OpenRaiser/Intern-Atlas huggingface dataset:
Groupim: A mutual information maximization framework for neural group recommendation
Authors: Aravind Sankar, Yanhong Wu, Yuhang Wu 0002, Wei Zhang 0189, Hao Yang 0007, Hari Sundaram
Venue: Conference SIGIR 2020
Year: 2020

We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together. Existing studies target persistent groups with substantial activity history, while ephemeral groups lack historical interactions. To overcome group interaction sparsity, we propose data-driven regularization strategies to exploit both the preference covariance amongst users who are in the same group, as well as the contextual relevance of users' individual preferences to each group. We make two contributions. First, we present a recommender architecture-agnostic framework GroupIM that can integrate arbitrary neural preference encoders and aggregators for ephemeral group recommendation. Second, we regularize the user-group latent space to overcome group interaction sparsity by: maximizing mutual information between representations of groups and group members; and dynamically prioritizing the preferences of highly informative members through contextual preference weighting. Our experimental results on several real-world datasets indicate significant performance improvements (31-62% relative NDCG@20) over state-of-the-art group recommendation techniques.
Interact and decide: Medley of sub-attention networks for effective group recommendation
Authors: Lucas Vinh Tran, Tuan-Anh Nguyen Pham, Yi Tay, Yiding Liu, Gao Cong, Xiaoli Li
Venue: Conference SIGIR 2019
Year: 2019

This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challenging task, in which intricate group dynamics have to be considered. As such, this is to be contrasted with the standard recommendation problem where recommendations are personalized with respect to a single user. Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i.e., a user's decision is highly dependent on the other group members. All in all, our key motivation manifests in a form of an attentive neural model that captures fine-grained interactions between group members. In our MoSAN model, each sub-attention module is representative of a single member, which models a user's preference with respect to all other group members. Subsequently, a Medley of Sub-Attention modules is then used to collectively make the group's final decision. Overall, our proposed model is both expressive and effective. Via a series of extensive experiments, we show that MoSAN not only achieves state-of-the-art performance but also improves standard baselines by a considerable margin.
Learning user representations with hypercuboids for recommender systems
Authors: Shuai Zhang, Huoyu Liu, Aston Zhang, Yue Hu, Ce Zhang, Yumeng Li, Tanchao Zhu, Shaojian He, Wenwu Ou
Venue: Conference WSDM 2021
Year: 2021

Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifically, the key novelty behind our model is that it explicitly models user interests as a hypercuboid instead of a point in the space. In our approach, the recommendation score is learned by calculating a compositional distance between the user hypercuboid and the item. This helps to alleviate the potential geometric inflexibility of existing collaborative filtering approaches, enabling a greater extent of modeling capability. Furthermore, we present two variants of hypercuboids to enhance the capability in capturing the diversities of user interests. A neural architecture is also proposed to facilitate user hypercuboid learning by capturing the activity sequences (e.g., buy and rate) of users. We demonstrate the effectiveness of our proposed model via extensive experiments on both public and commercial datasets. Empirical results show that our approach achieves very promising results, outperforming existing state-of-the-art.
User-controllable recommendation against filter bubbles
Authors: Wenjie Wang, Fuli Feng, Liqiang Nie, Tat-Seng Chua
Venue: Conference SIGIR 2022
Year: 2022

Recommender systems usually face the issue of filter bubbles: over-recommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention. This work proposes a new recommender prototype called User-Controllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands for users to mitigate the bubbles at different granularities. 3) UCRS can respond to the controls and adjust the recommendations on the fly. The key to adjusting lies in blocking the effect of out-of-date user representations on recommendations, which contains historical information inconsistent with the control commands. As such, we develop a causality-enhanced User-Controllable Inference (UCI) framework, which can quickly revise the recommendations based on user controls in the inference stage and utilize counterfactual inference to mitigate the effect of out-of-date user representations. Experiments on three datasets validate that the UCI framework can effectively recommend more desired items based on user controls, showing promising performance w.r.t. both accuracy and diversity.
Conditional Generation Net for Medication Recommendation
Authors: Rui Wu, Zhaopeng Qiu, Jiacheng Jiang, Guilin Qi, Xian Wu
Venue: Conference WWW 2022
Year: 2022

Medication recommendation targets to provide a proper set of medicines according to patients’ diagnoses, which is a critical task in clinics. Currently, the recommendation is manually conducted by doctors. However, for complicated cases, like patients with multiple diseases at the same time, it’s difficult to propose a considerate recommendation even for experienced doctors. This urges the emergence of automatic medication recommendation which can help treat the diagnosed diseases without causing harmful drug-drug interactions. Due to the clinical value, medication recommendation has attracted growing research interests. Existing works mainly formulate medication recommendation as a multi-label classification task to predict the set of medicines. In this paper, we propose the Conditional Generation Net (COGNet) which introduces a novel copy-or-predict mechanism to generate the set of medicines. Given a patient, the proposed model first retrieves his or her historical diagnoses and medication recommendations and mines their relationship with current diagnoses. Then in predicting each medicine, the proposed model decides whether to copy a medicine from previous recommendations or to predict a new one. This process is quite similar to the decision process of human doctors. We validate the proposed model on the public MIMIC data set, and the experimental results show that the proposed model can outperform state-of-the-art approaches.
Towards unbiased and robust causal ranking for recommender systems
Authors: Teng Xiao, Suhang Wang
Venue: Conference WSDM 2022
Year: 2022

We study the problem of optimizing ranking metrics with unbiased and robust causal estimation for recommender systems. A user may click/purchase an item regardless of whether the item is recommended or not. Thus, it is important to estimate the causal effect of recommendation and rank items higher with a larger causal effect. However, most existing works focused on improving the accuracy of recommendations, which usually have large bias and variance. Therefore, in this paper, we provide a general and theoretically rigorous framework for causal recommender systems, which enables unbiased evaluation and learning for the ranking metrics with confounding bias. We first propose a robust estimator for unbiased ranking evaluation and theoretically show that this estimator has a smaller bias and variance. We then propose a deep variational information bottleneck (IB) approach to exploit the sufficiency of the propensity score for estimation adjustment and better generalization. We also provide the learning bound and develop an unbiased learning algorithm to optimize the causal metric. Results on semi-synthetic and real-world datasets show that our evaluation and learning algorithms significantly outperform existing methods.
Molerec: Combinatorial Drug Recommendation with Substructure-Aware Molecular Representation Learning
Authors: Nianzu Yang, Kaipeng Zeng, Qitian Wu, Junchi Yan
Venue: Conference WWW 2023
Year: 2023

Combinatorial drug recommendation involves recommending a personalized combination of medication (drugs) to a patient over his/her longitudinal history, which essentially aims at solving a combinatorial optimization problem that pursues high accuracy under the safety constraint. Among existing learning-based approaches, the association between drug substructures (i.e., a sub-graph of the molecule that contributes to certain chemical effect) and the target disease is largely overlooked, though the function of drugs in fact exhibits strong relevance with particular substructures. To address this issue, we propose a molecular substructure-aware encoding method entitled MoleRec that entails a hierarchical architecture aimed at modeling inter-substructure interactions and individual substructures’ impact on patient’s health condition, in order to identify those substructures that really contribute to healing patients. Specifically, MoleRec learns to attentively pooling over substructure representations which will be element-wisely re-scaled by the model’s inferred relevancy with a patient’s health condition to obtain a prior-knowledge-informed drug representation. We further design a weight annealing strategy for drug-drug-interaction (DDI) objective to adaptively control the balance between accuracy and safety criteria throughout training. Experiments on the MIMIC-III dataset demonstrate that our approach achieves new state-of-the-art performance w.r.t. four accuracy and safety metrics. Our source code is publicly available at https://github.com/yangnianzu0515/MoleRec.
LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity
Authors: Yutao Zhang, Robert Chen, Jie Tang, Walter F. Stewart, Jimeng Sun
Venue: Conference KDD 2017
Year: 2017

Managing patients with complex multimorbidity has long been recognized as a difficult problem due to complex disease and medication dependencies and the potential risk of adverse drug interactions. Existing work either uses complicated rule-based protocols which are hard to implement and maintain, or simple statistical models that treat each disease independently, which may lead to sub-optimal or even harmful drug combinations. In this work, we propose the LEAP (LEArn to Prescribe) algorithm to decompose the treatment recommendation into a sequential decision-making process while automatically determining the appropriate number of medications. A recurrent decoder is used to model label dependencies and content-based attention is used to capture label instance mapping. We further leverage reinforcement learning to fine tune the model parameters to ensure accuracy and completeness. We incorporate external clinical knowledge into the design of the reinforcement reward to effectively prevent generating unfavorable drug combinations. Both quantitative experiments and qualitative case studies are conducted on two real world electronic health record datasets to verify the effectiveness of our solution. On both datasets, LEAP significantly outperforms baselines by up to 10-30% in terms of mean Jaccard coefficient and removes 99.8% adverse drug interactions in the recommended treatment sets.
Long-tail vocabulary dictionary extraction from the web
Authors: Zhe Chen, Michael J. Cafarella, H. V. Jagadish
Venue: Conference WSDM 2016
Year: 2016

A dictionary --- a set of instances belonging to the same conceptual class --- is central to information extraction and is a useful primitive for many applications, including query log analysis and document categorization. Considerable work has focused on generating accurate dictionaries given a few example seeds, but methods to date cannot obtain long-tail (rare) items with high accuracy and recall.
Autoname: a corpus-based set naming framework
Authors: Zhiqi Huang 0002, Razieh Rahimi, Puxuan Yu, Jingbo Shang, James Allan 0001
Venue: Conference SIGIR 2021
Year: 2021

We propose AutoName, an unsupervised framework that extracts a name for a set of query entities from a large-scale text corpus. Entity-set naming is useful in many tasks related to natural language processing and information retrieval such as session-based and conversational information seeking. Previous studies mainly extract set names from knowledge bases which provide highly reliable entity relations, but suffer from limited coverage of entities and set names that represent broad semantic classes. To address these problems, AutoName generates hypernym-anchored candidate phrases via probing a pre-trained language model and the entities' context in documents. Phrases are then clustered to identify ones that describe common concepts among query entities. Finally, AutoName ranks refined phrases based on the co-occurrences of their words with query entities and the conceptual integrity of their respective clusters. We built a new benchmark dataset for this task, consisting of 130 entity sets with name labels. Experimental results show that AutoName generates coherent and meaningful set names and significantly outperforms all baselines.
Egoset: Exploiting word ego-networks and user-generated ontology for multifaceted set expansion
Authors: Xin Rong, Zhe Chen, Qiaozhu Mei, Eytan Adar
Venue: Conference WSDM 2016
Year: 2016

A key challenge of entity set expansion is that multifaceted input seeds can lead to significant incoherence in the result set. In this paper, we present a novel solution to handling multifaceted seeds by combining existing user-generated ontologies with a novel wordsimilarity metric based on skip-grams. By blending the two resources we are able to produce sparse word ego-networks that are centered on the seed terms and are able to capture semantic equivalence among words. We demonstrate that the resulting networks possess internally-coherent clusters, which can be exploited to provide non-overlapping expansions, in order to reflect different semantic classes of the seeds. Empirical evaluation against state-ofthe-art baselines shows that our solution, EgoSet, is able to not only capture multiple facets in the input query, but also generate expansions for each facet with higher precision.
Corpus-based set expansion with lexical features and distributed representations
Authors: Puxuan Yu, Zhiqi Huang, Razieh Rahimi, James Allan
Venue: Conference SIGIR 2019
Year: 2019

Corpus-based set expansion refers to mining "sibling" entities of some given seed entities from a corpus. Previous works are limited to using either textual context matching or semantic matching to fulfill this task. Neither matching method takes full advantage of the rich information in free text. We present CaSE, an efficient unsupervised corpus-based set expansion framework that leverages lexical features as well as distributed representations of entities for the set expansion task. Experiments show that CaSE outperforms state-of-the-art set expansion algorithms in terms of expansion accuracy.
Defense Against Model Extraction Attacks on Recommender Systems
Authors: Sixiao Zhang, Hongzhi Yin, Hongxu Chen, Cheng Long
Venue: Conference WSDM 2024
Year: 2024

The robustness of recommender systems has become a prominent topic within the research community. Numerous adversarial attacks have been proposed, but most of them rely on extensive prior knowledge, such as all the white-box attacks or most of the black-box attacks which assume that certain external knowledge is available. Among these attacks, the model extraction attack stands out as a promising and practical method, involving training a surrogate model by repeatedly querying the target model. However, there is a significant gap in the existing literature when it comes to defending against model extraction attacks on recommender systems. In this paper, we introduce Gradient-based Ranking Optimization (GRO), which is the first defense strategy designed to counter such attacks. We formalize the defense as an optimization problem, aiming to minimize the loss of the protected target model while maximizing the loss of the attacker's surrogate model. Since top-k ranking lists are non-differentiable, we transform them into swap matrices which are instead differentiable. These swap matrices serve as input to a student model that emulates the surrogate model's behavior. By back-propagating the loss of the student model, we obtain gradients for the swap matrices. These gradients are used to compute a swap loss, which maximizes the loss of the student model. We conducted experiments on three benchmark datasets to evaluate the performance of GRO, and the results demonstrate its superior effectiveness in defending against model extraction attacks.
Information Retrieval Using a Transportable Natural Language Interface
Authors: Madeleine Bates, Robert J. Bobrow
Venue: Conference SIGIR 1983
Year: 1983

This paper describes work in progress to develop a facility for natural language access to a variety of computer databases and database systems. This facility, called IRUS for Information Retrieval using the RUS parsing system, allows users who are unfamiliar with the technical characteristics of the underlying database system to query databases using typed English input. This system can be thought of as a stand-alone query system or as part of a management information system (MIS) or a decision support system (DSS).Many systems boast of having a "user-friendly" or "English-like" or even "English" interface so that users require a minimum of special training to use the system, but most such systems use shallow, relatively ad hoc techniques that are not robust or linguistically sound. We are using a large, well-tested, theoretically-based, general parser of English that has been developed and extended in a variety of research projects for over a decade.One of the primary emphases of IRUS is transportability, which includes three types of changes: (1) changing the domain, (2) changing data bases within the same domain, and (3) changing data base systems. The use of a general parser for English is an important part of the solution to the transportability problem, but there are other parts as well, since portions of the system beyond the parser must know the conceptual content of the domain, the way in which this is reflected in a collection of datasets, and the operating characteristics of the dbms being used to access these datasets.Other researchers have investigated similar issues [8, 5, 6, 12]. We have attacked this problem by building a knowledge-based system, with procedural components independent of domain and data base structure, directed by domain and database dependent knowledge structures. We are also building tools for conveniently creating and maintaining these knowledge structures, with an eventual goal of allowing end-users to extend and modify these knowledge structures to suit their own needs. Given this set of goals, and these tools, we consider the current implementation, which uses the System 1022 dbms on the DEC KL-2060, to be only one of a set of possible implementations, and are not constraining IRUS on the basis of 1022's strengths and weaknesses.This paper presents an overview of the IRUS system, emphasizing those aspects of the design that are critical to transportability. We describe the parsing system, which is a completely independent module that has been interfaced to a variety of different applications, and then discuss the other modules which bridge the gap between the parser and the dbms.
TPR: Text-Aware Preference Ranking for Recommender Systems
Authors: Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yuan Fang, Ee-Peng Lim
Venue: Conference CIKM 2020
Year: 2020

Textual data is common and informative auxiliary information for recommender systems. Most prior art utilizes text for rating prediction, but rare work connects it to top-recommendation. Moreover, although advanced recommendation models capable of incorporating auxiliary information have been developed, none of these are specifically designed to model textual information, yielding a limited usage scenario for typical user-to-item recommendation. In this work, we present a framework of text-aware preference ranking (TPR) for top- recommendation, in which we comprehensively model the joint association of user-item interaction and relations between items and associated text. Using the TPR framework, we construct a joint likelihood function that explicitly describes two ranking structures: 1) item preference ranking (IPR) and 2) word relatedness ranking (WRR), where the former captures the item preference of each user and the latter captures the word relatedness of each item. As these two explicit structures are by nature mutually dependent, we propose TPR-OPT, a simple yet effective learning criterion that additionally includes implicit structures, such as relatedness between items and relatedness between words for each user for model optimization. Such a design not only successfully describes the joint association among users, words, and text comprehensively but also naturally yields powerful representations that are suitable for a range of recommendation tasks, including user-to-item, item-to-item, and user-to-word recommendation, as well as item-to-word reconstruction. In this paper, extensive experiments have been conducted on eight recommendation datasets, the results of which demonstrate that by including textual information from item descriptions, the proposed TPR model consistently outperforms state-of-the-art baselines on various recommendation tasks.
A black-box attack model for visually-aware recommender systems
Authors: Rami Cohen, Oren Sar Shalom, Dietmar Jannach, Amihood Amir
Venue: Conference WSDM 2021
Year: 2021

Due to the advances in deep learning, visually-aware recommender systems (RS) have recently attracted increased research interest. Such systems combine collaborative signals with images, usually represented as feature vectors outputted by pre-trained image models. Since item catalogs can be huge, recommendation service providers often rely on images that are supplied by the item providers. In this work, we show that relying on such external sources can make an RS vulnerable to attacks, where the goal of the attacker is to unfairly promote certain pushed items. Specifically, we demonstrate how a new visual attack model can effectively influence the item scores and rankings in a black-box approach, i.e., without knowing the parameters of the model. The main underlying idea is to systematically create small human-imperceptible perturbations of the pushed item image and to devise appropriate gradient approximation methods to incrementally raise the pushed item's score. Experimental evaluations on two datasets show that the novel attack model is effective even when the contribution of the visual features to the overall performance of the recommender system is modest.