OpenRaiser/Intern-Atlas huggingface dataset:
CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
Authors: Cheng Zhao, Chenliang Li 0005, Rong Xiao 0005, Hongbo Deng, Aixin Sun
Venue: SIGIR
Year: 2020

In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interactions with items in one domain but not in the other domain. To the latter, these users are considered as cold-start users. How to effectively transfer users' preferences based on their interactions from one domain to the other relevant domain, is the key issue in cross-domain recommendation. Inspired by the advances made in review-based recommendation, we propose to model user preference transfer at aspect-level derived from reviews. To this end, we propose a cross-domain recommendation framework via aspect transfer network for cold-start users (named CATN). CATN is devised to extract multiple aspects for each user and each item from their review documents, and learn aspect correlations across domains with an attention mechanism. In addition, we further exploit auxiliary reviews from like-minded users to enhance a user's aspect representations. Then, an end-to-end optimization framework is utilized to strengthen the robustness of our model. On real-world datasets, the proposed CATN outperforms SOTA models significantly in terms of rating prediction accuracy. Further analysis shows that our model is able to reveal user aspect connections across domains at a fine level of granularity, making the recommendation explainable.
Leveraging Demonstrations for Reinforcement Recommendation Reasoning over Knowledge Graphs
Authors: Kangzhi Zhao, Xiting Wang, Yuren Zhang, Li Zhao 0007, Zheng Liu 0011, Chunxiao Xing, Xing Xie 0001
Venue: SIGIR
Year: 2020

Knowledge graphs have been widely adopted to improve recommendation accuracy. The multi-hop user-item connections on knowledge graphs also endow reasoning about why an item is recommended. However, reasoning on paths is a complex combinatorial optimization problem. Traditional recommendation methods usually adopt brute-force methods to find feasible paths, which results in issues related to convergence and explainability. In this paper, we address these issues by better supervising the path finding process. The key idea is to extract imperfect path demonstrations with minimum labeling efforts and effectively leverage these demonstrations to guide path finding. In particular, we design a demonstration-based knowledge graph reasoning framework for explainable recommendation. We also propose an ADversarial Actor-Critic (ADAC) model for the demonstration-guided path finding. Experiments on three real-world benchmarks show that our method converges more quickly than the state-of-the-art baseline and achieves better recommendation accuracy and explainability.
Improving Neural Chinese Word Segmentation with Lexicon-enhanced Adaptive Attention
Authors: Xiaoyan Zhao 0005, Min Yang 0007, Qiang Qu 0001, Yang Sun
Venue: SIGIR
Year: 2020

Chinese word segmentation (CWS) is an important research topic in information retrieval (IR) and natural language processing (NLP). Significant progresses have been made by deep neural networks with context features. However, these deep models may fail to deal with rare or ambiguous words, thus limit the overall CWS performance. In this paper, we propose a lexicon-enhanced adaptive attention network (LAAN), which takes full advantage of external lexicons to deal with the rare or ambiguous words. Specifically, we devise an adaptive attention mechanism to learn the lexicon-aware representation. In addition, we propose a fusion gate to effectively integrate the additional word information with context information to improve the performance of CWS. LAAN is evaluated on four benchmark datasets, and the experimental results demonstrate that LAAN has robust superiority over the compared methods.
MaHRL: Multi-goals Abstraction Based Deep Hierarchical Reinforcement Learning for Recommendations
Authors: Dongyang Zhao, Liang Zhang, Bo Zhang, Lizhou Zheng, Yongjun Bao, Weipeng Yan
Venue: SIGIR
Year: 2020

As huge commercial value of the recommender system, there has been growing interest to improve its performance in recent years. The majority of existing methods have achieved great improvement on the metric of click, but perform poorly on the metric of conversion possibly due to its extremely sparse feedback signal. To track this challenge, we design a novel deep hierarchical reinforcement learning based recommendation framework to model consumers' hierarchical purchase interest. Specifically, the high-level agent catches long-term sparse conversion interest, and automatically sets abstract goals for low-level agent, while the low-level agent follows the abstract goals and catches short-term click interest via interacting with real-time environment. To solve the inherent problem in hierarchical reinforcement learning, we propose a novel multi-goals abstraction based deep hierarchical reinforcement learning algorithm (MaHRL). Our proposed algorithm contains three contributions: 1) the high-level agent generates multiple goals to guide the low-level agent in different sub-periods, which reduces the difficulty of approaching high-level goals; 2) different goals share the same state encoder structure and its parameters, which increases the update frequency of the high-level agent and thus accelerates the convergence of our proposed algorithm; 3) an appreciated reward assignment mechanism is designed to allocate rewards in each goal so as to coordinate different goals in a consistent direction. We evaluate our proposed algorithm based on a real-world e-commerce dataset and validate its effectiveness.
Incorporating Scenario Knowledge into A Unified Fine-tuning Architecture for Event Representation
Authors: Jianming Zheng, Fei Cai, Honghui Chen
Venue: SIGIR
Year: 2020

Given an occurred event, human can easily predict the next event or reason the preceding event, yet which is difficult for machine to perform such event reasoning. Event representation bridges the connection and targets to model the process of event reasoning as a machine-readable format, which then can support a wide range of applications in information retrieval, e.g., question answering and information extraction. Existing work mainly resorts to a joint training to integrate all levels of training loss in event chains by a simple loss summation, which is easily trapped into a local optimum. In addition, the scenario knowledge in event chains is not well investigated for event representation. In this paper, we propose a unified fine-tuning architecture, incorporated with scenario knowledge for event representation, i.e., UniFA-S, which mainly consists of a unified fine-tuning architecture (UniFA) and a scenario-level variational auto-encoder (S-VAE). In detail, UniFA employs a multi-step fine-tuning to integrate all levels of training and S-VAE applies a stochastic variable to implicitly represent the scenario-level knowledge. We evaluate our proposal from two aspects, i.e., the representation and inference abilities. For the representation ability, our ensemble model UniFA-S can beat state-of-the-art baselines for two similarity tasks. For the inference ability, UniFA-S can outperform the best baseline, achieving 4.1%-8.2% improvements in terms of accuracy for various inference tasks.
Sentiment-guided Sequential Recommendation
Authors: Lin Zheng 0003, Naicheng Guo, Weihao Chen, Jin Yu, Dazhi Jiang
Venue: SIGIR
Year: 2020

The existing sequential recommendation methods focus on modeling the temporal relationships of user behaviors and are good at using additional item information to improve performance. However, these methods rarely consider the influences of users' sequential subjective sentiments on their behaviors---and sometimes the temporal changes in human sentiment patterns plays a decisive role in users' final preferences. To investigate the influence of temporal sentiments on user preferences, we propose generating preferences by guiding user behavior through sequential sentiments. Specifically, we design a dual-channel fusion mechanism. The main channel consists of sentiment-guided attention to match and guide sequential user behavior, and the secondary channel consists of sparse sentiment attention to assist in preference generation. In the experiments, we demonstrate the effectiveness of these two sentiment modeling mechanisms through ablation studies. Our approach outperforms current state-of-the-art sequential recommendation methods that incorporate sentiment factors.
Social Media User Geolocation via Hybrid Attention
Authors: Cheng Zheng 0004, Jyun-Yu Jiang, Yichao Zhou 0001, Sean D. Young, Wei Wang 0010
Venue: SIGIR
Year: 2020

Determining user geolocation is vital to various real-world applications on the internet, such as online marketing and event detection. To identify the geolocations of users, their behaviors on social media like published posts and social interactions can be strong evidence. However, most of the existing social media based approaches individually learn from text contexts and social networks. This separation can not only lead to sub-optimal performance but also ignore the distinct importance of two resources for different users. To address this challenge, we propose a novel end-to-end framework, Hybrid-attentive User Geolocation (HUG), to jointly model post texts and user interactions in social media. The hybrid attention mechanism is introduced to automatically determine the importance of texts and social networks for each user while social media posts and interactions are modeled by a graph attention network and a language attention network. Extensive experiments conducted on three benchmark geolocation datasets using Twitter data demonstrate that HUG significantly outperforms competitive baseline methods. The in-depth analysis also indicates the robustness and interpretability of HUG.
Bridging East and West: An Integration of TCM and Western Medicine in Medical Text Mining
Authors: Runjie Zhu
Venue: SIGIR
Year: 2020

Traditional Chinese Medicine (TCM) has been used by practitioners for millennia to prevent and treat disease, but has struggled to gain broad acceptance in the West. In 2019, the World Health Organisation officially recognized TCM as a form of medical treatment, a step towards internationalizing TCM and integrating it with Western medicine (WM). The proposed dissertation research aims to bridge eastern and western medical philosophies by applying named entity recognition (NER) and information retrieval (IR) models supported by medical and cross lingual knowledge graphs, to enhance the retrieval performance as well as to increase the model explainability.
DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts
Authors: Markus Zlabinger, Marta Sabou, Sebastian Hofstätter, Mete Sertkan, Allan Hanbury
Venue: SIGIR
Year: 2020

The success of crowdsourcing based annotation of text corpora depends on ensuring that crowdworkers are sufficiently well-trained to perform the annotation task accurately. To that end, a frequent approach to train annotators is to provide instructions and a few example cases that demonstrate how the task should be performed (referred to as the CONTROL approach). These globally defined "task-level examples", however, (i) often only cover the common cases that are encountered during an annotation task; and (ii) require effort from crowdworkers during the annotation process to find the most relevant example for the currently annotated sample. To overcome these limitations, we propose to support workers in addition to task-level examples, also with "task-instance level" examples that are semantically similar to the currently annotated data sample (referred to as Dynamic Examples for Annotation, DEXA). Such dynamic examples can be retrieved from collections previously labeled by experts, which are usually available as gold standard dataset. We evaluate DEXA on a complex task of annotating participants, interventions, and outcomes (known as PIO) in sentences of medical studies. The dynamic examples are retrieved using BioSent2Vec, an unsupervised semantic sentence similarity method specific to the biomedical domain. Results show that (i) workers of the DEXA approach reach on average much higher agreements (Cohen's Kappa) to experts than workers of the the CONTROL approach (avg. of 0.68 to experts in DEXA vs. 0.40 in CONTROL); (ii) already three per majority voting aggregated annotations of the DEXA approach reach substantial agreements to experts of 0.78/0.75/0.69 for P/I/O (in CONTROL 0.73/0.58/0.46). Finally, (iii) we acquire explicit feedback from workers and show that in the majority of cases (avg. 72%) workers find the dynamic examples useful.
Towards Question-based Recommender Systems
Authors: Jie Zou 0001, Yifan Chen 0003, Evangelos Kanoulas
Venue: SIGIR
Year: 2020

Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless, research in the field is still limited, compared to traditional recommender systems. In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions. Previous conversational recommender systems ask users to express their preferences over items or item facets. Our model, instead, asks users to express their preferences over descriptive item features. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online by a closed-form solution based on the user answers. Meanwhile, our model infers the underlying user belief and preferences over items to learn an optimal question-asking strategy by using Generalized Binary Search, so as to ask a sequence of questions to the user. Our experimental results demonstrate that our proposed matrix factorization model outperforms the traditional Probabilistic Matrix Factorization model. Further, our proposed Qrec model can greatly improve the performance of state-of-the-art baselines, and it is also effective in the case of cold-start user and item recommendations.
Info-flow Enhanced GANs for Recommender
Authors: Yuan Lin 0001, Zhang Xie, Bo Xu 0009, Kan Xu, Hongfei Lin
Venue: SIGIR
Year: 2021

Recommendation systems can help users process large amounts of information, and generative adversarial networks (GANs) show great potential in recommendation systems. In this paper, we propose a new GAN model to enhance the information flow within the generator based on the information flow between the original generator and discriminator. Our experimental results indicate that our model reduces the discrepancy between the generator and the discriminator. Both the generator and discriminator yield considerable performance improvements compared to other strong baselines. The improvements by [email protected] and MRR are significant, which can reach 30.98% and 30.17%, respectively.
DRL4IR: 2nd Workshop on Deep Reinforcement Learning for Information Retrieval
Authors: Weinan Zhang 0001, Xiangyu Zhao 0001, Li Zhao 0007, Dawei Yin 0001, Grace Hui Yang
Venue: SIGIR
Year: 2021

Modern information retrieval (IR) consists of a series of processes, including query expansion, candidate item recall, item ranking, item re-ranking, etc. The final ranked item list will be exposed to the user, which will accordingly provide feedback through some expected actions such as browsing and click. Such a whole process can be formulated as a decision-making process where the agent is the IR system while the environment is the specific user. This decision-making process can be one-step or sequential, depending on the scenarios or the ways of problem formulation. Since 2013, Deep reinforcement learning (DRL) has been a fast-developing technique for decision-making tasks. The high capacity of deep learning models is incorporated in the reinforcement learning framework so that the agent may successfully handle complex decision-making. In recent years, there have been a bunch of publications attempting to leverage DRL techniques for different IR tasks such as ad hoc retrieval, learning to rank and interactive recommendation. Nonetheless, the fundamental theory, the principle of RL methods or the recognized experimental protocols of decision-making in IR, has not been well developed, making it challenging to evaluate the correctness of a proposed method or judge whether the reported experimental performance is valid. We propose the second DRL4IR workshop at SIGIR 2021, which provides a venue to gather the academia researchers and industry practitioners to present the recent progress of DRL techniques for IR. More importantly, people in this workshop are expected to discuss more about the fundamental principles of formulating a decision-making IR task, the underlying theory as well as the practical effectiveness of the experiment protocol design, which would foster further research on novel methodologies, innovative experimental findings and new applications of DRL for information retrieval. DRL4IR organized at SIGIR'20 was one of the most popular workshops and attracted over 200 conference attendees. In this year, we will pay more attention to fundamental research topics and recent applications, and expect about 300 participants.
Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction
Authors: Fan Zhou 0002, Liu Yu 0001, Xovee Xu, Goce Trajcevski
Venue: SIGIR
Year: 2021

Effectively predicting the size of information cascades is crucial for understanding the evolution of many social applications, such as influence maximization and fake news detection. Conventional methods face the challenge of data imbalance which, in turn, yields unsatisfactory prediction performance. To prevent the loss functions or metrics from being affected by extreme values and assure numerical stability, previous works reformulate the problem definitions or adopt other types of evaluation metrics. However, solving the regression prediction of information cascades from a long-tailed distribution perspective is under explored. In this paper, we propose a general decoupling prediction solution -- first extracting the representation, then fine-tuning the regressor, which combines the original prediction value and weighted bias generated by a sub-network (SUB) that we designed. Our experiments conducted on long-tailed benchmarks demonstrate that our method significantly improves the prediction accuracy over state-of-the-art methods and mitigates the long-tailed cascade prediction problem.
Regularized Dual-PPMI Co-clustering for Text Data
Authors: Séverine Affeldt, Lazhar Labiod, Mohamed Nadif
Venue: SIGIR
Year: 2021

Co-clustering of document-term matrices has proved to be more effective than one-sided clustering. By their nature, text data are also generally unbalanced and directional. Recently, the von Mises-Fisher (vMF) mixture model was proposed to handle unbalanced data while harnessing the directional nature of text. In this paper we propose a novel co-clustering approach based on a matrix formulation of vMF model-based co-clustering. This formulation leads to a flexible method for text co-clustering that can easily incorporate both word-word semantic relationships and document-document similarities. By contrast with existing methods, which generally use an additive incorporation of similarities, we propose a dual multiplicative regularization that better encapsulates the underlying text data structure. Extensive evaluations on various real-world text datasets demonstrate the superior performance of our proposed approach over baseline and competitive methods, both in terms of clustering results and co-cluster topic coherence.
A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images
Authors: Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
Venue: SIGIR
Year: 2021

Visual-based recommender systems (VRSs) enhance recommendation performance by integrating users' feedback with the visual features of items' images. Recently, human-imperceptible image perturbations, defined adversarial samples, have been shown capable of altering the VRSs performance, for example, by pushing (promoting) or nuking (demoting) specific categories of products. One of the most effective adversarial defense methods is adversarial training (AT), which enhances the robustness of the model by incorporating adversarial samples into the training process and minimizing an adversarial risk. The AT effectiveness has been verified on defending DNNs in supervised learning tasks such as image classification. However, the extent to which AT can protect deep VRSs, against adversarial perturbation of images remains mostly under-investigated. This work focuses on the defensive side of VRSs and provides general insights that could be further exploited to broaden the frontier in the field. First, we introduce a suite of adversarial attacks against DNNs on top of VRSs, and defense strategies to counteract them. Next, we present an evaluation framework, named Visual Adversarial Recommender (VAR), to empirically investigate the performance of defended or undefended DNNs in various visually-aware item recommendation tasks. The results of large-scale experiments indicate alarming risks in protecting a VRS through the DNN robustification. Source code and data are available at https://github.com/sisinflab/Visual-Adversarial-Recommendation.
Sim4IR: The SIGIR 2021 Workshop on Simulation for Information Retrieval Evaluation
Authors: Krisztian Balog, David Maxwell 0001, Paul Thomas 0001, Shuo Zhang 0006
Venue: SIGIR
Year: 2021

The use of simulation techniques is not foreign to information retrieval. In the past, simulation has been employed, for example, for constructing test collections and for model performance prediction and analysis in a broad array of information access scenarios. Nevertheless, a standardized methodology for performance evaluation via simulation has not yet been developed. The goal of this workshop is to create a forum for researchers and practitioners to promote methodology development and more widespread use of simulation for evaluation by: (1) identifying problem settings and application scenarios; (2) sharing tools, techniques, and experiences; (3) characterizing potentials and limitations; and (4) developing a research agenda.
YASBIL: Yet Another Search Behaviour (and) Interaction Logger
Authors: Nilavra Bhattacharya, Jacek Gwizdka
Venue: SIGIR
Year: 2021

Collecting participant search logs is an integral part of interactive IR research. Today's existing approaches are either piecemeal solutions, and/or require cumbersome setups. We present YASBIL, a two-component logging solution comprising a browser extension and a WordPress plugin. The browser extension logs the browsing activity in the participants' machines. The WordPress plugin collects the logged data into the researcher's data server. The logging works on any webpage, without the need to own or have knowledge about the HTML structure of the webpage. YASBIL also offers ethical data transparency and security towards participants, by enabling them to view and obtain copies of the logged data, as well as securely upload the data to the researcher's server over an HTTPS connection. We posit that ease of installation and use will make YASBIL especially suitable for remote user-studies, and longitudinal studies in IR.