not be used as basis of recommendation, and (3) Serving content that is personalized-enough yet expected to be liked in the beginning. Finally, we describe a methodology to inform applied theory modeling with survey results.
Evaluation of a Search Interface for Preference-Based Ranking -- Measuring User Satisfaction and System Performance
Authors: Dagmar Kern, Wilko van Hoek, Daniel Hienert
Date: 2023-02-13 00:00:00

Finding a product online can be a challenging task for users. Faceted search interfaces, often in combination with recommenders, can support users in finding a product that fits their preferences. However, those preferences are not always equally weighted: some might be more important to a user than others (e.g. red is the favorite color, but blue is also fine) and sometimes preferences are even contradictory (e.g. the lowest price vs. the highest performance). Often, there is even no product that meets all preferences. In those cases, faceted search interfaces reach their limits. In our research, we investigate the potential of a search interface, which allows a preference-based ranking based on weighted search and facet terms. We performed a user study with 24 participants and measured user satisfaction and system performance. The results show that with the preference-based search interface the users were given more alternatives that best meet their preferences and that they are more satisfied with the selected product than with a search interface using standard facets. Furthermore, in this work we study the relationship between user satisfaction and search precision within the whole search session and found first indications that there might be a relation between them.
UNDR: User-Needs-Driven Ranking of Products in E-Commerce
Authors: Andrea Papenmeier, Daniel Hienert, Firas Sabbah, Norbert Fuhr, Dagmar Kern
Date: 2023-02-13 00:00:00

Online retailers often offer a vast choice of products to their customers to filter and browse through. The order in which the products are listed depends on the ranking algorithm employed in the online shop. State-of-the-art ranking methods are complex and draw on many different information, e.g., user query and intent, product attributes, popularity, recency, reviews, or purchases. However, approaches that incorporate user-generated data such as click-through data, user ratings, or reviews disadvantage new products that have not yet been rated by customers. We therefore propose the User-Needs-Driven Ranking (UNDR) method that accounts for explicit customer needs by using facet popularity and facet value popularity. As a user-centered approach that does not rely on post-purchase ratings or reviews, our method bypasses the cold-start problem while still reflecting the needs of an average customer. In two preliminary user studies, we compare our ranking method with a rating-based ranking baseline. Our findings show that our proposed approach generates a ranking that fits current customer needs significantly better than the baseline. However, a more fine-grained usage-specific ranking did not further improve the ranking.
Dataset of Natural Language Queries for E-Commerce
Authors: Andrea Papenmeier, Dagmar Kern, Daniel Hienert, Alfred Sliwa, Ahmet Aker, Norbert Fuhr
Date: 2023-02-13 00:00:00
Tasks: Conversational Search, Information Retrieval, Natural Language Queries, Retrieval

Shopping online is more and more frequent in our everyday life. For e-commerce search systems, understanding natural language coming through voice assistants, chatbots or from conversational search is an essential ability to understand what the user really wants. However, evaluation datasets with natural and detailed information needs of product-seekers which could be used for research do not exist. Due to privacy issues and competitive consequences, only few datasets with real user search queries from logs are openly available. In this paper, we present a dataset of 3,540 natural language queries in two domains that describe what users want when searching for a laptop or a jacket of their choice. The dataset contains annotations of vague terms and key facts of 1,754 laptop queries. This dataset opens up a range of research opportunities in the fields of natural language processing and (interactive) information retrieval for product search.
Starting Conversations with Search Engines -- Interfaces that Elicit Natural Language Queries
Authors: Andrea Papenmeier, Dagmar Kern, Daniel Hienert, Alfred Sliwa, Ahmet Aker, Norbert Fuhr
Date: 2023-02-13 00:00:00
Tasks: Chatbot, Information Retrieval, Natural Language Queries, Retrieval

Search systems on the Web rely on user input to generate relevant results. Since early information retrieval systems, users are trained to issue keyword searches and adapt to the language of the system. Recent research has shown that users often withhold detailed information about their initial information need, although they are able to express it in natural language. We therefore conduct a user study (N = 139) to investigate how four different design variants of search interfaces can encourage the user to reveal more information. Our results show that a chatbot-inspired search interface can increase the number of mentioned product attributes by 84% and promote natural language formulations by 139% in comparison to a standard search bar interface.
DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation
Authors: Feng Zhu, Mingjie Zhong, Xinxing Yang, Longfei Li, Lu Yu, Tiehua Zhang, Jun Zhou, Chaochao Chen, Fei Wu, Guanfeng Liu, Yan Wang
Date: 2023-02-13 00:00:00
Tasks: counterfactual, Multi-Task Learning, Selection bias

In recommendation scenarios, there are two long-standing challenges, i.e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks. To cope with these issues, existing works emphasize on leveraging Multi-Task Learning (MTL) frameworks (Category 1) or causal debiasing frameworks (Category 2) to incorporate more auxiliary data in the entire exposure/inference space D or debias the selection bias in the click/training space O. However, these two kinds of solutions cannot effectively address the not-missing-at-random problem and debias the selection bias in O to fit the inference in D. To fill the research gaps, we propose a Direct entire-space Causal Multi-Task framework, namely DCMT, for post-click conversion prediction in this paper. Specifically, inspired by users' decision process of conversion, we propose a new counterfactual mechanism to debias the selection bias in D, which can predict the factual CVR and the counterfactual CVR under the soft constraint of a counterfactual prior knowledge. Extensive experiments demonstrate that our DCMT can improve the state-of-the-art methods by an average of 1.07% in terms of CVR AUC on the five offline datasets and 0.75% in terms of PV-CVR on the online A/B test (the Alipay Search). Such improvements can increase millions of conversions per week in real industrial applications, e.g., the Alipay Search.
On Modeling Long-Term User Engagement from Stochastic Feedback
Authors: Guoxi Zhang, Xing Yao, Xuanji Xiao
Date: 2023-02-13 00:00:00
Tasks: Recommendation Systems, Reinforcement Learning (RL), Sequential Recommendation

An ultimate goal of recommender systems (RS) is to improve user engagement. Reinforcement learning (RL) is a promising paradigm for this goal, as it directly optimizes overall performance of sequential recommendation. However, many existing RL-based approaches induce huge computational overhead, because they require not only the recommended items but also all other candidate items to be stored. This paper proposes an efficient alternative that does not require the candidate items. The idea is to model the correlation between user engagement and items directly from data. Moreover, the proposed approach consider randomness in user feedback and termination behavior, which are ubiquitous for RS but rarely discussed in RL-based prior work. With online A/B experiments on real-world RS, we confirm the efficacy of the proposed approach and the importance of modeling the two types of randomness.
Neural Node Matching for Multi-Target Cross Domain Recommendation
Authors: Wujiang Xu, Shaoshuai Li, Mingming Ha, Xiaobo Guo, Qiongxu Ma, Xiaolei Liu, Linxun Chen, Zhenfeng Zhu
Date: 2023-02-12 00:00:00

Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of interest recently, which intends to improve the recommendation performance in multiple domains (or systems) simultaneously. Most existing multi-target CDR frameworks primarily rely on the existence of the majority of overlapped users across domains. However, general practical CDR scenarios cannot meet the strictly overlapping requirements and only share a small margin of common users across domains}. Additionally, the majority of users have quite a few historical behaviors in such small-overlapping CDR scenarios}. To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i.e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions. The present framework} mainly contains two modules: (i) intra-to-inter node matching module, and (ii) intra node complementing module. Concretely, the first module conducts intra-knowledge fusion within each domain and subsequent inter-knowledge fusion across domains by fully connected user-user homogeneous graph information aggregating.
Denoising and Prompt-Tuning for Multi-Behavior Recommendation
Authors: Chi Zhang, Rui Chen, Xiangyu Zhao, Qilong Han, Li Li
Date: 2023-02-12 00:00:00
Tasks: Collaborative Filtering, Denoising

In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only have a single type of behavior with items, making it insufficient to utilize complex collaborative signals to learn informative representations and infer actual user preferences. Consequently, some pioneer studies explore modeling multi-behavior heterogeneity to learn better representations and boost the performance of recommendations for a target behavior. However, a large number of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant information to recommenders, which could mislead the target behavior (i.e., purchase) recommendation, rendering two critical challenges: (i) denoising auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and target behaviors. Motivated by the above observation, we propose a novel framework-Denoising and Prompt-Tuning (DPT) with a three-stage learning paradigm to solve the aforementioned challenges. In particular, DPT is equipped with a pattern-enhanced graph encoder in the first stage to learn complex patterns as prior knowledge in a data-driven manner to guide learning informative representation and pinpointing reliable noise for subsequent stages. Accordingly, we adopt different lightweight tuning approaches with effectiveness and efficiency in the following stages to further attenuate the influence of noise and alleviate the semantic gap among multi-typed behaviors. Extensive experiments on two real-world datasets demonstrate the superiority of DPT over a wide range of state-of-the-art methods. The implementation code is available online at https://github.com/zc-97/DPT.
Overview of the TREC 2022 Fair Ranking Track
Authors: Michael D. Ekstrand, Graham McDonald, Amifa Raj, Isaac Johnson
Date: 2023-02-11 00:00:00
Tasks: Retrieval

The TREC Fair Ranking Track aims to provide a platform for participants to develop and evaluate novel retrieval algorithms that can provide a fair exposure to a mixture of demographics or attributes, such as ethnicity, that are represented by relevant documents in response to a search query. For example, particular demographics or attributes can be represented by the documents topical content or authors. The 2022 Fair Ranking Track adopted a resource allocation task. The task focused on supporting Wikipedia editors who are looking to improve the encyclopedia's coverage of topics under the purview of a WikiProject. WikiProject coordinators and/or Wikipedia editors search for Wikipedia documents that are in need of editing to improve the quality of the article. The 2022 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia. The under-representation of particular protected characteristics in Wikipedia can result in systematic biases that can have a negative human, social, and economic impact, particularly for disadvantaged or protected societal groups.
Self-powered weigh-in-motion system combining vibration energy harvesting and self-sensing composite pavements
Authors: Hasan Borke Birgin, Enrique García-Macías, Antonella D'Alessandro, Filippo Ubertini
Date: 2023-01-27 00:00:00

Overloaded vehicles are the primary cause of accelerated degradation of road infrastructures. In this context, although weigh-in-motion (WIM) systems are most efficient to enforce weight regulations, current technologies require costly investments limiting their extensive implementation. Recent advances in multifunctional composites enabled cost-efficient alternatives in the form of smart pavements. Nevertheless, the need for a stable power supply still represents a major practical limitation. This work presents a novel proof-of-concept self-sustainable WIM technology combining smart pavements and vibration-based energy harvesting (EH). The feasibility of piezoelectric bimorph cantilevered beams to harvest traffic-induced vibrations is firstly investigated, followed by the demonstration of the proposed technology under laboratory conditions. The main original contributions of this work comprise (i) the development of a new self-powered data acquisition system, (ii) a novel approach for the fabrication and electromechanical testing of the piezoresistive composite pavement, and (iii) laboratory feasibility analysis of the developed EH unit to conduct traffic load identification through electrical resistivity measurements of the smart pavement. While the presented results conclude the need for dense EH networks or combinations of different EH technologies to attain complete self-sustainability, this work represents an initial feasibility evidence paving the way towards the development of self-powered low-cost WIM systems.
Example-guided learning of stochastic human driving policies using deep reinforcement learning
Authors: Ran Emuna, Rotem Duffney, Avinoam Borowsky, Armin Biess
Date: 2022-12-23 00:00:00
Proceeding: Neural Computing and Applications 2022 12
Tasks: Autonomous Vehicles, Deep Reinforcement Learning, Gaussian Processes, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Unity

Deep reinforcement learning has been successfully applied to the generation of goal-directed behavior in artificial agents. However, existing algorithms are often not designed to reproduce human-like behavior, which may be desired in many environments, such as human–robot collaborations, social robotics and autonomous vehicles. Here we introduce a model-free and easy-to-implement deep reinforcement learning approach to mimic the stochastic behavior of a human expert by learning distributions of task variables from examples. As tractable use-cases, we study static and dynamic obstacle avoidance tasks for an autonomous vehicle on a highway road in simulation (Unity). Our control algorithm receives a feedback signal from two sources: a deterministic (handcrafted) part encoding basic task goals and a stochastic (data-driven) part that incorporates human expert knowledge. Gaussian processes are used to model human state distributions and to assess the similarity between machine and human behavior. Using this generic approach, we demonstrate that the learning agent acquires human-like driving skills and can generalize to new roads and obstacle distributions unseen during training.