A survey on causal inference for recommendation

Causal inference has recently garnered significant interest among recommender system(RS)researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields.It offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation.Although there are already some valuable surveys on causal recommendations,they typically classify approaches based on the practical issues faced in RS,a classification that may disperse and fragment the uni-fied causal theories.Considering RS researchers’unfamiliarity with causality,it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective,thereby facilitating a deeper integration of causal inference in RS.This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal strategy.First,we introduce the fundamental concepts of causal inference as the basis of the following review.Subsequently,we propose a novel theory-driven taxonomy,categorizing existing methods based on the causal theory employed,namely those based on the potential outcome framework,the structural causal model,and general counterfactuals.The review then delves into the technical details of how existing methods apply causal inference to address particular recommender issues.Finally,we highlight some promising directions for future research in this field.Representative papers and open-source resources will be progressively available at https://github.com/Chrissie-Law/Causal-Inference-forRecommendation.

Pilot-Free End-to-End Underwater Acoustic Communication System Based on Autoencoder

The long delay spreads and significant Doppler effects of underwater acoustic(UWA)channels make the design of the UWA communication system more challenging.In this paper,we propose a learning-based end-to-end framework for UWA communications,leveraging a double feature extraction network(DFEN)for data preprocessing.The DFEN consists of an attentionbased module and a mixer-based module for channel feature extraction and data feature extraction,respectively.Considering the diverse nature of UWA channels,we propose a stack-network with a two-step training strategy to enhance generalization.By avoiding the use of pilot information,the proposed network can learn data mapping that is robust to UWA channels.Evaluation results show that our proposed algorithm outperforms the baselines by at least 2 dB under bit error rate(BER)10^(−2)on the simulation channel,and surpasses the compared neural network by at least 5 dB under BER 5×10^(−2)on the experiment channels.

Bisphenol A in combination with TNF-a selectively induces Th2 cell-promoting dendritic cells in vitro with an estrogen-like activity

Bisphenol A(BPA)is a monomer used in manufacturing a wide range of chemical products,including epoxy resins and polycarbonate.BPA,an important endocrine disrupting chemical that exerts estrogen-like activities,is detectable at nanomolar levels in human serum worldwide.The pregnancy associated doses of 17b-estradiol(E2)plus tumor-necrosis factor-a(TNF-a)induce distorted maturation of human dendritic cells(DCs)that result in an increased capacity to induce T helper(Th)2 responses.The current study demonstrated that the presence of BPA during DC maturation influences the function of human DCs,thereby polarizing the subsequent Th response.In the presence of TNF-a,BPA treatment enhanced the expression of CC chemokine ligand 1(CCL1)in DCs.In addition,DCs exposed to BPA/TNF-a produced higher levels of IL-10 relative to those of IL-12p70 on CD40 ligation,and preferentially induced Th2 deviation.BPA exerts the same effect with E2 at the same dose(0.01–0.1 mM)with regard to DC-mediated Th2 polarization.These findings imply that DCs exposed to BPA will provide one of the initial signals driving the development and perpetuation of Th2-dominated immune response in allergic reactions.

TOPIC SPLITTING： A HIERARCHICAL TOPIC MODEL BASED ON NON-NEGATIVE MATRIX FACTORIZATION

Hierarchical topic model has been widely applied in many real applications, because it can build a hierarchy on topics with guaranteeing of topics＇ quality. Most of traditional methods build a hierarchy by adopting low-level topics as new features to construct high-level ones, which will often cause semantic confusion between low-level topics and high-level ones. To address the above problem, we propose a novel topic model named hierarchical sparse NMF with orthogonal constraint （HSOC）, which is based on non-negative matrix factorization and builds topic hierarchy via splitting super-topics into sub-topics. In HSOC, we introduce global independence, local independence and information consistency to constraint the split topics. Extensive experimental results on real-world corpora show that the purposed model achieves comparable performance on topic quality and better performance on semantic feature representation of documents compared with baseline methods.

PSLDA:a novel supervised pseudo document-based topic model for short texts

Various kinds of online social media applications such as Twitter and Weibo,have brought a huge volume of short texts.However,mining semantic topics from short texts efficiently is still a challenging problem because of the sparseness of word-occurrence and the diversity of topics.To address the above problems,we propose a novel supervised pseudo-document-based maximum entropy discrimination latent Dirichlet allocation model(PSLDA for short).Specifically,we first assume that short texts are generated from the normal size latent pseudo documents,and the topic distributions are sampled from the pseudo documents.In this way,the model will reduce the sparseness of word-occurrence and the diversity of topics because it implicitly aggregates short texts to longer and higher-level pseudo documents.To make full use of labeled information in training data,we introduce labels into the model,and further propose a supervised topic model to learn the reasonable distribution of topics.Extensive experiments demonstrate that our proposed method achieves better performance compared with some state-of-the-art methods.