OpenRaiser/Intern-Atlas huggingface dataset:
View Synthesis and 3D Reconstruction of Piecewise Planar Scenes Using Intersection Lines between the Planes
Authors: Björn Johansson 0006
Venue: Conference ICCV 1999
Year: 1999

A linear algorithm for synthesizing new views of piecewise planar objects without making an explicit 3D reconstruction is proposed, together with a simple algorithm for 3D reconstruction of the scene. It is shown how this can be done using only one image and information about the projection of the intersection lines between the object planes. These could either be estimated manually or more automatically using at least one more image. No calibration information is needed. A main idea in the paper is to work with textured planes. A patch in one image, corresponding to a planar surface in the scene, is transformed to a patch in another image by a homography. The generalized eigenvectors and eigenvalues of the homographies have geometrical interpretations that are used in the algorithms. To generate a new image, we calculate the homographies for each plane to the new image. The textures can then easily be mapped to the new image. The reconstruction is given as the equation of each plane in the corresponding homography to a certain image.
Kernel information embeddings
Authors: Roland Memisevic
Venue: Conference ICML 2006
Year: 2006

We describe a family of embedding algorithms that are based on nonparametric estimates of mutual information (MI). Using Parzen window estimates of the distribution in the joint (input, embedding)-space, we derive a MI-based objective function for dimensionality reduction that can be optimized directly with respect to a set of latent data representatives. Various types of supervision signal can be introduced within the framework by replacing plain MI with several forms of conditional MI. Examples of the semi-(un)supervised algorithms that we obtain this way are a new model for manifold alignment, and a new type of embedding method that performs 'conditional dimensionality reduction'.
Parameter Learning with Truncated Message-Passing
Authors: Justin Domke
Venue: Conference CVPR 2011
Year: 2011

Training of conditional random fields often takes the form of a double-loop procedure with message-passing inference in the inner loop. This can be very expensive, as the need to solve the inner loop to high accuracy can require many message-passing iterations. This paper seeks to reduce the expense of such training, by redefining the training objective in terms of the approximate marginals obtained after message-passing is “truncated” to a fixed number of iterations. An algorithm is derived to efficiently compute the exact gradient of this objective. On a common pixel labeling benchmark, this procedure improves training speeds by an order of magnitude, and slightly improves inference accuracy if a very small number of message-passing iterations are used at test time.
Point prompt tuning for temporally language grounding
Authors: Yawen Zeng
Venue: Conference SIGIR 2022
Year: 2022

The task of temporally language grounding (TLG) aims to locate a video moment from an untrimmed video that match a given textual query, which has attracted considerable research attention. In recent years, typical retrieval-based TLG methods are inefficient due to pre-segmented candidate moments, while localization-based TLG solutions adopt reinforcement learning resulting in unstable convergence. Therefore, how to perform TLG task efficiently and stably is a non-trivial work. Toward this end, we innovatively contribute a solution, Point Prompt Tuning (PPT), which formulates this task as a prompt-based multi-modal problem and integrates multiple sub-tasks to tuning performance. Specifically, a flexible prompt strategy is contributed to rewrite the query firstly, which contains both query, start point and end point. Thereafter, a multi-modal Transformer is adopted to fully learn the multi-modal context. Meanwhile, we design various sub-tasks to constrain the novel framework, namely matching task and localization task. Finally, the start and end points of matched video moment are straightforward predicted, simply yet stably. Extensive experiments on two real-world datasets have well verified the effectiveness of our proposed solution.
DBRec: Dual-bridging recommendation via discovering latent groups
Authors: Jingwei Ma, Jiahui Wen, Mingyang Zhong, Liangchen Liu, Chaojie Li, Weitong Chen, Yin Yang, Hongkui Tu, Xue Li
Venue: Conference CIKM 2019
Year: 2019

In recommender systems, the user-item interaction data is usually sparse and not sufficient for learning comprehensive user/item representations for recommendation. To address this problem, we propose a novel dual-bridging recommendation model (DBRec). DBRec performs latent user/item group discovery simultaneously with collaborative filtering, and interacts group information with users/items for bridging similar users/items. Therefore, a user's preference over an unobserved item, in DBRec, can be bridged by the users within the same group who have rated the item, or the user-rated items that share the same group with the unobserved item. In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations. We jointly integrate collaborative filtering, latent group discovering and hierarchical modelling into a unified framework, so that all the model parameters can be learned toward the optimization of the objective function. We validate the effectiveness of the proposed model with two real datasets, and demonstrate its advantage over the state-of-the-art recommendation models with extensive experiments.
News Recommendation with Topic-Enriched Knowledge Graphs
Authors: Dongho Lee, Byungkook Oh, Seungmin Seo, Kyong-Ho Lee
Venue: Conference CIKM 2020
Year: 2020

News recommendation systems? purpose is to tackle the immense amount of news and offer personalized recommendations to users. A major issue in news recommendation is to capture the precise news representations for the efficacy of recommended items. Commonly, news contents are filled with well-known entities of different types. However, existing recommendation systems overlook exploiting external knowledge about entities and topical relatedness among the news. To cope with the above problem, in this paper, we propose Topic-Enriched Knowledge Graph Recommendation System(TEKGR). Three encoders in TEKGR handle news titles in two perspectives to obtain news representation embedding: (1) to extract meaning of news words without considering latent knowledge features in the news and (2) to extract semantic knowledge of news through topic information and contextual information from a knowledge graph. After obtaining news representation vectors, an attention network compares clicked news to the candidate news in order to get the user's final embedding. Our TEKGR model is superior to existing news recommendation methods by manipulating topical relations among entities and contextual features of entities. Experimental results on two public datasets show that our approach outperforms state-of-the-art deep recommendation approaches.
E-commerce recommendation with weighted expected utility
Authors: Zhichao Xu, Yi Han, Yongfeng Zhang, Qingyao Ai
Venue: Conference CIKM 2020
Year: 2020

Different from shopping at retail stores, consumers on e-commerce platforms usually cannot touch or try products before purchasing, which means that they have to make decisions when they are uncertain about the outcome (e.g., satisfaction level) of purchasing a product. To study people's preferences with regard to choices that have uncertain outcomes, economics researchers have proposed the hypothesis of Expected Utility (EU) that models the subject value associated with an individual's choice as the statistical expectations of that individual's valuations of the outcomes of this choice. Despite its success in studies of game theory and decision theory, the effectiveness of EU, however, is mostly unknown in e-commerce recommendation systems. Previous research on e-commerce recommendation interprets the utility of purchase decisions either as a function of the consumed quantity of the product or as the gain of sellers/buyers in the monetary sense. As most consumers just purchase one unit of a product at a time and most alternatives have similar prices, such modeling of purchase utility is likely to be inaccurate in practice. In this paper, we interpret purchase utility as the satisfaction level a consumer gets from a product and propose a recommendation framework using EU to model consumers' behavioral patterns. We assume that consumer estimates the expected utilities of all the alternatives and choose products with maximum expected utility for each purchase. To deal with the potential psychological biases of each consumer, we introduce the usage of Probability Weight Function (PWF) and design our algorithm based on Weighted Expected Utility (WEU). Empirical study on real-world e-commerce datasets shows that our proposed ranking-based recommendation framework achieves statistically significant improvement against both classical Collaborative Filtering/Latent Factor Models and state-of-the-art deep models in top-K recommendation.
AKUPM: Attention-enhanced knowledge-aware user preference model for recommendation
Authors: Xiaoli Tang, Tengyun Wang, Haizhi Yang, Hengjie Song
Venue: Conference KDD 2019
Year: 2019

Recently, much attention has been paid to the usage of knowledge graph within the context of recommender systems to alleviate the data sparsity and cold-start problems. However, when incorporating entities from a knowledge graph to represent users, most existing works are unaware of the relationships between these entities and users. As a result, the recommendation results may suffer a lot from some unrelated entities. In this paper, we investigate how to explore these relationships which are essentially determined by the interactions among entities. Firstly, we categorize the interactions among entities into two types: inter-entity-interaction and intra-entity-interaction. Inter-entity-interaction is the interactions among entities that affect their importances to represent users. And intra-entity-interaction is the interactions within an entity that describe the different characteristics of this entity when involved in different relations. Then, considering these two types of interactions, we propose a novel model named Attention-enhanced Knowledge-aware User Preference Model (AKUPM) for click-through rate (CTR) prediction. More specifically, a self-attention network is utilized to capture the inter-entity-interaction by learning appropriate importance of each entity w.r.t the user. Moreover, the intra-entity-interaction is modeled by projecting each entity into its connected relation spaces to obtain the suitable characteristics. By doing so, AKUPM is able to figure out the most related part of incorporated entities (i.e., filter out the unrelated entities). Extensive experiments on two real-world public datasets demonstrate that AKUPM achieves substantial gains in terms of common evaluation metrics (e.g., AUC, ACC and Recall@top-K) over several state-of-the-art baselines.
Streaming ranking based recommender systems
Authors: Weiqing Wang, Hongzhi Yin, Zi Huang, Qinyong Wang, Xingzhong Du, Quoc Viet Hung Nguyen
Venue: Conference SIGIR 2018
Year: 2018

Studying recommender systems under streaming scenarios has become increasingly important because real-world applications produce data continuously and rapidly. However, most existing recommender systems today are designed in the context of an offline setting. Compared with the traditional recommender systems, large-volume and high-velocity are posing severe challenges for streaming recommender systems. In this paper, we investigate the problem of streaming recommendations being subject to higher input rates than they can immediately process with their available system resources (i.e., CPU and memory). In particular, we provide a principled framework called as SPMF (Stream-centered Probabilistic Matrix Factorization model), based on BPR (Bayesian Personalized Ranking) optimization framework, for performing efficient ranking based recommendations in stream settings. Experiments on three real-world datasets illustrate the superiority of SPMF in online recommendations.
Bayesian personalized feature interaction selection for factorization machines
Authors: Yifan Chen, Pengjie Ren, Yang Wang, Maarten de Rijke
Venue: Conference SIGIR 2019
Year: 2019

Factorization Machines (FMs) are widely used for feature-based collaborative filtering tasks, as they are very effective at modeling feature interactions. Existing FM-based methods usually take all feature interactions into account, which is unreasonable because not all feature interactions are helpful: incorporating useless feature interactions will introduce noise and degrade the recommendation performance. Recently, methods that perform Feature Interaction Selection (FIS) have attracted attention because of their effectiveness at filtering out useless feature interactions. However, they assume that all users share the same feature interactions, which is not necessarily true, especially for collaborative filtering tasks. In this work, we address this issue and study Personalized Feature Interaction Selection (P-FIS) by proposing a Bayesian Personalized Feature Interaction Selection (BP-FIS) mechanism under the Bayesian Variable Selection (BVS) theory. Specifically, we first introduce interaction selection variables with hereditary spike and slab priors for P-FIS. Then, we form a Bayesian generative model and derive the Evidence Lower Bound (ELBO), which can be optimized by an efficient Stochastic Gradient Variational Bayes (SGVB) method to learn the parameters. Finally, because BP-FIS can be seamlessly integrated with different variants of FMs, we implement two FM variants under the proposed BP-FIS. We carry out experiments on three benchmark datasets. The empirical results demonstrate the effectiveness of BP-FIS for selecting personalized interactions and improving the recommendation performance.
MVIN: Learning Multiview Items for Recommendation
Authors: Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu, Lun-Wei Ku
Venue: Conference SIGIR 2020
Year: 2020

Researchers have begun to utilize heterogeneous knowledge graphs (KGs) as auxiliary information in recommendation systems to mitigate the cold start and sparsity issues. However, utilizing a graph neural network (GNN) to capture information in KG and further apply in RS is still problematic as it is unable to see each item's properties from multiple perspectives. To address these issues, we propose the multi-view item network (MVIN), a GNN-based recommendation model which provides superior recommendations by describing items from a unique mixed view from user and entity angles. MVIN learns item representations from both the user view and the entity view. From the user view, user-oriented modules score and aggregate features to make recommendations from a personalized perspective constructed according to KG entities which incorporates user click information. From the entity view, the mixing layer contrasts layer-wise GCN information to further obtain comprehensive features from internal entity-entity interactions in the KG. We evaluate MVIN on three real-world datasets: MovieLens-1M (ML-1M), LFM-1b 2015 (LFM-1b), and Amazon-Book (AZ-book). Results show that MVIN significantly outperforms state-of-the-art methods on these three datasets. In addition, from user-view cases, we find that MVIN indeed captures entities that attract users. Figures further illustrate that mixing layers in a heterogeneous KG plays a vital role in neighborhood information aggregation.
Spiral of silence in recommender systems
Authors: Dugang Liu, Chen Lin, Zhilin Zhang, Yanghua Xiao, Hanghang Tong
Venue: Conference WSDM 2019
Year: 2019

It has been established that, ratings are missing not at random in recommender systems. However, little research has been done to reveal how the ratings are missing. In this paper we present one possible explanation of the missing not at random phenomenon. We verify that, using a variety of different real-life datasets, there is a spiral process for a silent minority in recommender systems where (1) people whose opinions fall into the minority are less likely to give ratings than majority opinion holders; (2) as the majority opinion becomes more dominant, the rating possibility of a majority opinion holder is intensifying but the rating possibility of a minority opinion holder is shrinking; (3) only hardcore users remain to rate for minority opinions when the spiral achieves its steady state. Our empirical findings are beneficial for future recommendation models. To demonstrate the impact of our empirical findings, we present a probabilistic model that mimics the generation process of spiral of silence. We experimentally show that, the presented model offers more accurate recommendations, compared with state-of-the-art recommendation models.
Collaborative metric learning
Authors: Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge J. Belongie, Deborah Estrin
Venue: Conference WWW 2017
Year: 2017

Metric learning algorithms produce distance metrics that capture the important relationships among data. In this work, we study the connection between metric learning and collaborative filtering. We propose Collaborative Metric Learning (CML) which learns a joint metric space to encode not only users' preferences but also the user-user and item-item similarity. The proposed algorithm outperforms state-of-the-art collaborative filtering algorithms on a wide range of recommendation tasks and uncovers the underlying spectrum of users' fine-grained preferences. CML also achieves significant speedup for Top-K recommendation tasks using off-the-shelf, approximate nearest-neighbor search, with negligible accuracy reduction.
Disentangled ontology embedding for zero-shot learning
Authors: Yuxia Geng, Jiaoyan Chen 0001, Wen Zhang 0015, Yajing Xu, Zhuo Chen 0007, Jeff Z. Pan, Yufeng Huang, Feiyu Xiong, Huajun Chen
Venue: Conference KDD 2022
Year: 2022

Knowledge Graph (KG) and its variant of ontology have been widely used for\nknowledge representation, and have shown to be quite effective in augmenting\nZero-shot Learning (ZSL). However, existing ZSL methods that utilize KGs all\nneglect the intrinsic complexity of inter-class relationships represented in\nKGs. One typical feature is that a class is often related to other classes in\ndifferent semantic aspects. In this paper, we focus on ontologies for\naugmenting ZSL, and propose to learn disentangled ontology embeddings guided by\nontology properties to capture and utilize more fine-grained class\nrelationships in different aspects. We also contribute a new ZSL framework\nnamed DOZSL, which contains two new ZSL solutions based on generative models\nand graph propagation models, respectively, for effectively utilizing the\ndisentangled ontology embeddings. Extensive evaluations have been conducted on\nfive benchmarks across zero-shot image classification (ZS-IMGC) and zero-shot\nKG completion (ZS-KGC). DOZSL often achieves better performance than the\nstate-of-the-art, and its components have been verified by ablation studies and\ncase studies. Our codes and datasets are available at\nhttps://github.com/zjukg/DOZSL.\n
Uncertainty-guided continual learning with Bayesian neural networks
Authors: Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach
Venue: Conference ICLR 2020
Year: 2020

Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation to measure the parameters' \textit{importance}. In contrast, we propose Uncertainty-guided Continual Bayesian Neural Networks (UCB), where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in networks. Uncertainty is a natural way to identify \textit{what to remember} and \textit{what to change} as we continually learn, and thus mitigate catastrophic forgetting. We also show a variant of our model, which uses uncertainty for weight pruning and retains task performance after pruning by saving binary masks per tasks. We evaluate our UCB approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches. Additionally, we show that our model does not necessarily need task information at test time, i.e. it does not presume knowledge of which task a sample belongs to.
Improving out-of-vocabulary hashing in recommendation systems
Authors: William Shiao, Mingxuan Ju, Zhichun Guo, Xin Chen, Evangelos E. Papalexakis, Tong Zhao, Neil Shah, Yozen Liu
Venue: Conference WWW 2025
Year: 2025

Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions of users. In real applications, one common challenge is recommending new users and items unseen (out-of-vocabulary, or OOV) at training time, i.e. the inductive setting. Additionally, modern RS also faces challenges in ID embedding table size. To handle large cardinality user/item embeddings, memory intensive embedding tables are required. The size of OOV user/item IDs are often large and varies. As a result of both issues, existing solutions applied in practice are often naïve, such as assigning OOV or hashed users/items to a fix set of random buckets. In this work, we tackle the cold-start OOV and ID embedding memory problem and propose approaches that better leverage available user/item features and memory-efficient hashing at the embedding table level. We discuss plug-and-play approaches that are easily applicable to RS models and improve inductive performance without negatively impacting transductive performance. Through our extensive evaluation, we find that proposed methods that exploit feature similarity using LSH consistently outperform alternatives on a majority of model-dataset combinations, with the best one showing a mean improvement of 3.74% over the industry standard baseline in recommendation performance. We release our code and hope our work helps practitioners make more informed decisions for efficiently hashing OOV in their RS and further inspires academic research into improving OOV support in RS.
Dimensionality reduction and generalization
Authors: Sofia Mosci, Lorenzo Rosasco, Alessandro Verri
Venue: Conference ICML 2007
Year: 2007

In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning problems. We show that performing KPCA and then ordinary least squares on the projected data, a procedure known as kernel principal component regression (KPCR), is equivalent to spectral cut-off regularization, the regularization parameter being exactly the number of principal components to keep. Using probabilistic estimates for integral operators we can prove error estimates for KPCR and propose a parameter choice procedure allowing to prove consistency of the algorithm.