OpenRaiser/Intern-Atlas huggingface dataset:
Targeted shilling attacks on gnn-based recommender systems
Authors: Sihan Guo, Ting Bai, Weihong Deng
Venue: Conference CIKM 2023
Year: 2023

GNN-based recommender systems have shown their vulnerability to shilling attacks in recent studies. By conducting shilling attacks on recommender systems, the attackers aim to have homogeneous impacts on all users. However, such indiscriminate attacks suffer from a waste of resources because even if the target item is promoted to users who are not interested, they are unlikely to click on them. In this paper, we conduct targeted shilling attacks in GNN-based recommender systems. By automatically constructing the features and edges of the fake users, our proposed framework AutoAttack achieves accurate attacks on a specific group of users while minimizing the impact on non-target users. Specifically, the features of fake users are generated based on a similarity function, which is optimized according to the features of target users. The structure of fake users is learned by conducting spectral clustering on the target users based on their graph Laplacian matrix, which contains the degree and adjacency information that provides guidance to the edge generation of fake users. We conduct extensive experiments on four real-world datasets in different GNN-based RS and evaluate the performance of our method on the shilling attack and recommendation tasks comprehensively, showing the effectiveness and flexibility of our framework.
Petgen: Personalized text generation attack on deep sequence embedding-based classification models
Authors: Bing He, Mustaque Ahamad, Srijan Kumar
Venue: Conference KDD 2021
Year: 2021

What should a malicious user write next to fool a detection model? Identifying malicious users is critical to ensure the safety and integrity of internet platforms. Several deep learning based detection models have been created. However, malicious users can evade deep detection models by manipulating their behavior, rendering these models of little use. The vulnerability of such deep detection models against adversarial attacks is unknown. Here we create a novel adversarial attack model against deep user sequence embedding-based classification models, which use the sequence of user posts to generate user embeddings and detect malicious users. In the attack, the adversary generates a new post to fool the classifier. We propose a novel end-to-end Personalized Text Generation Attack model, called PETGEN, that simultaneously reduces the efficacy of the detection model and generates posts that have several key desirable properties. Specifically, PETGEN generates posts that are personalized to the user's writing style, have knowledge about a given target context, are aware of the user's historical posts on the target context, and encapsulate the user's recent topical interests. We conduct extensive experiments on two real-world datasets (Yelp and Wikipedia, both with ground-truth of malicious users) to show that PETGEN significantly reduces the performance of popular deep user sequence embedding-based classification models. PETGEN outperforms five attack baselines in terms of text quality and attack efficacy in both white-box and black-box classifier settings. Overall, this work paves the path towards the next generation of adversary-aware sequence classification models.
Collaborative variational autoencoder for recommender systems
Authors: Xiaopeng Li, James She
Venue: Conference KDD 2017
Year: 2017

Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their methods are very limited in current multimedia scenario. This paper proposes a Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario. The model learns deep latent representations from content data in an unsupervised manner and also learns implicit relationships between items and users from both content and rating. Unlike previous works with denoising criteria, the proposed CVAE learns a latent distribution for content in latent space instead of observation space through an inference network and can be easily extended to other multimedia modalities other than text. Experiments show that CVAE is able to significantly outperform the state-of-the-art recommendation methods with more robust performance.
Adversarial item promotion: Vulnerabilities at the core of top-n recommenders that use images to address cold start
Authors: Zhuoran Liu, Martha A. Larson
Venue: Conference WWW 2021
Year: 2021

E-commerce platforms provide their customers with ranked lists of recommended items matching the customers’ preferences. Merchants on e-commerce platforms would like their items to appear as high as possible in the top-N of these ranked lists. In this paper, we demonstrate how unscrupulous merchants can create item images that artificially promote their products, improving their rankings. Recommender systems that use images to address the cold start problem are vulnerable to this security risk. We describe a new type of attack, Adversarial Item Promotion (AIP), that strikes directly at the core of Top-N recommenders: the ranking mechanism itself. Existing work on adversarial images in recommender systems investigates the implications of conventional attacks, which target deep learning classifiers. In contrast, our AIP attacks are embedding attacks that seek to push features representations in a way that fools the ranker (not a classifier) and directly leads to item promotion. We introduce three AIP attacks insider attack, expert attack, and semantic attack, which are defined with respect to three successively more realistic attack models. Our experiments evaluate the danger of these attacks when mounted against three representative visually-aware recommender algorithms in a framework that uses images to address cold start. We also evaluate potential defenses, including adversarial training and find that common, currently-existing, techniques do not eliminate the danger of AIP attacks. In sum, we show that using images to address cold start opens recommender systems to potential threats with clear practical implications.
How useful are reviews for recommendation? a critical review and potential improvements
Authors: Noveen Sachdeva, Julian J. McAuley
Venue: Conference SIGIR 2020
Year: 2020

We investigate a growing body of work that seeks to improve recommender systems through the use of review text. Generally, these papers argue that since reviews 'explain' users' opinions, they ought to be useful to infer the underlying dimensions that predict ratings or purchases. Schemes to incorporate reviews range from simple regularizers to neural network approaches. Our initial findings reveal several discrepancies in reported results, partly due to (e.g.) copying results across papers despite changes in experimental settings or data pre-processing. First, we attempt a comprehensive analysis to resolve these ambiguities. Further investigation calls for discussion on a much larger problem about the "importance" of user reviews for recommendation. Through a wide range of experiments, we observe several cases where state-of-the-art methods fail to outperform existing baselines, especially as we deviate from a few narrowly-defined settings where reviews are useful. We conclude by providing hypotheses for our observations, that seek to characterize under what conditions reviews are likely to be helpful. Through this work, we aim to evaluate the direction in which the field is progressing and encourage robust empirical evaluation.
A symbolic and surgical acquisition of terms through variation
Authors: Christian Jacquemin
Venue: Conference IJCAI 1995
Year: 1995

Terminological acquisition is an important issue in learning for Natural Language Processing (NLP) due to the constant terminological renewal through technological changes. Terms play a key role in several NLP-activities such as machine translation, automatic indexing or text understanding. In opposition to classical once-and-for-all approaches, this paper proposes an incremental process for terminological enrichment which operates on existing reference lists and large corpora. Candidate terms are acquired by extracting variants of reference terms through FASTR (FAst Syntactic Term Recognizer), a unification-based partial parser. As acquisition is performed within specific morphosyntactic contexts (coordinations, insertions or permutations of complex nominals), rich conceptual links are learned together with candidate terms. A clustering of terms related through coordinations yields classes of conceptually close terms while graphs resulting from insertions denote generic/specific relations. A graceful degradation of the volume of acquisition on partial initial lists confirms the robustness of the method to incomplete data.
Term clustering of syntactic phrases
Authors: David D. Lewis, W. Bruce Croft
Venue: Conference SIGIR 1990
Year: 1990

Term clustering and syntactic phrase formation are methods for transforming natural language text. Both have had only mixed success as strategies for improving the quality of text representations for document retrieval. Since the strengths of these methods are complementary, we have explored combining them to produce superior representations. In this paper we discuss our implementation of a syntactic phrase generator, as well as our preliminary experiments with producing phrase clusters. These experiments show small improvements in retrieval effectiveness resulting from the use of phrase clusters, but it is clear that corpora much larger than standard information retrieval test collections will be required to thoroughly evaluate the use of this technique.
Training a selection function for extraction
Authors: Chin-Yew Lin
Venue: Conference CIKM 1999
Year: 1999

In this paper we compare performance of several heuristics in generating informative generic/query-oriented extracts for newspaper articles in order to learn how topic prominence affects the performance of each heuristic. We study how different query types can affect the performance of each heuristic and discuss the possibility of using machine learning algorithms to automatically learn good combination functions to combine several heuristics. We also briefly describe the design, implementation, and performance of a multilingual text summarization system SUMMARIST.
Learning phonetic similarity for matching named entity translations and mining new translations
Authors: Wai Lam, Ruizhang Huang, Pik-Shan Cheung
Venue: Conference SIGIR 2004
Year: 2004

We propose a novel named entity matching model which considers both semantic and phonetic clues. The matching is formulated as an optimization problem. One major component is a phonetic matching model which exploits similarity at the phoneme level. We investigate three learning algorithms for obtaining the similarity information of basic phoneme units based on training examples. By applying this proposed named entity matching model, we also develop a mining framework for discovering new, unseen named entity translations from online daily Web news. This framework harvests comparable news in different languages using an existing bilingual dictionary. It is able to discover new name translations not found in the dictionary.
Pagerank without hyperlinks: structural re-ranking using links induced by language models
Authors: Oren Kurland, Lillian Lee
Venue: Conference SIGIR 2005
Year: 2005

Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.
Extracting semantic relations from query logs
Authors: Ricardo A. Baeza-Yates, Alessandro Tiberi
Venue: Conference KDD 2007
Year: 2007

In this paper we study a large query log of more than twenty million queries with the goal of extracting the semantic relations that are implicitly captured in the actions of users submitting queries and clicking answers. Previous query log analyses were mostly done with just the queries and not the actions that followed after them. We first propose a novel way to represent queries in a vector space based on a graph derived from the query-click bipartite graph. We then analyze the graph produced by our query log, showing that it is less sparse than previous results suggested, and that almost all the measures of these graphs follow power laws, shedding some light on the searching user behavior as well as on the distribution of topics that people want in the Web. The representation we introduce allows to infer interesting semantic relationships between queries. Second, we provide an experimental analysis on the quality of these relations, showing that most of them are relevant. Finally we sketch an application that detects multitopical URLs.
Learning with constrained and unlabelled data
Authors: Tilman Lange, Martin H. C. Law, Anil K. Jain 0001, Joachim M. Buhmann
Venue: Conference CVPR 2005
Year: 2005

Classification problems abundantly arise in many computer vision tasks eing of supervised, semi-supervised or unsupervised nature. Even when class labels are not available, a user still might favor certain grouping solutions over others. This bias can be expressed either by providing a clustering criterion or cost function and, in addition to that, by specifying pairwise constraints on the assignment of objects to classes. In this work, we discuss a unifying formulation for labelled and unlabelled data that can incorporate constrained data for model fitting. Our approach models the constraint information by the maximum entropy principle. This modeling strategy allows us (i) to handle constraint violations and soft constraints, and, at the same time, (ii) to speed up the optimization process. Experimental results on face classification and image segmentation indicates that the proposed algorithm is computationally efficient and generates superior groupings when compared with alternative techniques.
Extracting data records from the web using tag path clustering
Authors: Gengxin Miao, Jun&apos, ichi Tatemura, Wang-Pin Hsiung, Arsany Sawires, Louise E. Moser
Venue: Conference WWW 2009
Year: 2009

Fully automatic methods that extract lists of objects from the Web have been studied extensively. Record extraction, the first step of this object extraction process, identifies a set of Web page segments, each of which represents an individual object (e.g., a product). State-of-the-art methods suffice for simple search, but they often fail to handle more complicated or noisy Web page structures due to a key limitation -- their greedy manner of identifying a list of records through pairwise comparison (i.e., similarity match) of consecutive segments. This paper introduces a new method for record extraction that captures a list of objects in a more robust way based on a holistic analysis of a Web page. The method focuses on how a distinct tag path appears repeatedly in the DOM tree of the Web document. Instead of comparing a pair of individual segments, it compares a pair of tag path occurrence patterns (called visual signals) to estimate how likely these two tag paths represent the same list of objects. The paper introduces a similarity measure that captures how closely the visual signals appear and interleave. Clustering of tag paths is then performed based on this similarity measure, and sets of tag paths that form the structure of data records are extracted. Experiments show that this method achieves higher accuracy than previous methods.
To personalize or not to personalize: modeling queries with variation in user intent
Authors: Jaime Teevan, Susan T. Dumais, Daniel J. Liebling
Venue: Conference SIGIR 2008
Year: 2008

In most previous work on personalized search algorithms, the results for all queries are personalized in the same manner. However, as we show in this paper, there is a lot of variation across queries in the benefits that can be achieved through personalization. For some queries, everyone who issues the query is looking for the same thing. For other queries, different people want very different results even though they express their need in the same way. We examine variability in user intent using both explicit relevance judgments and large-scale log analysis of user behavior patterns. While variation in user behavior is correlated with variation in explicit relevance judgments the same query, there are many other factors, such as result entropy, result quality, and task that can also affect the variation in behavior. We characterize queries using a variety of features of the query, the results returned for the query, and people's interaction history with the query. Using these features we build predictive models to identify queries that can benefit from personalization.
A Study of Methods for Negative Relevance Feedback
Authors: Xuanhui Wang, Hui Fang, ChengXiang Zhai
Venue: Conference SIGIR 2008
Year: 2008

Negative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search results are poor. Although in principle any standard relevance feedback technique can be applied to negative relevance feedback, it may not perform well due to the lack of positive examples. In this paper, we conduct a systematic study of methods for negative relevance feedback. We compare a set of representative negative feedback methods, covering vector-space models and language models, as well as several special heuristics for negative feedback. Evaluating negative feedback methods requires a test set with sufficient difficult topics, but there are not many naturally difficult topics in the existing test collections. We use two sampling strategies to adapt a test collection with easy topics to evaluate negative feedback. Experiment results on several TREC collections show that language model based negative feedback methods are generally more effective than those based on vector-space models, and using multiple negative models is an effective heuristic for negative feedback. Our results also show that it is feasible to adapt test collections with easy topics for evaluating negative feedback methods through sampling.
Improve retrieval accuracy for difficult queries using negative feedback
Authors: Xuanhui Wang, Hui Fang, ChengXiang Zhai
Venue: Conference CIKM 2007
Year: 2007

How to improve search accuracy for difficult topics is an under-addressed, yet important research question. In this paper, we consider a scenario when the search results are so poor that none of the top-ranked documents is relevant to a user's query, and propose to exploit negative feedback to improve retrieval accuracy for such difficult queries. Specifically, we propose to learn from a certain number of top-ranked non-relevant documents to rerank the rest unseen documents. We propose several approaches to penalizing the documents that are similar to the known non-relevant documents in the language modeling framework. To evaluate the proposed methods, we adapt standard TREC collections to construct a test collection containing only difficult queries. Experiment results show that the proposed approaches are effective for improving retrieval accuracy of difficult queries.
Optimizing web search using social annotations
Authors: Shenghua Bao, Gui-Rong Xue, Xiaoyuan Wu, Yong Yu, Ben Fei, Zhong Su
Venue: Conference WWW 2007
Year: 2007

This paper explores the use of social annotations to improve web search. Nowadays, many services, e.g. del.icio.us, have been developed for web users to organize and share their favorite web pages on line by using social annotations. We observe that the social annotations can benefit web search in two aspects: 1) the annotations are usually good summaries of corresponding web pages; 2) the count of annotations indicates the popularity of web pages. Two novel algorithms are proposed to incorporate the above information into page ranking: 1) SocialSimRank (SSR) calculates the similarity between social annotations and web queries; 2) SocialPageRank (SPR) captures the popularity of web pages. Preliminary experimental results show that SSR can find the latent semantic association between queries and annotations, while SPR successfully measures the quality (popularity) of a web page from the web users ’ perspective. We further evaluate the proposed methods empirically with 50 manually constructed queries and 3000 auto-generated queries on a dataset crawled from del.icio.us. Experiments show that both SSR and SPR benefit web search significantly.
A ranking approach to keyphrase extraction
Authors: Xin Jiang, Yunhua Hu, Hang Li
Venue: Conference SIGIR 2009
Year: 2009

This paper addresses the issue of automatically extracting keyphrases from a document. Previously, this problem was formalized as classification and learning methods for classification were utilized. This paper points out that it is more essential to cast the problem as ranking and employ a learning to rank method to perform the task. Specifically, it employs Ranking SVM, a state-of-art method of learning to rank, in keyphrase extraction. Experimental results on three datasets show that Ranking SVM significantly outperforms the baseline methods of SVM and Naive Bayes, indicating that it is better to exploit learning to rank techniques in keyphrase extraction.
Towards effective browsing of large scale social annotations
Authors: Rui Li, Shenghua Bao, Yong Yu, Ben Fei, Zhong Su
Venue: Conference WWW 2007
Year: 2007

This paper is concerned with the problem of browsing social annotations. Today, a lot of services (e.g., Del.icio.us, Filckr) have been provided for helping users to manage and share their favorite URLs and photos based on social annotations. Due to the exponential increasing of the social annotations, more and more users, however, are facing the problem how to effectively find desired resources from large annotation data. Existing methods such as tag cloud and annotation matching work well only on small annotation sets. Thus, an effective approach for browsing large scale annotation sets and the associated resources is in great demand by both ordinary users and service providers. In this paper, we propose a novel algorithm, namely Effective Large Scale Annotation Browser (ELSABer), to browse large-scale social annotation data. ELSABer helps the users browse huge number of annotations in a semantic, hierarchical and efficient way. More specifically, ELSABer has the following features: 1) the semantic relations between annotations are explored for browsing of similar resources; 2) the hierarchical relations between annotations are constructed for browsing in a top-down fashion; 3) the distribution of social annotations is studied for efficient browsing. By incorporating the personal and time information, ELSABer can be further extended for personalized and time-related browsing. A prototype system is implemented and shows promising results.