OpenRaiser/Intern-Atlas huggingface dataset:
Improving long-tail item recommendation with graph augmentation
Authors: Sichun Luo, Chen Ma, Yuanzhang Xiao, Linqi Song
Venue: Conference CIKM 2023
Year: 2023

The ubiquitous long-tail distribution of inherent user behaviors results in worse recommendation performance for the items with fewer user records (i.e., tail items) than those with richer ones (i.e., head items). Graph-based recommendation methods (e.g., using graph neural networks) have recently emerged as a powerful tool for recommender systems, often outperforming traditional methods. However, existing techniques for alleviating the long-tail problem mainly focus on traditional methods. There is a lack of graph-based methods that can efficiently deal with the long-tail problem. In this paper, we propose a novel approach, Graph Augmentation for Long-tail Recommendation (GALORE), which can be plugged into any graph-based recommendation models to improve the performance for tail items. GALORE incorporates an edge addition module that enriches the graph's connectivity for tail items by injecting additional item-to-item edges. To further balance the graph structure, GALORE utilizes a degree-aware edge dropping strategy, preserving the more valuable edges from the tail items while selectively discarding less informative edges from the head items. Beyond structural augmentation, we synthesize new data samples, thereby addressing the data scarcity issue for tail items. We further introduce a two-stage training strategy to facilitate the learning for both head and tail items. Comprehensive empirical studies conducted on four datasets show that GALORE outperforms existing methods in terms of the performance for tail items as well as the overall performance.
Visual positions of links and clicks on wikipedia
Authors: Dimitar Dimitrov, Philipp Singer, Florian Lemmerich, Markus Strohmaier
Venue: Conference WWW 2016
Year: 2016

In this work, we study the visual position of links and their clicks on Wikipedia, particularly where links are visually located, at which screen positions users click on links, and which areas on the screen exhibit more or less clicks per links. For that purpose, we introduce a novel dataset containing the on-screen coordinate position for all links between pages in the English Wikipedia and additionally resort to navigation logs of Wikipedia users. Using this data, we can observe a preference of certain link and click locations on Wikipedia including first evidence of positional click bias. For example, our results suggest that users have a tendency to prefer to click on the left side of the screen which exceeds what one would expect from the presence of links on pages. We believe that presented data and research can be useful for optimizing the process of link creation and link consumption on Wikipedia and other Web platforms.
Discovering invariant and changing mechanisms from data
Authors: Sarah Mameche, David Kaltenpoth, Jilles Vreeken
Venue: Conference KDD 2022
Year: 2022

While invariance of causal mechanisms has inspired recent work in both robust machine learning and causal inference, causal mechanisms may also vary over domains due to, for example, population-specific differences, the context of data collection, or intervention. To discover invariant and changing mechanisms from data, we propose extending the algorithmic model for causation to mechanism changes and instantiating it using Minimum Description Length. In essence, for a continuous variable Y in multiple contexts C, we identify variables X as causal if the regression functions g : X → Y have succinct descriptions in all contexts. In empirical evaluations we show that our method, VARIO, finds invariant variable sets, reveals mechanism changes, and discovers causal networks, such as on real-world data that gives insight into the signaling pathways in human immune cells.
When imbalance meets imbalance: Structure-driven learning for imbalanced graph classification
Authors: Wei Xu, Pengkun Wang, Zhe Zhao, Binwu Wang, Xu Wang, Yang Wang
Venue: Conference WWW 2024
Year: 2024

Graph Neural Networks (GNNs) can learn representative graph-level features to achieve efficient graph classification. But GNNs usually assume an environment where both class and structure distribution are balanced. Although previous works have considered the graph classification problem under the scenario of class imbalance or structure imbalance, they habitually ignored the obvious fact that class imbalance and structural imbalance are often intertwined in the real world. In this paper, we propose a carefully designed structure-driven learning framework called ImbGNN to address the potential intertwined class imbalance and structural imbalance in graph classification. Specifically, we find that feature-oriented augmentation (e.g., feature masking) and structure-oriented augmentation (e.g., edge perturbation) will have differential impacts when applied to different graphs. Therefore, we design optional augmentation based on the average degree distribution to alleviate structural imbalance. Furthermore, based on the imbalance of graph size distribution, we utilize a similarity-friendly graph random walk to extract a core subgraph to improve the accuracy of graph kernel similarity calculation, and then construct a more reasonable kernel-based graph of graphs, thereby alleviating the class imbalance and size imbalance. Extensive experiments on multiple benchmark datasets demonstrate that our proposed ImbGNN framework outperforms previous baselines on imbalanced graph classification tasks. The code of ImbGNN is available in~https://github.com/Xiaovy/ImbGNN.
Meta-learning helps personalized product search
Authors: Bin Wu, Zaiqiao Meng, Qiang Zhang, Shangsong Liang
Venue: Conference WWW 2022
Year: 2022

Personalized product search that provides users with customized search services is an important task for e-commerce platforms. This task remains a challenge when inferring users’ preferences from few records or even no records, which is also known as the few-shot or zero-shot learning problem. In this paper, we propose a Bayesian Online Meta-Learning Model (BOML), which transfers meta-knowledge, from the inference for other users’ preferences, to help to infer the current user’s interest behind her/his few or even no historical records. To extract meta-knowledge from various inference patterns, our model constructs a mixture of meta-knowledge and transfers the corresponding meta-knowledge to the specific user according to her/his records. Based on the meta-knowledge learned from other similar inferences, our proposed model searches a ranked list of products to meet users’ personalized query intents for those with few search records (i.e., few-shot learning problem) or even no search records (i.e., zero-shot learning problem). Under the records arriving sequentially setting, we propose an online variational inference algorithm to update meta-knowledge over time. Experimental results demonstrate that our proposed BOML outperforms state-of-the-art algorithms.
Connecting unseen domains: Cross-domain invariant learning in recommendation
Authors: Yang Zhang, Yue Shen, Dong Wang, Jinjie Gu, Guannan Zhang
Venue: Conference SIGIR 2023
Year: 2023

As web applications continue to expand and diversify their services, user interactions exist in different scenarios. To leverage this wealth of information, cross-domain recommendation (CDR) has gained significant attention in recent years. However, existing CDR approaches mostly focus on information transfer between observed domains, with little attention paid to generalizing to unseen domains. Although recent research on invariant learning can help for the purpose of generalization, relying only on invariant preference may be overly conservative and result in mediocre performance when the unseen domain shifts slightly. In this paper, we present a novel framework that considers both CDR and domain generalization through a united causal invariant view. We assume that user interactions are determined by domain-invariant preference and domain-specific preference. The proposed approach differentiates the invariant preference and the specific preference from observational behaviors in a way of adversarial learning. Additionally, a novel domain routing module is designed to connect unseen domains to observed domains. Extensive experiments on public and industry datasets have proved the effectiveness of the proposed approach under both CDR and domain generalization settings.
Frankenplace: interactive thematic mapping for ad hoc exploratory search
Authors: Benjamin Adams, Grant McKenzie, Mark Gahegan
Venue: Conference WWW 2015
Year: 2015

Ad hoc keyword search engines built using modern information retrieval methods do a good job of handling fine-grained queries. However, they perform poorly at facilitating spatial and spatially-embedded thematic exploration of the results, despite the fact that many queries, e.g. "civil war," refer to different documents and topics in different places. This is not for lack of data: geographic information, such as place names, events, and coordinates are common in unstructured document collections on the web. The associations between geographic and thematic contents in these documents can provide a rich groundwork to organize information for exploratory research. In this paper we describe the architecture of an interactive thematic map search engine, Frankenplace, designed to facilitate document exploration at the intersection of theme and place. The map interface enables a user to zoom the geographic context of their query in and out, and quickly explore through thousands of search results in a meaningful way. And by combining topic models with geographically contextualized search results, users can discover related topics based on geographic context. Frankenplace utilizes a novel indexing method called geoboost for boosting terms associated with cells on a discrete global grid. The resulting index factors in the geographic scale of the place or feature mentioned in related text, the relative textual scope of the place reference, and the overall importance of the containing document in the document network. The system is currently indexed with over 5 million documents from the web, including the English Wikipedia and online travel blog entries. We demonstrate that Frankenplace can support four distinct types of exploratory search tasks while being adaptive to scale and location of interest.
Learning grid cells as vector representation of self-position coupled with matrix representation of self-motion
Authors: Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu
Venue: Conference ICLR 2019
Year: 2019

This paper proposes a representational model for grid cells. In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector. Each component of the vector is a unit or a cell. The model consists of the following three sub-models. (1) Vector-matrix multiplication. The movement from the current position to the next position is modeled by matrix-vector multiplication, i.e., the vector of the next position is obtained by multiplying the matrix of the motion to the vector of the current position. (2) Magnified local isometry. The angle between two nearby vectors equals the Euclidean distance between the two corresponding positions multiplied by a magnifying factor. (3) Global adjacency kernel. The inner product between two vectors measures the adjacency between the two corresponding positions, which is defined by a kernel function of the Euclidean distance between the two positions. Our representational model has explicit algebra and geometry. It can learn hexagon patterns of grid cells, and it is capable of error correction, path integral and path planning.
A Prerequisite Attention Model for Knowledge Proficiency Diagnosis of Students
Authors: Haiping Ma, Jinwei Zhu, Shangshang Yang, Qi Liu, Haifeng Zhang, Xingyi Zhang, Yunbo Cao, Xuemin Zhao
Venue: Conference CIKM 2022
Year: 2022

With the rapid development of intelligent education platforms, how to enhance the performance of diagnosing students' knowledge proficiency has become an important issue, e.g., by incorporating the prerequisite relation of knowledge concepts. Unfortunately, the differentiated influence from different predecessor concepts to successor concepts is still underexplored in existing approaches. To this end, we propose a Prerequisite Attention model for Knowledge Proficiency diagnosis of students (PAKP) to learn the attentive weights of precursor concepts on successor concepts and model it for inferring the knowledge proficiency. Specifically, given the student response records and knowledge prerequisite graph, we design an embedding layer to output the representations of students, exercises, and concepts. Influence coefficient among concepts is calculated via an efficient attention mechanism in a fusion layer. Finally, the performance of each student is predicted based on the mined student and exercise factors. Extensive experiments on real-data sets demonstrate that PAKP exhibits great efficiency and interpretability advantages without accuracy loss.
Exploring personal impact for group recommendation
Authors: Xingjie Liu, Yuan Tian, Mao Ye, Wang-Chien Lee
Venue: Conference CIKM 2012
Year: 2012

Group activities are essential ingredients of people's social life. The rapid growth of online social networking services has greatly boosted group activities by providing convenient platform for users to organize and participate in such activities. Therefore, recommender systems, as a critical component in social networking services, now face new challenges in supporting group activities. In this paper, we study the group recommendation problem, i.e., making recommendations to a group of people in social networking services. We analyze the decision making process in a group to propose a personal impact topic (PIT) model for group recommendations. The PIT model effectively identifies the group preference profile for a given group by considering the personal preferences and personal impacts of group members. Moreover, we further enhance the discovery of personal impact with social network information to obtain an extended personal impact topic (E-PIT) model. We have conducted comprehensive data analysis and evaluations on three real datasets. The results show that our proposed group recommendation techniques outperform baseline approaches.
Dual Intents Graph Modeling for User-centric Group Discovery
Authors: Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang
Venue: Conference CIKM 2023
Year: 2023

Online groups have become increasingly prevalent, providing users with space to share experiences and explore interests. Therefore, user-centric group discovery task, i.e., recommending groups to users can help both users' online experiences and platforms' long-term developments. Existing recommender methods can not deal with this task as modeling user-group participation into a bipartite graph overlooks their item-side interests. Although there exist a few works attempting to address this task, they still fall short in fully preserving the social context and ensuring effective interest representation learning. In this paper, we focus on exploring the intents that motivate users to participate in groups, which can be categorized into different types, like the social-intent and the personal interest-intent. The former refers to users joining a group affected by their social links, while the latter relates to users joining groups with like-minded people for interest-oriented self-enjoyment. To comprehend different intents, we propose a novel model, DiRec, that first models each intent separately and then fuses them together for predictions. Specifically, for social-intent, we introduce the hypergraph structure to model the relationship between groups and members. This allows for more comprehensive group information preservation, leading to a richer understanding of the social context. As for interest-intent, we employ novel structural refinement on the interactive graph to uncover more intricate user behaviors, item characteristics, and group interests, realizing better representation learning of interests. Furthermore, we also observe the intent overlapping in real-world scenarios and devise a novel self-supervised learning loss that encourages such alignment for final recommendations. Extensive experiments on three public datasets show the significant improvement of DiRec over the state-of-the-art methods.
Beyond individuals: Modeling mutual and multiple interactions for inductive link prediction between groups
Authors: Gongzhu Yin, Xing Wang, Hongli Zhang, Chao Meng, Yuchen Yang, Kun Lu, Yi Luo
Venue: Conference WSDM 2023
Year: 2023

Link prediction is a core task in graph machine learning with wide applications. However, little attention has been paid to link prediction between two group entities. This limits the application of the current approaches to many real-life problems, such as predicting collaborations between academic groups or recommending bundles of items to group users. Moreover, groups are often ephemeral or emergent, forcing the predicting model to deal with challenging inductive scenes. To fill this gap, we develop a framework composed of a GNN-based encoder and neural-based aggregating networks, namely the Mutual Multi-view Attention Networks (MMAN). First, we adopt GNN-based encoders to model multiple interactions among members and groups through propagating. Then, we develop MMAN to aggregate members' node representations into multi-view group representations and compute the final results by pooling pairwise scores between views. Specifically, several view-guided attention modules are adopted when learning multi-view group representations, thus capturing diversified member weights and multifaceted group characteristics. In this way, MMAN can further mimic the mutual and multiple interactions between groups. We conduct experiments on three datasets, including two academic group link prediction datasets and one bundle-to-group recommendation dataset. The results demonstrate that the proposed approach can achieve superior performance on both tasks compared with plain GNN-based methods and other aggregating methods.
COM: a generative model for group recommendation
Authors: Quan Yuan, Gao Cong, Chin-Yew Lin
Venue: Conference KDD 2014
Year: 2014

With the rapid development of online social networks, a growing number of people are willing to share their group activities, e.g. having dinners with colleagues, and watching movies with spouses. This motivates the studies on group recommendation, which aims to recommend items for a group of users. Group recommendation is a challenging problem because different group members have different preferences, and how to make a trade-off among their preferences for recommendation is still an open problem. In this paper, we propose a probabilistic model named COM (COnsensus Model) to model the generative process of group activities, and make group recommendations. Intuitively, users in a group may have different influences, and those who are expert in topics relevant to the group are usually more influential. In addition, users in a group may behave differently as group members from as individuals. COM is designed based on these intuitions, and is able to incorporate both users' selection history and personal considerations of content factors. When making recommendations, COM estimates the preference of a group to an item by aggregating the preferences of the group members with different weights. We conduct extensive experiments on four datasets, and the results show that the proposed model is effective in making group recommendations, and outperforms baseline methods significantly.
Reproducibility, replicability, and insights into dense multi-representation retrieval models: from colbert to col
Authors: Xiao Wang, Craig Macdonald, Nicola Tonellotto, Iadh Ounis
Venue: Conference SIGIR 2023
Year: 2023

Dense multi-representation retrieval models, exemplified as ColBERT, estimate the relevance between a query and a document based on the similarity of their contextualised token-level embeddings. Indeed, by using contextualised token embeddings, dense retrieval, conducted as either exact or semantic matches, can result in increased effectiveness for both in-domain and out-of-domain retrieval tasks, indicating that it is an important model to study. However, the exact role that these semantic matches play is not yet well investigated. For instance, although tokenisation is one of the crucial design choices for various pretrained language models, its impact on the matching behaviour has not been examined in detail. In this work, we inspect the reproducibility and replicability of the contextualised late interaction mechanism by extending ColBERT to Col⋆ which implements the late interaction mechanism across various pretrained models and different types of tokenisers. As different tokenisation methods can directly impact the matching behaviour within the late interaction mechanism, we study the nature of matches occurring in different Col⋆ models, and further quantify the contribution of lexical and semantic matching on retrieval effectiveness. Overall, our experiments successfully reproduce the performance of ColBERT on various query sets, and replicate the late interaction mechanism upon different pretrained models with different tokenisers. Moreover, our experimental results yield new insights, such as: (i) semantic matching behaviour varies across different tokenisers; (ii) more specifically, high-frequency tokens tend to perform semantic matching than other token families; (iii) late interaction mechanism benefits more from lexical matching than semantic matching; (iv) special tokens, such as [CLS], play a very important role in late interaction.
Evaluating interpolation and extrapolation performance of neural retrieval models
Authors: Jingtao Zhan, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
Venue: Conference CIKM 2022
Year: 2022

A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models have demonstrated impressive performance on ad-hoc search benchmarks, we still know little about how they perform in terms of interpolation and extrapolation. In this paper, we demonstrate the importance of separately evaluating the two capabilities of neural retrieval models. Firstly, we examine existing ad-hoc search benchmarks from the two perspectives. We investigate the distribution of training and test data and find a considerable overlap in query entities, query intent, and relevance labels. This finding implies that the evaluation on these test sets is biased toward interpolation and cannot accurately reflect the extrapolation capacity. Secondly, we propose a novel evaluation protocol to separately evaluate the interpolation and extrapolation performance on existing benchmark datasets. It resamples the training and test data based on query similarity and utilizes the resampled dataset for training and evaluation. Finally, we leverage the proposed evaluation protocol to comprehensively revisit a number of widely-adopted neural retrieval models. Results show models perform differently when moving from interpolation to extrapolation. For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation. Therefore, it is necessary to separately evaluate both interpolation and extrapolation performance and the proposed resampling method serves as a simple yet effective evaluation tool for future IR studies.