OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial Graph Perturbations for Recommendations at Scale
Authors: Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai, Xia Hu, Fei Wang, Hao Yang
Venue: Conference SIGIR 2022
Year: 2022

Graph Neural Networks (GNNs) provide a class of powerful architectures that are effective for graph-based collaborative filtering. Nevertheless, GNNs are known to be vulnerable to adversarial perturbations. Adversarial training is a simple yet effective way to improve the robustness of neural models. For example, many prior studies inject adversarial perturbations into either node features or hidden layers of GNNs. However, perturbing graph structures has been far less studied in recommendations. To bridge this gap, we propose AdvGraph to model adversarial graph perturbations during the training of GNNs. Our AdvGraph is mainly based on min-max robust optimization, where an universal graph perturbation is obtained through an inner maximization while the outer optimization aims to compute the model parameters of GNNs. However, direct optimizing the inner problem is challenging due to discrete nature of the graph perturbations. To address this issue, an unbiased gradient estimator is further proposed to compute the gradients of discrete variables. Extensive experiments demonstrate that our AdvGraph is able to enhance the generalization performance of GNN-based recommenders.
Recommender system using sequential and global preference via attention mechanism and topic modeling
Authors: Kyeongpil Kang, Junwoo Park, Wooyoung Kim, Hojung Choe, Jaegul Choo
Venue: Conference CIKM 2019
Year: 2019

Deep neural networks improved the accuracy of sequential recommendation approach which takes into account the sequential patterns of user logs, e.g., a purchase history of a user. However, incorporating only the individual's recent logs may not be sufficient in properly reflecting global preferences and trends across all users and items. In response, we propose a self-attentive sequential recommender system with topic modeling-based category embedding as a novel approach to exploit global information in the process of sequential recommendation. Our self-attention module effectively leverages the sequential patterns from the user's recent history. In addition, our novel category embedding approach, which utilizes the information computed by topic modeling, efficiently captures global information that the user generally prefers. Furthermore, to provide diverse recommendations as well as to prevent overfitting, our model also incorporates a vector obtained by random sampling. Experimental studies show that our model outperforms state-of-the-art sequential recommendation models, and that category embedding effectively provides global preference information.
Global and local influence-based social recommendation
Authors: Qinzhe Zhang, Jia Wu, Hong Yang, Weixue Lu, Guodong Long, Chengqi Zhang
Venue: Conference CIKM 2016
Year: 2016

Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms in recommendation, for instance, social links are considered as new constraints. However, social influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global and local influence-based social recommendation model. Based on the observation that user purchase behaviour is influenced by both global influential nodes and the local influential nodes of the user, we formulate the global and local influence as an regularization terms, and incorporate them into a matrix factorization-based recommendation model. Experimental results on large data sets demonstrate the performance of the proposed method.
Variational User Modeling with Slow and Fast Features
Authors: Ghazal Fazelnia, Eric Simon, Ian Anderson, Benjamin A. Carterette, Mounia Lalmas
Venue: Conference WSDM 2022
Year: 2022

Recommender systems play a key role in helping users find their favorite music to play among an often extremely large catalog of items on online streaming services. To correctly identify users' interests, recommendation algorithms rely on past user behavior and feedback to aim at learning users' preferences through the logged interactions. User modeling is a fundamental part of this large-scale system as it enables the model to learn an optimal representation for each user. For instance, in music recommendation, the focus of this paper, users' interests at any time is shaped by their general preferences for music as well as their recent or momentary interests in a particular type of music. In this paper, we present a novel approach for learning user representation based on general and slow-changing user interests as well as fast-moving current preferences. We propose a variational autoencoder-based model that takes fast and slow-moving features and learns an optimal user representation. Our model, which we call FS-VAE, consists of sequential and non-sequential encoders to capture patterns in user-item interactions and learn users' representations. We evaluate FS-VAE on a real-world music streaming dataset. Our experimental results show a clear improvement in learning optimal representations compared to state-of-the-art baselines on the next item recommendation task. We also demonstrate how each of the model components, slow input feature, and fast ones play a role in achieving the best results in next item prediction and learning users' representations.
HeteroCS: A Heterogeneous Community Search System With Semantic Explanation
Authors: Weibin Cai, Fanwei Zhu, Zemin Liu, Minghui Wu
Venue: Conference SIGIR 2023
Year: 2023

Community search, which looks for query-dependent communities in a graph, is an important task in graph analysis. Existing community search studies address the problem by finding a densely-connected subgraph containing the query. However, many real-world networks are heterogeneous with rich semantics. Queries in heterogeneous networks generally involve in multiple communities with different semantic connections, while returning a single community with mixed semantics has limited applications. In this paper, we revisit the community search problem on heterogeneous networks and introduce a novel paradigm of heterogeneous community search and ranking. We propose to automatically discover the query semantics to enable the search of different semantic communities and develop a comprehensive community evaluation model to support the ranking of results. We build HeteroCS, a heterogeneous community search system with semantic explanation, upon our semantic community model, and deploy it on two real-world graphs. We present a demonstration case to illustrate the novelty and effectiveness of the system.
Causality and independence enhancement for biased node classification
Authors: Guoxin Chen, Yongqing Wang, Fangda Guo, Qinglang Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng
Venue: Conference CIKM 2023
Year: 2023

Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias. However, anticipating the type of bias in advance is extremely challenging, and designing models solely for one specific type may not necessarily improve overall generalization performance. Moreover, limited research has focused on the impact of mixed biases, which are more prevalent and demanding in real-world scenarios. To address these limitations, we propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs). Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations through the backdoor adjustment. Meanwhile, independence constraint is introduced to improve the discriminability and stability of causal and spurious features in complex biased environments. Essentially, CIE eliminates different types of data biases from a unified perspective, without the need to design separate methods for each bias as before. To evaluate the performance under specific types of data biases, mixed biases, and low-resource scenarios, we conducted comprehensive experiments on five publicly available datasets. Experimental results demonstrate that our approach CIE not only significantly enhances the performance of GNNs but outperforms state-of-the-art debiased node classification methods.
XKnowSearch!: Exploiting Knowledge Bases for Entity-based Cross-lingual Information Retrieval
Authors: Lei Zhang, Michael Färber, Achim Rettinger
Venue: Conference CIKM 2016
Year: 2016

In recent years, the amount of entities in large knowledge bases available on the Web has been increasing rapidly, making it possible to propose new ways of intelligent information access. Within the context of globalization, there is a clear need for techniques and systems that can enable multilingual and cross-lingual information access. In this paper, we present XKnowSearch!, a novel entity-based system for multilingual and cross-lingual information retrieval, which supports keyword search and also allows users to influence the search process according to their search intents. By leveraging the multilingual knowledge base on the Web, keyword queries and documents can be represented in their semantic forms, which can facilitate query disambiguation and expansion, and can also overcome the language barrier between queries and documents in different languages.
Event early embedding: predicting event volume dynamics at early stage
Authors: Zhiwei Liu, Yang Yang, Zi Huang, Fumin Shen, Dongxiang Zhang, Heng Tao Shen
Venue: Conference SIGIR 2017
Year: 2017

Social media has become one of the most credible sources for delivering messages, breaking news, as well as events. Predicting the future dynamics of an event at a very early stage is significantly valuable, e.g, helping company anticipate marketing trends before the event becomes mature. However, this prediction is non-trivial because a) social events always stay with "noise'' under the same topic and b) the information obtained at its early stage is too sparse and limited to support an accurate prediction. In order to overcome these two problems, in this paper, we design an event early embedding model (EEEM) that can 1) extract social events from noise, 2) find the previous similar events, and 3) predict future dynamics of a new event. Extensive experiments conducted on a large-scale dataset of Twitter data demonstrate the capacity of our model on extract events and the promising performance of prediction by considering both volume information as well as content information.
CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation
Authors: Chong Liu, Xiaoyang Liu, Rongqin Zheng, Lixin Zhang, Xiaobo Liang, Juntao Li 0005, Lijun Wu 0003, Min Zhang 0005, Leyu Lin
Venue: Conference KDD 2023
Year: 2023

Sequential recommendation methods are increasingly important in cutting-edge recommender systems. Through leveraging historical records, the systems can capture user interests and perform recommendations accordingly. State-of-the-art sequential recommendation models proposed very recently combine contrastive learning techniques for obtaining high-quality user representations. Though effective and performing well, the models based on contrastive learning require careful selection of data augmentation methods and pretext tasks, efficient negative sampling strategies, and massive hyper-parameters validation. In this paper, we propose an ultra-simple alternative for obtaining better user representations and improving sequential recommendation performance. Specifically, we present a simple yet effective \textbf{C}onsistency \textbf{T}raining method for sequential \textbf{Rec}ommendation (CT4Rec) in which only two extra training objectives are utilized without any structural modifications and data augmentation. Experiments on three benchmark datasets and one large newly crawled industrial corpus demonstrate that our proposed method outperforms SOTA models by a large margin and with much less training time than these based on contrastive learning. Online evaluation on real-world content recommendation system also achieves 2.717\% improvement on the click-through rate and 3.679\% increase on the average click number per capita. Further exploration reveals that such a simple method has great potential for CTR prediction. Our code is available at \url{https://github.com/ct4rec/CT4Rec.git}.
Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation
Authors: Vincenzo Paparella, Vito Walter Anelli, Franco Maria Nardini, Raffaele Perego, Tommaso Di Noia
Venue: Conference CIKM 2023
Year: 2023

Information Retrieval (IR) and Recommender Systems (RS) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution from the frontier. In detail, PDU analyzes the distribution of the points by investigating how far each point is from its utopia point (the ideal performance for the objectives). The possibility of considering fine-grained utopia points allows PDU to select solutions tailored to individual user preferences, a novel feature we call "calibration". We compare PDU against existing state-of-the-art strategies through extensive experiments on tasks from both IR and RS. Experimental results show that PDU and combined with calibration notably impact the solution selection. Furthermore, the results show that the proposed framework selects a solution in a principled way, irrespective of its position on the frontier, thus overcoming the limits of other strategies.
Slate-Aware Ranking for Recommendation
Authors: Yi Ren, Xiao Han, Xu Zhao, Shenzheng Zhang, Yan Zhang
Venue: Conference WSDM 2023
Year: 2023

We see widespread adoption of slate recommender systems, where an ordered item list is fed to the user based on the user interests and items' content. For each recommendation, the user can select one or several items from the list for further interaction. In this setting, the significant impact on user behaviors from the mutual influence among the items is well understood. The existing methods add another step of slate re-ranking after the ranking stage of recommender systems, which considers the mutual influence among recommended items to re-rank and generate the recommendation results so as to maximize the expected overall utility. However, to model the complex interaction of multiple recommended items, the re-ranking stage usually can just handle dozens of candidates because of the constraint of limited hardware resource and system latency. Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage. In this paper, we propose a solution named Slate-Aware ranking (SAR ) for the ranking stage. By implicitly considering the relations among the slate items, it significantly enhances the quality of the re-ranking stage's candidate set and boosts the relevance and diversity of the overall recommender systems. Both experiments with the public datasets and internal online A/B testing are conducted to verify its effectiveness.
On-device Integrated Re-ranking with Heterogeneous Behavior Modeling
Authors: Yunjia Xi, Weiwen Liu, Yang Wang 0019, Ruiming Tang, Weinan Zhang 0001, Yue Zhu, Rui Zhang 0003, Yong Yu 0001
Venue: Conference KDD 2023
Year: 2023

As an emerging field driven by industrial applications, integrated re-ranking combines lists from upstream sources into a single list, and presents it to the user. The quality of integrated re-ranking is especially sensitive to real-time user behaviors and preferences. However, existing methods are all built on the cloud-to-edge framework, where mixed lists are generated by the cloud model and then sent to the devices. Despite its effectiveness, such a framework fails to capture users' real-time preferences due to the network bandwidth and latency. Hence, we propose to place the integrated re-ranking model on devices, allowing for the full exploitation of real-time behaviors. To achieve this, we need to address two key issues: first, how to extract users' preferences for different sources from heterogeneous and imbalanced user behaviors; second, how to explore the correlation between the extracted personalized preferences and the candidate items. In this work, we present the first on-Device Integrated Re-ranking framework, DIR, to avoid delays in processing real-time user behaviors. DIR includes a multi-sequence behavior modeling module to extract the user's source-level preferences, and a preference-adaptive re-ranking module to incorporate personalized source-level preferences into the re-ranking of candidate items. Besides, we design exposure loss and utility loss to jointly optimize exposure fairness and overall utility. Extensive experiments on three datasets show that DIR significantly outperforms the state-of-the-art baselines in utility-based and fairness-based metrics.
Integrated Ranking for News Feed with Reinforcement Learning
Authors: Menghui Zhu, Wei Xia, Weiwen Liu, Yifan Liu, Ruiming Tang, Weinan Zhang
Venue: Conference WWW 2023
Year: 2023

With the development of recommender systems, it becomes an increasingly common need to mix multiple item sequences from different sources. Therefore, the integrated ranking stage is proposed to be responsible for this task with re-ranking models. However, existing methods ignore the relation between the sequences, thus resulting in local optimum over the interaction session. To resolve this challenge, in this paper, we propose a new model named NFIRank (News Feed Integrated Ranking with reinforcement learning) and formulate the whole interaction session as a MDP (Markov Decision Process). Sufficient offline experiments are provided to verify the effectiveness of our model. In addition, we deployed our model on Huawei Browser and gained 1.58% improvements in CTR compared with the baseline in online A/B test. Code will be available at https://gitee.com/mindspore/models/tree/master/research/recommend/NFIRank.
Applying probabilistic term weighting to OCR text in the case of a large alphabetic library catalogue
Authors: Elke Mittendorf, Peter Schäuble, Paraic Sheridan
Venue: Conference SIGIR 1995
Year: 1995

We report on a probabilistic weighting approach to indexing the scanned images of very short documents. This fully automatic process copes with short and very noisy texts (67% word accuracy) derived from the images by Optical Character Recognition (OCR). The probabilistic term weighting approach is based on a theoretical proof explaining how the retrieval effectiveness is affected by recognition errors. We have evaluated our probabilistic weighting approach on a sample of index cards from an alphabetic library catalogue where, on the average, a card contains only 23 terms. We have demonstrated over 30% improvement in retrieval effectiveness over a conventional weighted retrieval method where the recognition errors are not taken into account. We also show how we can take advantage of the ordering information of the alphabetic library catalogue. 1 Introduction A part of a large library catalogue has been captured by scanning and applying Optical Character Recognition (OCR) to obtain fo...
Little words can make a big difference for text classification
Authors: Ellen Riloff
Venue: Conference SIGIR 1995
Year: 1995

Most information retrieval systems use stopword lists and stemming algorithms. However, we have found that recognizing singular and plural nouns, verb forms, negation, and prepositions can produce dramatically different text classification results. We present results from text classification experiments that compare relevancy signatures, which use local linguistic context, with corresponding indexing terms that do not. In two different domains, relevancy signatures produced better results than the simple indexing terms. These experiments suggest that stopword lists and stemming algorithms may remove or conflate many words that could be used to create more effective indexing terms. Introduction Most information retrieval systems use a stopword list to prevent common words from being used as indexing terms. Highly frequent words, such as determiners and prepositions, are not considered to be content words because they appear in virtually every document. Stopword lists are almost univer...
Document clustering with committees
Authors: Patrick Pantel, Dekang Lin
Venue: Conference SIGIR 2002
Year: 2002

Document clustering is useful in many information retrieval tasks: document browsing, organization and viewing of retrieval results, generation of Yahoo-like hierarchies of documents, etc. The general goal of clustering is to group data elements such that the intra-group similarities are high and the inter-group similarities are low. We present a clustering algorithm called CBC (Clustering By Committee) that is shown to produce higher quality clusters in document clustering tasks as compared to several well known clustering algorithms. It initially discovers a set of tight clusters (high intra-group similarity), called committees, that are well scattered in the similarity space (low inter-group similarity). The union of the committees is but a subset of all elements. The algorithm proceeds by assigning elements to their most similar committee. Evaluating cluster quality has always been a difficult task. We present a new evaluation methodology that is based on the editing distance between output clusters and manually constructed classes (the answer key). This evaluation measure is more intuitive and easier to interpret than previous evaluation measures.
Hits on question answer portals: exploration of link analysis for author ranking
Authors: Pawel Jurczyk, Eugene Agichtein
Venue: Conference SIGIR 2007
Year: 2007

Question-Answer portals such as Naver and Yahoo! Answers are growing in popularity. However, despite the increased popularity, the quality of answers is uneven, and while some users usually provide good answers, many others often provide bad answers. Hence, estimating the authority, or the expected quality of users, is a crucial task for this emerging domain, with potential applications to answer ranking and to incentive mechanism design. We adapt a powerful link analysis methodology from the web domain as a first step towards estimating authority in Question Answer portals. Our experimental results over more than 3 million answers from Yahoo! Answers are promising, and warrant further exploration along the lines outlined in this poster.