Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Recommendation": models, code, and papers

CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation

Jul 13, 2021
Ruihong Qiu, Sen Wang, Zhi Chen, Hongzhi Yin, Zi Huang

Visually-aware recommendation on E-commerce platforms aims to leverage visual information of items to predict a user's preference. It is commonly observed that user's attention to visual features does not always reflect the real preference. Although a user may click and view an item in light of a visual satisfaction of their expectations, a real purchase does not always occur due to the unsatisfaction of other essential features (e.g., brand, material, price). We refer to the reason for such a visually related interaction deviating from the real preference as a visual bias. Existing visually-aware models make use of the visual features as a separate collaborative signal similarly to other features to directly predict the user's preference without considering a potential bias, which gives rise to a visually biased recommendation. In this paper, we derive a causal graph to identify and analyze the visual bias of these existing methods. In this causal graph, the visual feature of an item acts as a mediator, which could introduce a spurious relationship between the user and the item. To eliminate this spurious relationship that misleads the prediction of the user's real preference, an intervention and a counterfactual inference are developed over the mediator. Particularly, the Total Indirect Effect is applied for a debiased prediction during the testing phase of the model. This causal inference framework is model agnostic such that it can be integrated into the existing methods. Furthermore, we propose a debiased visually-aware recommender system, denoted as CausalRec to effectively retain the supportive significance of the visual information and remove the visual bias. Extensive experiments are conducted on eight benchmark datasets, which shows the state-of-the-art performance of CausalRec and the efficacy of debiasing.

  Access Paper or Ask Questions

AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation

Oct 11, 2021
Nuo Li, Bin Guo, Yan Liu, Lina Yao, Jiaqi Liu, Zhiwen Yu

Questions in Community Question Answering (CQA) sites are recommended to users, mainly based on users' interest extracted from questions that users have answered or have asked. However, there is a general phenomenon that users answer fewer questions while pay more attention to follow questions and vote answers. This can impact the performance when recommending questions to users (for obtaining their answers) by using their historical answering behaviors on existing studies. To address the data sparsity issue, we propose AskMe, which aims to leverage the rich, hybrid behavior interactions in CQA to improve the question recommendation performance. On the one hand, we model the rich correlations between the users' diverse behaviors (e.g., answer, follow, vote) to obtain the individual-level behavior interaction. On the other hand, we model the sophisticated behavioral associations between similar users to obtain the community-level behavior interaction. Finally, we propose the way of element-level fusion to mix these two kinds of interactions together to predict the ranking scores. A dataset collected from Zhihu (1126 users, 219434 questions) is utilized to evaluate the performance of the proposed model, and the experimental results show that our model has gained the best performance compared to baseline methods, especially when the historical answering behaviors data is scarce.

* WWWJ 2021 
* 26 pages; 10 fingures 

  Access Paper or Ask Questions

FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation

Apr 01, 2022
Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang

Single-tower models are widely used in the ranking stage of news recommendation to accurately rank candidate news according to their fine-grained relatedness with user interest indicated by user behaviors. However, these models can easily inherit the biases related to users' sensitive attributes (e.g., demographics) encoded in training click data, and may generate recommendation results that are unfair to users with certain attributes. In this paper, we propose FairRank, which is a fairness-aware single-tower ranking framework for news recommendation. Since candidate news selection can be biased, we propose to use a shared candidate-aware user model to match user interest with a real displayed candidate news and a random news, respectively, to learn a candidate-aware user embedding that reflects user interest in candidate news and a candidate-invariant user embedding that indicates intrinsic user interest. We apply adversarial learning to both of them to reduce the biases brought by sensitive user attributes. In addition, we use a KL loss to regularize the attribute labels inferred from the two user embeddings to be similar, which can make the model capture less candidate-aware bias information. Extensive experiments on two datasets show that FairRank can improve the fairness of various single-tower news ranking models with minor performance losses.

  Access Paper or Ask Questions

Knowledge Graph Convolutional Networks for Recommender Systems with Label Smoothness Regularization

May 11, 2019
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, Zhongyuan Wang

Knowledge graphs capture interlinked information between entities and they represent an attractive source of structured information that can be harnessed for recommender systems. However, existing recommender engines use knowledge graphs by manually designing features, do not allow for end-to-end training, or provide poor scalability. Here we propose Knowledge Graph Convolutional Networks (KGCN), an end-to-end trainable framework that harnesses item relationships captured by the knowledge graph to provide better recommendations. Conceptually, KGCN computes user-specific item embeddings by first applying a trainable function that identifies important knowledge graph relations for a given user and then transforming the knowledge graph into a user-specific weighted graph. Then, KGCN applies a graph convolutional neural network that computes an embedding of an item node by propagating and aggregating knowledge graph neighborhood information. Moreover, to provide better inductive bias KGCN uses label smoothness (LS), which provides regularization over edge weights and we prove that it is equivalent to label propagation scheme on a graph. Finally, We unify KGCN and LS regularization, and present a scalable minibatch implementation for KGCN-LS model. Experiments show that KGCN-LS outperforms strong baselines in four datasets. KGCN-LS also achieves great performance in sparse scenarios and is highly scalable with respect to the knowledge graph size.

* KDD 2019 research track oral 

  Access Paper or Ask Questions

LOH and behold: Web-scale visual search, recommendation and clustering using Locally Optimized Hashing

Jul 30, 2016
Yannis Kalantidis, Lyndon Kennedy, Huy Nguyen, Clayton Mellina, David A. Shamma

We propose a novel hashing-based matching scheme, called Locally Optimized Hashing (LOH), based on a state-of-the-art quantization algorithm that can be used for efficient, large-scale search, recommendation, clustering, and deduplication. We show that matching with LOH only requires set intersections and summations to compute and so is easily implemented in generic distributed computing systems. We further show application of LOH to: a) large-scale search tasks where performance is on par with other state-of-the-art hashing approaches; b) large-scale recommendation where queries consisting of thousands of images can be used to generate accurate recommendations from collections of hundreds of millions of images; and c) efficient clustering with a graph-based algorithm that can be scaled to massive collections in a distributed environment or can be used for deduplication for small collections, like search results, performing better than traditional hashing approaches while only requiring a few milliseconds to run. In this paper we experiment on datasets of up to 100 million images, but in practice our system can scale to larger collections and can be used for other types of data that have a vector representation in a Euclidean space.

* Accepted for publication at the 4th Workshop on Web-scale Vision and Social Media (VSM), ECCV 2016 

  Access Paper or Ask Questions

Learning, transferring, and recommending performance knowledge with Monte Carlo tree search and neural networks

May 06, 2020
Don M. Dini

Making changes to a program to optimize its performance is an unscalable task that relies entirely upon human intuition and experience. In addition, companies operating at large scale are at a stage where no single individual understands the code controlling its systems, and for this reason, making changes to improve performance can become intractably difficult. In this paper, a learning system is introduced that provides AI assistance for finding recommended changes to a program. Specifically, it is shown how the evaluative feedback, delayed-reward performance programming domain can be effectively formulated via the Monte Carlo tree search (MCTS) framework. It is then shown that established methods from computational games for using learning to expedite tree-search computation can be adapted to speed up computing recommended program alterations. Estimates of expected utility from MCTS trees built for previous problems are used to learn a sampling policy that remains effective across new problems, thus demonstrating transferability of optimization knowledge. This formulation is applied to the Apache Spark distributed computing environment, and a preliminary result is observed that the time required to build a search tree for finding recommendations is reduced by up to a factor of 10x.

* 8 pages, 2 figures 

  Access Paper or Ask Questions

Learning Heterogeneous Similarity Measures for Hybrid-Recommendations in Meta-Mining

Oct 04, 2012
Phong Nguyen, Jun Wang, Melanie Hilario, Alexandros Kalousis

The notion of meta-mining has appeared recently and extends the traditional meta-learning in two ways. First it does not learn meta-models that provide support only for the learning algorithm selection task but ones that support the whole data-mining process. In addition it abandons the so called black-box approach to algorithm description followed in meta-learning. Now in addition to the datasets, algorithms also have descriptors, workflows as well. For the latter two these descriptions are semantic, describing properties of the algorithms. With the availability of descriptors both for datasets and data mining workflows the traditional modelling techniques followed in meta-learning, typically based on classification and regression algorithms, are no longer appropriate. Instead we are faced with a problem the nature of which is much more similar to the problems that appear in recommendation systems. The most important meta-mining requirements are that suggestions should use only datasets and workflows descriptors and the cold-start problem, e.g. providing workflow suggestions for new datasets. In this paper we take a different view on the meta-mining modelling problem and treat it as a recommender problem. In order to account for the meta-mining specificities we derive a novel metric-based-learning recommender approach. Our method learns two homogeneous metrics, one in the dataset and one in the workflow space, and a heterogeneous one in the dataset-workflow space. All learned metrics reflect similarities established from the dataset-workflow preference matrix. We demonstrate our method on meta-mining over biological (microarray datasets) problems. The application of our method is not limited to the meta-mining problem, its formulations is general enough so that it can be applied on problems with similar requirements.

  Access Paper or Ask Questions

Multi-task recommendation system for scientific papers with high-way networks

Apr 21, 2022
Aram Karimi, Simon Dobnik

Finding and selecting the most relevant scientific papers from a large number of papers written in a research community is one of the key challenges for researchers these days. As we know, much information around research interest for scholars and academicians belongs to papers they read. Analysis and extracting contextual features from these papers could help us to suggest the most related paper to them. In this paper, we present a multi-task recommendation system (RS) that predicts a paper recommendation and generates its meta-data such as keywords. The system is implemented as a three-stage deep neural network encoder that tries to maps longer sequences of text to an embedding vector and learns simultaneously to predict the recommendation rate for a particular user and the paper's keywords. The motivation behind this approach is that the paper's topics expressed as keywords are a useful predictor of preferences of researchers. To achieve this goal, we use a system combination of RNNs, Highway and Convolutional Neural Networks to train end-to-end a context-aware collaborative matrix. Our application uses Highway networks to train the system very deep, combine the benefits of RNN and CNN to find the most important factor and make latent representation. Highway Networks allow us to enhance the traditional RNN and CNN pipeline by learning more sophisticated semantic structural representations. Using this method we can also overcome the cold start problem and learn latent features over large sequences of text.

* 9 pages, 9 figures, 2 tables 

  Access Paper or Ask Questions