OpenRaiser/Intern-Atlas huggingface dataset:
Itmprec: Intention-based targeted multi-round proactive recommendation
Authors: Yahong Lian, Chunyao Song, Tingjian Ge
Venue: Conference WWW 2025
Year: 2025

Personalized recommendations are integrated into daily life, but providers may want certain items to become more appealing over time through user interactions, yet this issue is often overlooked. The existing works are often based on the assumption that users will passively accept all intermediate sequences or not explore intention modeling in the targeted nudging process. Both of these factors result in suboptimal performance in the proactive recommendation. In this paper, we propose a novel intention-based targeted multi-round proactive recommendation method, dubbed ITMPRec. We first select target items using a pre-match strategy. Then, we employ a multi-round nudging recommendation method, incorporating a module to quantify users' intention-level evolution, helping choose suitable intermediate items. Additionally, we model users' sensitivity to changes caused by these items. Lastly, we propose an LLM agent as a pluggable component to simulate user feedback, offering an alternative to traditional click models by leveraging the agent's external knowledge and reasoning capabilities. Through extensive experiments on four public datasets, we demonstrate the superiority of ITMPRec compared to eight baseline models.
Leveraging llms for influence path planning in proactive recommendation
Authors: Mingze Wang, Shuxian Bi, Wenjie Wang, Chongming Gao, Yangyang Li, Fuli Feng
Venue: Conference WWW 2025
Year: 2025

Recommender systems are pivotal in Internet social platforms, yet they often cater to users' historical interests, leading to critical issues like echo chambers. To broaden user horizons, proactive recommender systems aim to guide user interest to gradually like a target item beyond historical interests through an influence path,i.e., a sequence of recommended items. As a representative, Influential Recommender System (IRS) designs a sequential model for influence path planning but faces issues of lacking target item inclusion and path coherence. To address the issues, we leverage the advanced planning capabilities of Large Language Models (LLMs) and propose an LLM-based Influence Path Planning (LLM-IPP) method. LLM-IPP generates coherent and effective influence paths by capturing user interest shifts and item characteristics. We introduce novel evaluation metrics and user simulators to benchmark LLM-IPP against traditional methods. Our experiments demonstrate that LLM-IPP significantly enhances user acceptability and path coherence, outperforming existing approaches.
English prompts are better for NLI-based zero-shot emotion classification than target-language prompts
Authors: Patrick Bareiß, Roman Klinger, Jeremy Barnes
Venue: Conference WWW 2024
Year: 2024

Emotion classification in text is a challenging task due to the processes involved when interpreting a textual description of a potential emotion stimulus. In addition, the set of emotion categories is highly domain-specific. For instance, literature analysis might require the use of aesthetic emotions (e.g., finding something beautiful), and social media analysis could benefit from fine-grained sets (e.g., separating anger from annoyance) than only those that represent basic categories as they have been proposed by Paul Ekman (anger, disgust, fear, joy, surprise, sadness). This renders the task an interesting field for zero-shot classifications, in which the label set is not known at model development time. Unfortunately, most resources for emotion analysis are English, and therefore, most studies on emotion analysis have been performed in English, including those that involve prompting language models for text labels. This leaves us with a research gap that we address in this paper: In which language should we prompt for emotion labels on non-English texts? This is particularly of interest when we have access to a multilingual large language model, because we could request labels with English prompts even for non-English data. Our experiments with natural language inference-based language models show that it is consistently better to use English prompts even if the data is in a different language.
KGPR: Knowledge graph enhanced passage ranking
Authors: Jinyuan Fang, Zaiqiao Meng, Craig Macdonald
Venue: Conference CIKM 2023
Year: 2023

Passage ranking aims to rank a set of passages based on their relevance to a query. Current state-of-the-art models for this task typically employ a cross-encoder structure. However, these models lack access to background knowledge, i.e., information related to the query that can be helpful in retrieving relevant passages. Knowledge Graphs (KGs) provide a structured way of storing information about entities and their relationships, offering valuable background knowledge about entities. While KGs have been used to augment pretrained language models (LMs) to perform several reasoning tasks such as question answering, it remains an open question of how to utilise the information from KGs to enhance the performance of cross-encoders on the passage ranking task. Therefore, we propose KGPR, a KG-enhanced cross-encoder for the Passage Retrieval task. KGPR is built upon LUKE, an entity-aware pretrained LM, with an additional module that fuses information from KGs into LUKE. By leveraging the background knowledge from KGs, KGPR enhances the model's comprehension of queries and passages, resulting in improved ranking performance. Experimental results demonstrate that using KGs can enhance the performance of LUKE in the passage retrieval task, and KGPR can outperform state-of-the-art monoT5 cross-encoder by 3.32% and 10.77% on the MS MARCO development set and TREC DL-HARD query set respectively, using a model with a similar number of parameters.
Intractability and clustering with constraints
Authors: Ian Davidson, S. S. Ravi
Venue: Conference ICML 2007
Year: 2007

Clustering with constraints is a developing area of machine learning. Various papers have used constraints to enforce particular clusterings, seed clustering algorithms and even learn distance functions which are then used for clustering. We present intractability results for some constraint combinations and illustrate both formally and experimentally the implications of these results for using constraints with clustering.
Learning nonparametric kernel matrices from pairwise constraints
Authors: Steven C. H. Hoi, Rong Jin 0001, Michael R. Lyu
Venue: Conference ICML 2007
Year: 2007

Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Some kernel learning methods assume the target kernel matrix to be a linear combination of parametric kernel matrices. This assumption again importantly limits the flexibility of the target kernel matrices. The key challenge with nonparametric kernel learning arises from the difficulty in linking the nonparametric kernels to the input patterns. In this paper, we resolve this problem by introducing the graph Laplacian of the observed data as a regularizer when optimizing the kernel matrix with respect to the pairwise constraints. We formulate the problem into Semi-Definite Programs (SDP), and propose an efficient algorithm to solve the SDP problem. The extensive evaluation on clustering with pairwise constraints shows that the proposed nonparametric kernel learning method is more effective than other state-of-the-art kernel learning techniques.
Dual-geometric space embedding model for two-view knowledge graphs
Authors: Roshni G. Iyer, Yunsheng Bai, Wei Wang 0010, Yizhou Sun
Venue: Conference KDD 2022
Year: 2022

Two-view knowledge graphs (KGs) jointly represent two components: an ontology view for abstract and commonsense concepts, and an instance view for specific entities that are instantiated from ontological concepts. As such, these KGs contain heterogeneous structures that are hierarchical, from the ontology-view, and cyclical, from the instance-view. Despite these various structures in KGs, recent works on embedding KGs assume that the entire KG belongs to only one of the two views but not both simultaneously. For works that seek to put both views of the KG together, the instance and ontology views are assumed to belong to the same geometric space, such as all nodes embedded in the same Euclidean space or non-Euclidean product space, an assumption no longer reasonable for two-view KGs where different portions of the graph exhibit different structures. To address this issue, we define and construct a dual-geometric space embedding model (DGS) that models two-view KGs using a complex non-Euclidean geometric space, by embedding different portions of the KG in different geometric spaces. DGS utilizes the spherical space, hyperbolic space, and their intersecting space in a unified framework for learning embeddings. Furthermore, for the spherical space, we propose novel closed spherical space operators that directly decompose to using properties of the spherical space without the need for mapping to an approximate tangent space. Experiments on public datasets show that DGS significantly outperforms previous state-of-the-art baseline models on KG completion tasks, demonstrating its ability to better model heterogeneous structures in KGs.
Emometa: A multimodal dataset for fine-grained emotion classification in chinese metaphors
Authors: Xingyuan Lu, Yuxi Liu, Dongyu Zhang, Zhiyao Wu, Jing Ren, Feng Xia
Venue: Conference WWW 2025
Year: 2025

Metaphors play a pivotal role in expressing emotions, making them crucial for emotional intelligence. The advent of multimodal data and widespread communication has led to a proliferation of multimodal metaphors, amplifying the complexity of emotion classification compared to single-mode scenarios. However, the scarcity of research on constructing multimodal metaphorical fine-grained emotion datasets hampers progress in this domain. Moreover, existing studies predominantly focus on English, overlooking potential variations in emotional nuances across languages. To address these gaps, we introduce a multimodal dataset in Chinese comprising 5,000 text-image pairs of metaphorical advertisements. Each entry is meticulously annotated for metaphor occurrence, domain relations and fine-grained emotion classification encompassing joy, love, trust, fear, sadness, disgust, anger, surprise, anticipation, and neutral. Our dataset is publicly accessible (https://github.com/DUTIR-YSQ/EmoMeta), facilitating further advancements in this burgeoning field.
Popularity-aware Distributionally Robust Optimization for Recommendation System
Authors: Jujia Zhao, Wenjie Wang, Xinyu Lin, Leigang Qu, Jizhi Zhang, Tat-Seng Chua
Venue: Conference CIKM 2023
Year: 2023

Collaborative Filtering (CF) has been widely applied for personalized recommendations in various industrial applications. However, due to the training strategy of Empirical Risk Minimization, CF models tend to favor popular items, resulting in inferior performance on sparse users and items. To enhance the CF representation learning of sparse users and items without sacrificing the performance of popular items, we propose a novel Popularity- aware Distributionally Robust Optimization (PDRO) framework. In particular, PDRO emphasizes the optimization of sparse users/items, while incorporating item popularity to preserve the performance of popular items through two modules. First, an implicit module develops a new popularity-aware DRO objective, paying more attention to items that will potentially become popular over time. Second, an explicit module that directly predicts the popularity of items to help the estimation of user-item matching scores. We apply PDRO to a micro-video recommendation scenario and implement it on two representative backend models. Extensive experiments on a real-world industrial dataset, as well as two public benchmark datasets, validate the efficacy of our proposed PDRO. Additionally, we perform an offline A/B test on the industrial dataset, further demonstrating the superiority of PDRO in real-world application scenarios.
Retrieval Consistency in the Presence of Query Variations
Authors: Peter Bailey, Alistair Moffat, Falk Scholer, Paul Thomas
Venue: Conference SIGIR 2017
Year: 2017

A search engine that can return the ideal results for a person's information need, independent of the specific query that is used to express that need, would be preferable to one that is overly swayed by the individual terms used; search engines should be consistent in the presence of syntactic query variations responding to the same information need. In this paper we examine the retrieval consistency of a set of five systems responding to syntactic query variations over one hundred topics, working with the UQV100 test collection, and using Rank-Biased Overlap (RBO) relative to a centroid ranking over the query variations per topic as a measure of consistency. We also introduce a new data fusion algorithm, Rank-Biased Centroid (RBC), for constructing a centroid ranking over a set of rankings from query variations for a topic. RBC is compared with alternative data fusion algorithms.
Contributionsum: Generating disentangled contributions for scientific papers
Authors: Meng-Huan Liu, An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: Conference CIKM 2023
Year: 2023

Contributions are essentially the core of every scientific research, highlighting their key values to the academic community. Systems that are capable of identifying the contributions from scientific papers precisely and organizing them into well-structured summaries can facilitate both text processing and human comprehension. In this paper, we present ContributionSum, a dataset consisting of 24K computer science papers with contributions explicitly listed by the authors, which are further classified into different contribution types based on a newly-proposed annotation scheme. In addition, we study the task of generating disentangled contributions that summarize the values of scientific papers into key points. We propose a fine-grained post-training strategy tailored to our task and leverage salient information of different contribution types in the papers. To assess the coherency and coverage of each contribution aspect, we perform summary-level and contribution-level evaluations for our task. Experimental results show that our method improves upon mainstream baselines.
Ad-hoc object retrieval in the web of data
Authors: Jeffrey Pound, Peter Mika, Hugo Zaragoza
Venue: Conference WWW 2010
Year: 2010

Semantic Search refers to a loose set of concepts, challenges and techniques having to do with harnessing the information of the growing Web of Data (WoD) for Web search. Here we propose a formal model of one specific semantic search task: ad-hoc object retrieval. We show that this task provides a solid framework to study some of the semantic search problems currently tackled by commercial Web search engines. We connect this task to the traditional ad-hoc document retrieval and discuss appropriate evaluation metrics. Finally, we carry out a realistic evaluation of this task in the context of a Web search application.
Wikipedia Entity Expansion and Attribute Extraction from the Web Using Semi-supervised Learning
Authors: Lidong Bing, Wai Lam, Tak-Lam Wong
Venue: Conference WSDM 2013
Year: 2013

We develop a new framework to achieve the goal of Wikipedia entity expansion and attribute extraction from the Web. Our framework takes a few existing entities that are automatically collected from a particular Wikipedia category as seed input and explores their attribute infoboxes to obtain clues for the discovery of more entities for this category and the attribute content of the newly discovered entities. One characteristic of our framework is to conduct discovery and extraction from desirable semi-structured data record sets which are automatically collected from the Web. A semi-supervised learning model with Conditional Random Fields is developed to deal with the issues of extraction learning and limited number of labeled examples derived from the seed entities. We make use of a proximate record graph to guide the semi-supervised learning process. The graph captures alignment similarity among data records. Then the semi-supervised learning process can leverage the unlabeled data in the record set by controlling the label regularization under the guidance of the proximate record graph. Extensive experiments on different domains have been conducted to demonstrate its superiority for discovering new entities and extracting attribute content.
Nordlys: A Toolkit for Entity-Oriented and Semantic Search
Authors: Faegheh Hasibi, Krisztian Balog, Darío Garigliotti, Shuo Zhang
Venue: Conference SIGIR 2017
Year: 2017

We introduce Nordlys, a toolkit for entity-oriented and semantic search. It provides functionality for entity cataloging, entity retrieval, entity linking, and target type identification. Nordlys may be used as a Python library or as a RESTful API, and also comes with a web-based user interface. The toolkit is open source and is available at http://nordlys.cc.
Spatio-temporal models for estimating click-through rate
Authors: Deepak Agarwal, Bee-Chung Chen, Pradheep Elango
Venue: Conference WWW 2009
Year: 2009

We propose novel spatio-temporal models to estimate click-through rates in the context of content recommendation. We track article CTR at a fixed location over time through a dynamic Gamma-Poisson model and combine information from correlated locations through dynamic linear regressions, significantly improving on per-location model. Our models adjust for user fatigue through an exponential tilt to the first-view CTR (probability of click on first article exposure) that is based only on user-specific repeat-exposure features. We illustrate our approach on data obtained from a module (Today Module) published regularly on Yahoo! Front Page and demonstrate significant improvement over commonly used baseline methods. Large scale simulation experiments to study the performance of our models under different scenarios provide encouraging results. Throughout, all modeling assumptions are validated via rigorous exploratory data analysis.
Adaptive Online Hyper- Parameters Tuning for Ad Event-Prediction Models
Authors: Michal Aharon, Amit Kagian, Oren Somekh
Venue: Conference WWW 2017
Year: 2017

Yahoo's native advertising (also known as Gemini native) is one of its fastest growing businesses, reaching a run-rate of several hundred Millions USD in the past year. Driving the Gemini native models that are used to predict both, click probability (pCTR) and conversion probability (pCONV), is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. OFFSET is a one-pass algorithm that updates its model for every new batch of logged data using a stochastic gradient descent (SGD) based approach. As most learning algorithms, OFFSET includes several hyper-parameters that can be tuned to provide best performance for a given system conditions. Since the marketplace environment is very dynamic and influenced by seasonality and other temporal factors, having a fixed single set of hyper-parameters (or configuration) for the learning algorithm is sub-optimal.
Modeling impression discounting in large-scale recommender systems
Authors: Pei Lee, Laks V. S. Lakshmanan, Mitul Tiwari, Sam Shah
Venue: Conference KDD 2014
Year: 2014

Recommender systems have become very important for many online activities, such as watching movies, shopping for products, and connecting with friends on social networks. User behavioral analysis and user feedback (both explicit and implicit) modeling are crucial for the improvement of any online recommender system. Widely adopted recommender systems at LinkedIn such as "People You May Know" and "Endorsements" are evolving by analyzing user behaviors on impressed recommendation items.
The k-peak Decomposition: Mapping the Global Structure of Graphs
Authors: Priya Govindan, Chenghong Wang, Chumeng Xu, Hongyu Duan, Sucheta Soundarajan
Venue: Conference WWW 2017
Year: 2017

The structure of real-world complex networks has long been an area of interest, and one common way to describe the structure of a network has been with the k-core decomposition. The core number of a node can be thought of as a measure of its centrality and importance, and is used by applications such as community detection, understanding viral spreads, and detecting fraudsters. However, we observe that the k-core decomposition suffers from an important flaw: namely, it is calculated globally, and so if the network contains distinct regions of different densities, the sparser among these regions may be neglected.
Measuring and improving the core resilience of networks
Authors: Ricky Laishram, Ahmet Erdem Sariyüce, Tina Eliassi-Rad, Ali Pinar, Sucheta Soundarajan
Venue: Conference WWW 2018
Year: 2018

The concept of k-cores is important for understanding the global structure of networks, as well as for identifying central or important nodes within a network. It is often valuable to understand the resilience of the k-cores of a network to attacks and dropped edges (i.e., damaged communications links). We provide a formal definition of a network»s core resilience, and examine the problem of characterizing core resilience in terms of the network»s structural features: in particular, which structural properties cause a network to have high or low core resilience? To measure this, we introduce two novel node properties,Core Strength andCore Influence, which measure the resilience of individual nodes» core numbers and their influence on other nodes» core numbers. Using these properties, we propose theMaximize Resilience of k-Core algorithm to add edges to improve the core resilience of a network. We consider two attack scenarios - randomly deleted edges and randomly deleted nodes. Through experiments on a variety of technological and infrastructure network datasets, we verify the efficacy of our node-based resilience measures at predicting the resilience of a network, and evaluate MRKC at the task of improving a network»s core resilience. We find that on average, for edge deletion attacks, MRKC improves the resilience of a network by 11.1% over the original network, as compared to the best baseline method, which improves the resilience of a network by only 2%. For node deletion attacks, MRKC improves the core resilience of the original network by 19.7% on average, while the best baseline improves it by only 3%.
Spread it good, spread it fast: Identification of influential nodes in social networks
Authors: Maria-Evgenia G. Rossi, Fragkiskos D. Malliaros, Michalis Vazirgiannis
Venue: Conference WWW 2015
Year: 2015

Understanding and controlling spreading dynamics in networks presupposes the identification of those influential nodes that will trigger an efficient information diffusion. It has been shown that the best spreaders are the ones located in the core of the network - as produced by the k-core decomposition. In this paper we further refine the set of the most influential nodes, showing that the nodes belonging to the best K-truss subgraph, as identified by the K-truss decomposition of the network, perform even better leading to faster and wider epidemic spreading.