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Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems assume that for an individual, this vector is the same for all items. However, this assumption is often invalid, especially when considering a user's interactions with items of diverse characteristics. To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named A$^3$NCF, which can capture the varying aspect attentions that a user pays to different items. Specifically, we design a new topic model to extract user preferences and item characteristics from review texts. They are then used to 1) guide the representation learning of users and items, and 2) capture a user's special attention on each aspect of the targeted item with an attention network. Through extensive experiments on several large-scale datasets, we demonstrate that our model outperforms the state-of-the-art review-aware recommender systems in the rating prediction task.

Electrocatalytic nitrogen reduction reaction (NRR) has been an important area for many scientists. However, high voltage requirements, low NH3 yield, and poor stability remain the biggest challenges for NRR. Here, novel high‐entropy alloys RuFeCoNiCu nanoparticles with small size (≈16 nm) and uniformity, prepared in oil phase at atmospheric pressure and low temperature (≤250 °C) are reported for the first time and are applied to NRR. According to the experiments, there is a high NH3 yield at a low overpotential. It has a surprising NH3 yield of 57.1 µg h–1 mgcat−1 (11.4 µg h–1 cm–2) at 0.05 V versus RHE in 0.1 m KOH, and the corresponding Faradaic efficiency reaches 38.5%, which is the electrocatalyst with the highest NH3 yield at the voltage of 0.05 V versus RHE reported so far. Similarly, the material also exhibits excellent electrochemical properties in other electrolytes such as 0.1 m Li2SO4, 0.1 m Na2SO4, and 0.1 m HCl electrolytes. Besides, after the 100 h test, only slightly diminished in activity. Theoretical calculation shows that Fe surrounded by alloy metals is the best site for N2 adsorption and activation. Co‐Cu and Ni‐Ru couples show an excellent capacity to surface hydrogenation at a low overpotential.

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