OpenRaiser/Intern-Atlas huggingface dataset:
BRB-KMeans: Enhancing Binary Data Clustering for Binary Product Quantization
Authors: Suwon Lee, Sang-Min Choi
Venue: SIGIR
Year: 2024

In Binary Product Quantization (BPQ), where product quantization is applied to binary data, the traditional k-majority method is used for clustering, with centroids determined based on Hamming distance and majority vote for each bit. However, this approach often leads to a degradation in clustering quality, negatively impacting BPQ's performance. To address these challenges, we introduce Binary-to-Real-and-Back K-Means (BRB-KMeans), a novel method that initially transforms binary data into real-valued vectors, performs k-means clustering on these vectors, and then converts the generated centroids back into binary data. This innovative approach significantly enhances clustering quality by leveraging the high clustering quality of k-means in the real-valued vector space, thereby facilitating future quantization for binary data. Through extensive experiments, we demonstrate that BRB-KMeans significantly enhances clustering quality and overall BPQ performance, notably outperforming traditional methods.
Multi-view Mixed Attention for Contrastive Learning on Hypergraphs
Authors: Jongsoo Lee, Dong-Kyu Chae
Venue: SIGIR
Year: 2024

Hypergraphs are effective in learning high-order relationships between nodes, which naturally represent group interactions as hyperedges (i.e., arbitrary-sized subsets of nodes). However, most approaches currently used for learning hypergraph representations do not consider pairwise relationships between nodes. While high-order relationships provide insight into the general connections among nodes in a group, they do not reveal the pairwise relationships between individual nodes within that group. Considering that it is unlikely for all nodes in the same group to share identical relationships, we argue that considering pairwise relationships is a critical aspect. In this paper, we propose Multi-view Mixed Attention for Contrastive Learning (MMACL) to address the aforementioned problem. MMACL proposes Mixed-Attention, which blends high-order relationships derived from the hypergraph attention network and pairwise relationships derived from the graph attention network. Then, it performs node-level contrastive learning to the graph structure with different views learned at each layer to finally obtain an expressive node representation. Our extensive experimental results on several popular datasets validate the effectiveness of the proposed MMACL for hypergraph node classification. Our code is available at: https://github.com/JongsooLee-HYU/MMACL
ConvLogRecaller: Real-Time Conversational Lifelog Recaller
Authors: Yuan-Chi Lee, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: SIGIR
Year: 2024

The popularization of networks fosters the convenience of communication. People can easily share their life experiences and thoughts with relatives and friends via instant messaging software. As time passes, individuals may forget certain details of life events, leading to difficulties in effectively communicating with others. The propensity of individuals to forget or mix up life events highlights the importance of services aimed at retrieving information about past experiences. This paper presents a conversational information recall system, ConvLogRecaller, which proactively supports real-time memory recall assistance during online conversations. Given a conversation of the user with others, ConvLogRecaller suggests a message if the user forgets the details of the life experiences. The services provided by our system can avoid hesitations or memory lapses that might hinder the efficiency of a conversation.
A Large Scale Test Corpus for Semantic Table Search
Authors: Aristotelis Leventidis, Martin Pekár Christensen, Matteo Lissandrini, Laura Di Rocco, Katja Hose, Renée J. Miller
Venue: SIGIR
Year: 2024

Table search aims to answer a query with a ranked list of tables. Unfortunately, current test corpora have focused mostly on needle-in-the-haystack tasks, where only a few tables are expected to exactly match the query intent. Instead, table search tasks often arise in response to the need for retrieving new datasets or augmenting existing ones, e.g., for data augmentation within data science or machine learning pipelines. Existing table repositories and benchmarks are limited in their ability to test retrieval methods for table search tasks. Thus, to close this gap, we introduce a novel dataset for query-by-example Semantic Table Search. This novel dataset consists of two snapshots of the large-scale Wikipedia tables collection from 2013 and 2019 with two important additions: (1) a page and topic aware ground truth relevance judgment and (2) a large-scale DBpedia entity linking annotation. Moreover, we generate a novel set of entity-centric queries that allows testing existing methods under a novel search scenario: semantic exploratory search. The resulting resource consists of 9,296 novel queries, 610,553 query-table relevance annotations, and 238,038 entity-linked tables from the 2013 snapshot. Similarly, on the 2019 snapshot, the resource consists of 2,560 queries, 958,214 relevance annotations, and 457,714 total tables. This makes our resource the largest annotated table-search corpus to date (97 times more queries and 956 times more annotated tables than any existing benchmark). We perform a user study among domain experts and prove that these annotators agree with the automatically generated relevance annotations. As a result, we can re-evaluate some basic assumptions behind existing table search approaches identifying their shortcomings along with promising novel research directions.
Behavior Pattern Mining-based Multi-Behavior Recommendation
Authors: Haojie Li, Zhiyong Cheng, Xu Yu, Jinhuan Liu, Guanfeng Liu, Junwei Du
Venue: SIGIR
Year: 2024

Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary behaviors (such as page views and favorites) to address the limitations of traditional models that depend solely on sparse target behaviors like purchases. Existing approaches to multi-behavior recommendations typically follow one of two strategies: some derive initial node representations from individual behavior subgraphs before integrating them for a comprehensive profile, while others interpret multi-behavior data as a heterogeneous graph, applying graph neural networks to achieve a unified node representation. However, these methods do not adequately explore the intricate patterns of behavior among users and items. To bridge this gap, we introduce a novel algorithm called Behavior Pattern mining-based Multi-behavior Recommendation (BPMR). Our method extensively investigates the diverse interaction patterns between users and items, utilizing these patterns as features for making recommendations. We employ a Bayesian approach to streamline the recommendation process, effectively circumventing the challenges posed by graph neural network algorithms, such as the inability to accurately capture user preferences due to over-smoothing. Our experimental evaluation on three realworld datasets demonstrates that BPMR significantly outperforms existing state-of-the-art algorithms, showing an average improvement of 268.29% in Recall@10 and 248.02% in NDCG@10 metrics. The code of our BPMR is openly accessible for use and further research at https://github.com/rookitkitlee/BPMR.
Mutual Information-based Preference Disentangling and Transferring for Non-overlapped Multi-target Cross-domain Recommendations
Authors: Zhi Li, Daichi Amagata, Yihong Zhang, Takahiro Hara, Shuichiro Haruta, Kei Yonekawa, Mori Kurokawa
Venue: SIGIR
Year: 2024

Building high-quality recommender systems is challenging for new services and small companies, because of their sparse interactions. Cross-domain recommendations (CDRs) alleviate this issue by transferring knowledge from data in external domains. However, most existing CDRs leverage data from only a single external domain and serve only two domains. CDRs serving multiple domains require domain-shared entities (i.e., users and items) to transfer knowledge, which significantly limits their applications due to the hardness and privacy concerns of finding such entities. We therefore focus on a more general scenario, non-overlapped multi-target CDRs (NO-MTCDRs), which require no domain-shared entities and serve multiple domains. Existing methods require domain-shared users to learn user preferences and cannot work on NO-MTCDRs. We hence propose MITrans, a novel mutual information-based (MI-based) preference disentangling and transferring framework to improve recommendations for all domains. MITrans effectively leverages knowledge from multiple domains as well as learning both domain-shared and domain-specific preferences without using domain-shared users. In MITrans, we devise two novel MI constraints to disentangle domain-shared and domain-specific preferences. Moreover, we introduce a module that fuses domain-shared preferences in different domains and combines them with domain-specific preferences to improve recommendations. Our experimental results on two real-world datasets demonstrate the superiority of MITrans in terms of recommendation quality and application range against state-of-the-art overlapped and non-overlapped CDRs.
MKV: Mapping Key Semantics into Vectors for Rumor Detection
Authors: Yang Li, Liguang Liu, Jiacai Guo, Lap-Kei Lee, Fu Lee Wang, Zhenguo Yang
Venue: SIGIR
Year: 2024

The cross-attention mechanism has been widely employed in the multimodal rumor detection task, which is computation-intensive and suffers from the restricted modal receptive field. In this paper, we propose a multimodal rumor detection model (MKV), which maps multimodal key semantics with discrimination into feature vectors for rumor detection. More specifically, MKV extracts high-dimensional features for each modality separately by the Multimodal Feature Extractor (MFE). The mapping mechanism learns low-dimensional mapping scheme (Map) and key semantics (Key) with discrimination from the different modal features respectively. Subsequently, the Map and Key jointly construct a state matrix (State) containing all possible permutations of modalities. In particular, a max pooling operation is performed on State and products a feature vector (Vector). The mapping mechanism is able to incrementally learn the discriminative semantics by stacking manner. Vectors from the stacking process are leveraged in the Rumor Detection module (RD). Extensive experiments on two public datasets show that the MKV achieves the state-of-the-art performance.
Self-Improving Teacher Cultivates Better Student: Distillation Calibration for Multimodal Large Language Models
Authors: Xinwei Li, Li Lin, Shuai Wang, Chen Qian
Venue: SIGIR
Year: 2024

Multimodal content generation, which leverages visual information to enhance the comprehension of cross-modal understanding, plays a critical role in Multimodal Information Retrieval. With the development of large language models (LLMs), recent research has adopted visual instruction tuning to inject the knowledge of LLMs into downstream multimodal tasks. The high complexity and great demand for resources urge researchers to study efficient distillation solutions to transfer the knowledge from pre-trained multimodal models.(teachers) to more compact student models. However, the instruction tuning for knowledge distillation in multimodal LLMs is resource-intensive and capability-restricted. The comprehension of students is highly reliant on the teacher models. To address this issue, we propose a novel Multimodal Distillation Calibration framework (MmDC). The main idea is to generate high-quality training instances that challenge student models to comprehend and prompt the teacher to calibrate the knowledge transferred to students, ultimately cultivating a better student model in downstream tasks. This framework comprises two stages: (1) multimodal alignment and (2) knowledge distillation calibration. In the first stage, parameter-efficient fine-tuning is used to enhance feature alignment between different modalities. In the second stage, we develop a calibration strategy to assess the student model's capability and generate high-quality instances to calibrate knowledge distillation from teacher to student. The experiments on diverse datasets show that our framework efficiently improves the student model's capabilities. Our 7B-size student model, after three iterations of distillation calibration, outperforms the current state-of-the-art LLaVA-13B model on the ScienceQA and LLaVA Test datasets and also exceeds other strong baselines in a zero-shot setting.
Graph Diffusive Self-Supervised Learning for Social Recommendation
Authors: Jiuqiang Li, Hongjun Wang
Venue: SIGIR
Year: 2024

Social recommendation aims at augmenting user-item interaction relationships and boosting recommendation quality by leveraging social information. Recently, self-supervised learning (SSL) has gained widespread adoption for social recommender. However, most existing methods exhibit poor robustness when faced with sparse user behavior data and are susceptible to inevitable social noise. To overcome the aforementioned limitations, we introduce a new Graph Diffusive Self-Supervised Learning (GDSSL) paradigm for social recommendation. Our approach involves the introduction of a guided social graph diffusion model that can adaptively mitigate the impact of social relation noise commonly found in real-world scenarios. This model progressively introduces random noise to the initial social graph and then iteratively restores it to recover the original structure. Additionally, to enhance robustness against noise and sparsity, we propose graph diffusive self-supervised learning, which utilizes the denoised social relation graph generated by our diffusion model for contrastive learning. The extensive experimental outcomes consistently indicate that our proposed GDSSL outmatches existing advanced solutions in social recommendation.
Intent Distribution based Bipartite Graph Representation Learning
Authors: Haojie Li, Wei Wei, Guanfeng Liu, Jinhuan Liu, Feng Jiang, Junwei Du
Venue: SIGIR
Year: 2024

Bipartite graph representation learning embeds users and items into a low-dimensional latent space based on observed interactions. Previous studies mainly fall into two categories: one reconstructs the structural relations of the graph through the representations of nodes, while the other aggregates neighboring node information using graph neural networks. However, existing methods only explore the local structural information of nodes during the learning process. This makes it difficult to represent the macroscopic structural information and leaves it easily affected by data sparsity and noise. To address this issue, we propose the Intent Distribution based Bipartite graph Representation learning (IDBR) model, which explicitly integrates node intent distribution information into the representation learning process. Specifically, we obtain node intent distributions through clustering and design an intent distribution based graph convolution neural network to generate node representations. Compared to traditional methods, we expand the scope of node representations, enabling us to obtain more comprehensive representations of global intent. When constructing the intent distributions, we effectively alleviated the issues of data sparsity and noise. Additionally, we enrich the representations of nodes by integrating potential neighboring nodes from both structural and semantic dimensions. Experiments on the link prediction and recommendation tasks illustrate that the proposed approach outperforms existing state-of-the-art methods. The code of IDBR is available at https://github.com/rookitkitlee/IDBR.
Who To Align With: Feedback-Oriented Multi-Modal Alignment in Recommendation Systems
Authors: Yang Li, Qi&apos, ao Zhao, Chen Lin, Jinsong Su, Zhilin Zhang
Venue: SIGIR
Year: 2024

Multi-modal Recommendation Systems (MRSs) utilize diverse modalities, such as image and text, to enrich item representations and enhance recommendation accuracy. Current MRSs overlook the large misalignment between multi-modal content features and ID embeddings. While bidirectional alignment between visual and textual modalities has been extensively studied in large multi-modal models, this study suggests that multi-modal alignment in MRSs should be in a one-way direction. A plug-and-play framework is presented, called FEedback-orienTed mulTi-modal aLignmEnt (FETTLE). FETTLE contains three novel solutions: (1) it automatically determines item-level alignment direction between each pair of modalities based on estimated user feedback; (2) it coordinates the alignment directions among multiple modalities; (3) it implements cluster-level alignment from both user and item perspectives for more stable alignments. Extensive experiments on three real datasets demonstrate that FETTLE significantly improves various backbone models. Conventional collaborative filtering models are improved by 24.79%-62.79%, and recent MRSs are improved by 5.91% - 20.11%.
Optimal Transport Enhanced Cross-City Site Recommendation
Authors: Xinhang Li, Xiangyu Zhao, Zihao Wang, Yang Duan, Yong Zhang, Chunxiao Xing
Venue: SIGIR
Year: 2024

Site recommendation, which aims at predicting the optimal location for brands to open new branches, has demonstrated an important role in assisting decision-making in modern business. In contrast to traditional recommender systems that can benefit from extensive information, site recommendation starkly suffers from extremely limited information and thus leads to unsatisfactory performance. Therefore, existing site recommendation methods primarily focus on several specific name brands and heavily rely on fine-grained human-crafted features to avoid the data sparsity problem. However, such solutions are not able to fulfill the demand for rapid development in modern business. Therefore, we aim to alleviate the data sparsity problem by effectively utilizing data across multiple cities and thereby propose a novel Optimal Transport enhanced Cross-city (OTC) framework for site recommendation. Specifically, OTC leverages optimal transport (OT) on the learned embeddings of brands and regions separately to project the brands and regions from the source city to the target city. Then, the projected embeddings of brands and regions are utilized to obtain the inference recommendation in the target city. By integrating the original recommendation and the inference recommendations from multiple cities, OTC is able to achieve enhanced recommendation results. The experimental results on the real-world OpenSiteRec dataset, encompassing thousands of brands and regions across four metropolises, demonstrate the effectiveness of our proposed OTC in further improving the performance of site recommendation models.
DeCoCDR: Deployable Cloud-Device Collaboration for Cross-Domain Recommendation
Authors: Yu Li, Yi Zhang, Zimu Zhou, Qiang Li
Venue: SIGIR
Year: 2024

Cross-domain recommendation (CDR) is a widely used methodology in recommender systems to combat data sparsity. It leverages user data across different domains or platforms for providing personalized recommendations. Traditional CDR assumes user preferences and behavior data can be shared freely among cloud and users, which is now impractical due to strict restrictions of data privacy. In this paper, we propose a Deployment-friendly Cloud-Device Collaboration framework for Cross-Domain Recommendation (DeCoCDR). It splits CDR into a two-stage recommendation model through cloud-device collaborations, i.e., item-recall on cloud and item re-ranking on device. This design enables effective CDR while preserving data privacy for both the cloud and the device. Extensive offline and online experiments are conducted to validate the effectiveness of DeCoCDR. In offline experiments, DeCoCDR outperformed the state-of-the-arts in three large datasets. While in real-world deployment, DeCoCDR improved the conversion rate by 45.3% compared with the baseline.
Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction
Authors: You Li, Xupeng Zeng, Yixiao Zeng, Yuming Lin
Venue: SIGIR
Year: 2024

Aspect sentiment triplet extraction (ASTE) is an emerging sentiment analysis task that aims to extract sentiment triplets from review sentences. Each sentiment triplet consists of an aspect, corresponding opinion, and sentiment. Although extensive research has been conducted on the ASTE task, existing methods use the span representations to predict the relationship between spans, failing to consider the interrelation between span pairs. On the other hand, early fusion of entity information is critical for sentiment classification. In this paper, we propose an Enhanced Packed Marker with Entity Information (EPMEI) framework for ASTE task to address the above limitations of the existing works. Specifically, EPMEI consists of entity recognition and sentiment classification models. The entity information is obtained from the entity recognition model first. After that, we insert solid markers with entity information at the input layer of the sentiment classification model to highlight the subject span and improve subject span representation. Furthermore, we introduce a subject-oriented packing strategy, which packs each subject span and all its levitated markers of object spans to model the interrelation between the same-subject span pairs. Extensive experimental results on four ASTE benchmark datasets demonstrate that EPMEI achieves the state-of-the-art baseline. Our code can be found in https://github.com/MKMaS-GUET/EPMEI.
Bootstrap Deep Metric for Seed Expansion in Attributed Networks
Authors: Chunquan Liang, Yifan Wang, Qiankun Chen, Xinyuan Feng, Luyue Wang, Mei Li, Hongming Zhang
Venue: SIGIR
Year: 2024

Seed expansion tasks play an important role in various network applications such as recommendation systems, social network analysis, and bioinformatics. Given a network and a small group of examples as seeds, these tasks involve identifying additional members of interest from the same community. While most existing expansion methods focus on defining a fixed metric function based on the network structure alone, they often overlook the rich content associated with nodes in attributed networks. In this paper, we bridge the gap by learning a deep metric that takes into account both the network structure and node attributes, and by utilizing the recent advanced graph neural networks as encoding functions. The key challenge lies in the extreme scarcity of given positive examples (i.e., the seed nodes) in real-world applications and the absence of negatives (i.e., non-members of the target community). We introduce Bootstrap Deep Metric (BDM), a graph deep metric learning framework for seed expansion problems. BDM utilizes previous versions of representations to generate anchors for positive and unlabeled nodes, and learns enhanced node representations by minimizing the metric losses on both positive and unlabeled nodes. It eliminates the need for negative nodes, while producing closely aligned representations for members of target community and uniformly distributed representations for non-members, which effectively aid in selecting expansion nodes. Experimental results on real-life datasets show that our BDM not only substantially outperforms state-of-the-art approaches but also remarkably surpasses fully labeled classification models in most cases. Codes are available at https://github.com/wangyfnwsuaf/bdm.