DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant Recommendations

In e-commerce platforms, the relevant recommendation is a unique scenario providing related items for a trigger item that users are interested in. However, users' preferences for the similarity and diversity of recommendation results are dynamic and vary under different conditions. Moreover, individual item-level diversity is too coarse-grained since all recommended items are related to the trigger item. Thus, the two main challenges are to learn fine-grained representations of similarity and diversity and capture users' dynamic preferences for them under different conditions. To address these challenges, we propose a novel method called the Dynamic Preference-based and Attribute-aware Network (DPAN) for predicting Click-Through Rate (CTR) in relevant recommendations. Specifically, based on Attribute-aware Activation Values Generation (AAVG), Bi-dimensional Compression-based Re-expression (BCR) is designed to obtain similarity and diversity representations of user interests and item information. Then Shallow and Deep Union-based Fusion (SDUF) is proposed to capture users' dynamic preferences for the diverse degree of recommendation results according to various conditions. DPAN has demonstrated its effectiveness through extensive offline experiments and online A/B testing, resulting in a significant 7.62% improvement in CTR. Currently, DPAN has been successfully deployed on our e-commerce platform serving the primary traffic for relevant recommendations. The code of DPAN has been made publicly available

FAN: Fatigue-Aware Network for Click-Through Rate Prediction in E-commerce Recommendation

Since clicks usually contain heavy noise, increasing research efforts have been devoted to modeling implicit negative user behaviors (i.e., non-clicks). However, they either rely on explicit negative user behaviors (e.g., dislikes) or simply treat non-clicks as negative feedback, failing to learn negative user interests comprehensively. In such situations, users may experience fatigue because of seeing too many similar recommendations. In this paper, we propose Fatigue-Aware Network (FAN), a novel CTR model that directly perceives user fatigue from non-clicks. Specifically, we first apply Fourier Transformation to the time series generated from non-clicks, obtaining its frequency spectrum which contains comprehensive information about user fatigue. Then the frequency spectrum is modulated by category information of the target item to model the bias that both the upper bound of fatigue and users' patience is different for different categories. Moreover, a gating network is adopted to model the confidence of user fatigue and an auxiliary task is designed to guide the learning of user fatigue, so we can obtain a well-learned fatigue representation and combine it with user interests for the final CTR prediction. Experimental results on real-world datasets validate the superiority of FAN and online A/B tests also show FAN outperforms representative CTR models significantly

Deposition Process and Properties of Electroless Ni-P-Al2O3 Composite Coatings on Magnesium Alloy

Abstract To improve the corrosion resistance and wear resistance of electroless nickel-phosphorus (Ni-P) coating on magnesium (Mg) alloy. Ni-P-Al2O3 coatings were produced on Mg alloy from a composite plating bath. The optimum Al2O3 concentration was determined by the properties of plating bath and coatings. Morphology growth evolution of Ni-P-Al2O3 composite coatings at different times was observed by using a scanning electronic microscope (SEM). The results show that nano-Al2O3 particles may slow down the replacement reaction of Mg and Ni2+ in the early stage of the deposition process, but it has almost no effect on the rate of Ni-P auto-catalytic reduction process. The anti-corrosion and micro-hardness tests of coatings reveal that the Ni-P-Al2O3 composite coatings exhibit better performance compared with Ni-P coating owing to more appropriate crystal plane spacing and grain size of Ni-P-Al2O3 coatings. Thermal shock test indicates that the Al2O3 particles have no effect on the adhesion of coatings. In addition, the service life of composite plating bath is 4.2 metal turnover, suggesting it has potential application in the field of magnesium alloy

Sensitivity analysis of heat and mass transfer characteristics during forced-air cooling process of peaches on different air-inflow velocities

Peach is one of the most perishable fruits. During forced-convection cooling, the heat sources (respiratory and evaporative latent heat) internal to freshly harvested peaches have a remarkable influence on its evaluation of cooling characteristics with respect to various cooling strategies. Therefore, to improve the accuracy of simulation results in peaches cooling, the term of heat source was coded as detailed procedures and included into a computational fluid dynamics (CFD) model. By comparing the temperature simulated with and without considering these heat sources, it is found that a reasonable decrease in variations of cooling performances is obtained with sustained increase in air-inflow velocities. A maximum discrepancy in peaches volume-weighted average temperature (∆Tvwa-max) is mainly concentrated in 0.1–0.3°C when the air-inflow velocity not exceeds 1.7 m/s, and its corresponded 7/8ths cooling time (SECT) is also prolonged by 1–6 min. This means that, below 1.7 m/s, these heat sources should be added as a term into the heat transfer equations for modifying the mathematical model inside peaches computational domain. Furthermore, the feasibility of this modeling method is confirmed by a great agreement with experiments, and its modified model has a higher accuracy with the decreased RMSE and MAPE values of 6.90%–11.26% and 7.28%–12.95%, respectively

Lead Selenide Thin Films and Uncooled Midinfrared Detectors by Vapor Phase Deposition

The broad application of lead selenide (PbSe)-based uncooled midinfrared (MIR) detectors has been hindered by the nonuniformity of wafer-level films prepared by the conventional chemical bath deposition (CBD) method. Herein, using a vapor phase deposition (VPD) approach, we demonstrate the deposition of 3 in. wafer-scale uniform PbSe thin films with thicknesses of up to 1.5 μm. To trigger the MIR response, the as-grown films were sensitized at an elevated temperature in an oxygen–iodine atmosphere. We discovered that the key to spark off the MIR response of the PbSe detector originated from the self-assembled rodlike microstructures in the thin films, which can be controlled by the I2/PbSe flux ratio in the VPD process. At room temperature, the thin film detector exhibits an excellent optoelectronic performance, with detectivity up to 2.4 × 109 cm Hz1/2 W–1 achieved under optimized conditions. Our results show that the VPD method opens up a new avenue to the industrialization of uncooled lead-salt MIR detectors