OpenRaiser/Intern-Atlas huggingface dataset:
Multimodal Post Attentive Profiling for Influencer Marketing
Authors: Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han, Wei Wang
Venue: Conference WWW 2020
Year: 2020

Influencer marketing has become a key marketing method for brands in recent years. Hence, brands have been increasingly utilizing influencers’ social networks to reach niche markets, and researchers have been studying various aspects of influencer marketing. However, brands have often suffered from searching and hiring the right influencers with specific interests/topics for their marketing due to a lack of available influencer data and/or limited capacity of marketing agencies. This paper proposes a multimodal deep learning model that uses text and image information from social media posts (i) to classify influencers into specific interests/topics (e.g., fashion, beauty) and (ii) to classify their posts into certain categories. We use the attention mechanism to select the posts that are more relevant to the topics of influencers, thereby generating useful influencer representations. We conduct experiments on the dataset crawled from Instagram, which is the most popular social media for influencer marketing. The experimental results show that our proposed model significantly outperforms existing user profiling methods by achieving 98% and 96% accuracy in classifying influencers and their posts, respectively. We release our influencer dataset of 33,935 influencers labeled with specific topics based on 10,180,500 posts to facilitate future research.
Photometric stereo under perspective projection
Authors: Ariel Tankus, Nahum Kiryati
Venue: Conference ICCV 2005
Year: 2005

Photometric stereo is a fundamental approach in computer vision. At its core lies a set of image irradiance equations each taken with a different illumination. The vast majority of studies in this field have assumed orthography as the projection model. This paper re-examines the basic set of equations of photometric stereo, under an assumption of perspective projection. We show that the resulting system is linear (as is the case under the orthographic model; Nevertheless, the unknowns are different in the perspective case). We then suggest a simple reconstruction algorithm based on the perspective formulae, and compare it to its orthographic counterpart on synthetic as well as real images. This algorithm obtained lower error rates than the orthographic one in all of the error measures. These findings strengthen the hypothesis that a more realistic set of assumptions, the perspective one, improves reconstruction significantly.
CausCF: Causal collaborative filtering for recommendation effect estimation
Authors: Xu Xie, Zhaoyang Liu, Shiwen Wu, Fei Sun, Cihang Liu, Jiawei Chen, Jinyang Gao, Bin Cui, Bolin Ding
Venue: Conference CIKM 2021
Year: 2021

To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clicks and purchases). However, they overlook the fact that users may purchase the items even without recommendations. The real effective items are the ones that can contribute to purchase probability uplift. To select these effective items, it is essential to estimate the causal effect of recommendations. Nevertheless, it is difficult to obtain the real causal effect since we can only recommend or not recommend an item to a user at one time. Furthermore, previous works usually rely on the randomized controlled trial (RCT) experiment to evaluate their performance. However, it is usually not practicable in the recommendation scenario due to its expensive experimental cost. To tackle these problems, in this paper, we propose a causal collaborative filtering (CausCF) method inspired by the widely adopted collaborative filtering (CF) technique. It is based on the idea that similar users not only have a similar taste on items but also have similar treatment effects under recommendations. CausCF extends the classical matrix factorization to the tensor factorization with three dimensions---user, item, and treatment. Furthermore, we also employ regression discontinuity design (RDD) to evaluate the precision of the estimated causal effects from different models. With the testable assumptions, RDD analysis can provide an unbiased causal conclusion without RCT experiments. Through dedicated experiments on both offline and online experiments, we demonstrate the effectiveness of our proposed CausCF on the causal effect estimation and ranking performance improvement.
Trustworthy transfer learning: Transferability and trustworthiness
Authors: Jun Wu 0019, Jingrui He
Venue: Conference KDD 2023
Year: 2023

Deep transfer learning investigates the transfer of knowledge or information from a source domain to a relevant target domain via deep neural networks. In this tutorial, we dive into understanding deep transfer learning from the perspective of knowledge transferability and trustworthiness (e.g., privacy, robustness, fairness, transparency, etc.). To this end, we provide a comprehensive review of state-of-the-art theoretical analysis and algorithms for deep transfer learning. To be specific, we start by introducing the concepts of transferability and trustworthiness in the context of deep transfer learning. Then we summarize recent theories and algorithms for understanding knowledge transferability from two aspects: (1) IID transferability: the samples within each domain are independent and identically distributed (e.g., individual images), and (2) non-IID transferability: The samples within each domain are interdependent (e.g., connected nodes within a graph). In addition to knowledge transferability, we also review the impact of trustworthiness on deep transfer learning, e.g., whether the transferred knowledge is adversarially robust or algorithmically fair, how to transfer the knowledge under privacy-preserving constraints, etc. Finally, we highlight the open questions and future directions for understanding deep transfer learning in real-world applications. We believe this tutorial can benefit researchers and practitioners by rethinking the trade-off between knowledge transferability and trustworthiness in developing trustworthy transfer learning systems.
Unified knowledge prompt pre-training for customer service dialogues
Authors: Keqing He, Jingang Wang, Chaobo Sun, Wei Wu
Venue: Conference CIKM 2022
Year: 2022

Dialogue bots have been widely applied in customer service scenarios to provide timely and user-friendly experience. These bots must classify the appropriate domain of a dialogue, understand the intent of users, and generate proper responses. Existing dialogue pre-training models are designed only for several dialogue tasks and ignore weakly-supervised expert knowledge in customer service dialogues. In this paper, we propose a novel unified knowledge prompt pre-training framework, UFA (\textbf{U}nified Model \textbf{F}or \textbf{A}ll Tasks), for customer service dialogues. We formulate all the tasks of customer service dialogues as a unified text-to-text generation task and introduce a knowledge-driven prompt strategy to jointly learn from a mixture of distinct dialogue tasks. We pre-train UFA on a large-scale Chinese customer service corpus collected from practical scenarios and get significant improvements on both natural language understanding (NLU) and natural language generation (NLG) benchmarks.
Biokg: A knowledge graph for relational learning on biological data
Authors: Brian Walsh, Sameh K. Mohamed, Vít Novácek
Venue: Conference CIKM 2020
Year: 2020

Knowledge graphs became a popular means for modeling complex biological systems where they model the interactions between biological entities and their effects on the biological system. They also provide support for relational learning models which are known to provide highly scalable and accurate predictions of associations between biological entities. Despite the success of the combination of biological knowledge graph and relation learning models in biological predictive tasks, there is a lack of unified biological knowledge graph resources. This forced all current efforts and studies for applying a relational learning model on biological data to compile and build biological knowledge graphs from open biological databases. This process is often performed inconsistently across such efforts, especially in terms of choosing the original resources, aligning identifiers of the different databases, and assessing the quality of included data. To make relational learning on biomedical data more standardised and reproducible, we propose a new biological knowledge graph which provides a compilation of curated relational data from open biological databases in a unified format with common, interlinked identifiers. We also provide a new module for mapping identifiers and labels from different databases which can be used to align our knowledge graph with biological data from other heterogeneous sources. Finally, to illustrate the practical relevance of our work, we provide a set of benchmarks based on the presented data that can be used to train and assess the relational learning models in various tasks related to pathway and drug discovery.
Privacy-preserving triangle counting in large graphs
Authors: Xiaofeng Ding, Xiaodong Zhang, Zhifeng Bao, Hai Jin
Venue: Conference CIKM 2018
Year: 2018

Triangle count is a critical parameter in mining relationships among people in social networks. However, directly publishing the findings obtained from triangle counts may bring potential privacy concern, which raises great challenges and opportunities for privacy-preserving triangle counting. In this paper, we choose to use differential privacy to protect triangle counting for large scale graphs. To reduce the large sensitivity caused in large graphs, we propose a novel graph projection method that can be used to obtain an upper bound for sensitivity in different distributions. In particular, we publish the triangle counts satisfying the node-differential privacy with two kinds of histograms: the triangle count distribution and the cumulative distribution. Moreover, we extend the research on privacy preserving triangle counting to one of its applications, the local clustering coefficient. Experimental results show that the cumulative distribution can fit the real statistical information better, and our proposed mechanism has achieved better accuracy for triangle counts while maintaining the requirement of differential privacy.