Explainable Recommendation: Theory and Applications

Although personalized recommendation has been investigated for decades, the wide adoption of Latent Factor Models (LFM) has made the explainability of recommendations a critical issue to both the research community and practical application of recommender systems. For example, in many practical systems the algorithm just provides a personalized item recommendation list to the users, without persuasive personalized explanation about why such an item is recommended while another is not. Unexplainable recommendations introduce negative effects to the trustworthiness of recommender systems, and thus affect the effectiveness of recommendation engines. In this work, we investigate explainable recommendation in aspects of data explainability, model explainability, and result explainability, and the main contributions are as follows: 1. Data Explainability: We propose Localized Matrix Factorization (LMF) framework based Bordered Block Diagonal Form (BBDF) matrices, and further applied this technique for parallelized matrix factorization. 2. Model Explainability: We propose Explicit Factor Models (EFM) based on phrase-level sentiment analysis, as well as dynamic user preference modeling based on time series analysis. In this work, we extract product features and user opinions towards different features from large-scale user textual reviews based on phrase-level sentiment analysis techniques, and introduce the EFM approach for explainable model learning and recommendation. 3. Economic Explainability: We propose the Total Surplus Maximization (TSM) framework for personalized recommendation, as well as the model specification in different types of online applications. Based on basic economic concepts, we provide the definitions of utility, cost, and surplus in the application scenario of Web services, and propose the general framework of web total surplus calculation and maximization.Comment: 169 pages, in Chinese, 3 main research chapter

Data_Sheet_1_iTRAQ-based proteomics analysis of Bacillus pumilus responses to acid stress and quorum sensing in a vitamin C fermentation system.docx

Microbial consortia play a key role in human health, bioenergy, and food manufacturing due to their strong stability, robustness and versatility. One of the microbial consortia consisting of Ketogulonicigenium vulgare and Bacillus megaterium for the production of the vitamin C precursor, 2-keto-L-gulonic acid (2-KLG), has been widely used for large-scale industrial production. To further investigate the cell–cell communication in microbial consortia, a microbial consortium consisting of Ketogulonicigenium vulgare and Bacillus pumilus was constructed and the differences in protein expression at different fermentation time points (18 h and 40 h) were analyzed by iTRAQ-based proteomics. The results indicated that B. pumilus was subjected to acid shocks in the coculture fermentation system and responded to it. In addition, the quorum sensing system existed in the coculture fermentation system, and B. pumilus could secrete quorum-quenching lactonase (YtnP) to inhibit the signaling pathway of K. vulgare. This study offers valuable guidance for further studies of synthetic microbial consortia.</p

Correction to “Regioselective Synthesis of Highly Functionalized Pyrazoles from <i>N</i>‑Tosylhydrazones”

Correction to “Regioselective Synthesis of Highly Functionalized Pyrazoles from N‑Tosylhydrazones

DataSheet1_CAT-CPI: Combining CNN and transformer to learn compound image features for predicting compound-protein interactions.docx

Compound-protein interaction (CPI) prediction is a foundational task for drug discovery, which process is time-consuming and costly. The effectiveness of CPI prediction can be greatly improved using deep learning methods to accelerate drug development. Large number of recent research results in the field of computer vision, especially in deep learning, have proved that the position, geometry, spatial structure and other features of objects in an image can be well characterized. We propose a novel molecular image-based model named CAT-CPI (combining CNN and transformer to predict CPI) for CPI task. We use Convolution Neural Network (CNN) to learn local features of molecular images and then use transformer encoder to capture the semantic relationships of these features. To extract protein sequence feature, we propose to use a k-gram based method and obtain the semantic relationships of sub-sequences by transformer encoder. In addition, we build a Feature Relearning (FR) module to learn interaction features of compounds and proteins. We evaluated CAT-CPI on three benchmark datasets—Human, Celegans, and Davis—and the experimental results demonstrate that CAT-CPI presents competitive performance against state-of-the-art predictors. In addition, we carry out Drug-Drug Interaction (DDI) experiments to verify the strong potential of the methods based on molecular images and FR module.</p

Effect of EE on protein level of Bcl-2, Bax, and caspase-3 activities.

<p>Western blots of (A) Expressions of Bax, Bcl-2 and caspase-3 proteins of myocardial tissue in the SA, EP and EE groups at 24 h after ROSC. (B) Expressions of Bcl-2/Bax proteins of myocardial tissue at 24 h after ROSC. (C) Quantification of Bax, Bcl-2 and active caspase-3 protein levels. The value represent mean ± SD. SA  =  saline, EP =  epinephrine, EE  =  epinephrine combined with esmolol. *<i>p</i><0.05, **<i>p</i><0.01 vs.SA, ^#P<0.05 vs. EP (repeated-measures ANOVA).</p

Serum lactate concentration (mmol/L) at baseline and throughout the study time points in SA, EP and EE groups.

<p>±SD. SA  =  saline, EP =  epinephrine, EE  =  epinephrine combined with esmolol. Δ <i>p</i><0.05 vs. baseline, *<i>p</i><0.05 vs.SA, ^#P<0.05 vs. EP (repeated-measures ANOVA). Values are mean</p

Resuscitational outcome in SA, EP and EE groups.

<p>±SD or number (n). SA  =  saline, EP =  epinephrine, EE  =  epinephrine combined with esmolol. ROSC  =  restoration of spontaneous circulation. *<i>p</i><0.05, **<i>p</i><0.01 vs.SA, ^#P<0.05 vs. EP. (a Chi-square analysis has been utilized) Values are mean </p

Ultra structure of the myocardium under an electron microscope.

<p>(Fig.2A, B): normal myocardial cell structure and normal mitochondria structure in the SHAM group (arrows); (Fig.2C, D): myocardial fiber and intercalated disk were obviously disordered, broken, even dissolved in the SA group (arrows); (Fig. 2E, F): myofibril organelles were extensively damaged and the myocardium exhibited progressive, severe deterioration in the EP group (arrows). (Fig. 2G, H): moderate edema occurred in the mitochondria and sarcoplasmic reticula in the EE groups (arrows).</p

Serum cTNI concentration (ng/mL) at baseline and throughout the study time points in SA, EP and EE groups.

<p>±SD.SA  =  saline, EP =  epinephrine, EE  =  epinephrine combined with esmolol. Δ p<0.05 vs. baseline, *p<0.05 vs.SA, #P<0.05 vs. EP (repeated-measures ANOVA). Values are mean </p

Baseline characteristics data among SA, EP and EE groups.

<p>±SD.SA  =  saline, EP =  epinephrine, EE  =  epinephrine combined with esmolol. Values are mean </p