The Cover Feature shows the natural cycle of harvesting silicon from soil and carbon from the air by using rice husks. A molten salt electrolysis approach is able to convert the rice husks to SiC/C or Si/C composites ...
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The Cover Feature shows the natural cycle of harvesting silicon from soil and carbon from the air by using rice husks. A molten salt electrolysis approach is able to convert the rice husks to SiC/C or Si/C composites that are used as high‐performance anode in lithium‐ion *** information can be found in the Article by Z. Zhao et al.
In this paper, we investigate the observational constraints on the scenario of vacuum energy interacting with cold dark matter. We consider eight typical interaction forms in such an interacting vacuum energy scenario...
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TianQin is a planned space-based gravitational wave (GW) observatory consisting of three earth orbiting satellites with an orbital radius of about 105 km. The satellites will form a equilateral triangle constellation ...
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Nowadays, research on session-based recommender systems (SRSs) is one of the hot spots in the recommendation domain. Existing methods make recommendations based on the user’s current intention (also called short-term...
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Nowadays, research on session-based recommender systems (SRSs) is one of the hot spots in the recommendation domain. Existing methods make recommendations based on the user’s current intention (also called short-term preference) during a session, often overlooking the specific preferences associated with these intentions. In reality, users usually exhibit diverse preferences for different intentions, and even for the same intention, individual preferences can vary significantly between users. As users interact with items throughout a session, their intentions can shift accordingly. To enhance recommendation quality, it is crucial not only to consider the user’s intentions but also to dynamically learn their varying preferences as these intentions change. In this paper, we propose a novel Intention-sensitive Preference Learning Network (IPLN) including three main modules: intention recognizer, preference detector, and prediction layer. Specifically, the intention recognizer infers the user’s underlying intention within his/her current session by analyzing complex relationships among items. Based on the acquired intention, the preference detector learns the intention-specific preference by selectively integrating latent features from items in the user’s historical sessions. Besides, the user’s general preference is utilized to refine the obtained preference to reduce the potential noise carried from historical records. Ultimately, the fine-tuned preference and intention collaborate to instruct the next-item recommendation in the prediction layer. To prove the effectiveness of the proposed IPLN, we perform extensive experiments on two real-world datasets. The experiment results demonstrate the superiority of IPLN compared with other state-of-the-art models.
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