Titlebook: Web and Big Data; 7th International Jo Xiangyu Song,Ruyi Feng,Geyong Min Conference proceedings 2024 The Editor(s) (if applicable) and The

財政

,Time-Aware Preference Recommendation Based on?Behavior Sequence,pecially, long and short-term based methods capture user preferences and provide more precise recommendations. However, they rarely consider the effect of time intervals and limit the short-term preferences’ weight in predicting the next items. In this paper, we propose a novel model called TPR-BS (

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紅潤

,Efficient Multi-object Detection for?Complexity Spatio-Temporal Scenes,w of traffic on roads. However, the existing algorithms are inefficient in detecting real scenarios due to the following drawbacks: (1) a scarcity of traffic scene datasets; (2) a lack of tailoring for specific scenarios; and (3) high computational complexity, which hinders practical use. In this pa

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冬眠

,Efficient Multi-object Detection for?Complexity Spatio-Temporal Scenes,w of traffic on roads. However, the existing algorithms are inefficient in detecting real scenarios due to the following drawbacks: (1) a scarcity of traffic scene datasets; (2) a lack of tailoring for specific scenarios; and (3) high computational complexity, which hinders practical use. In this pa

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,NV-QALSH+: Locality-Sensitive Hashing Optimized for?Non-volatile Memory,te-of-the-art LSH method, is a disk-based algorithm and suffers from high latency of disk I/O, even though it exploits disk-friendly B+-Trees as index data structures. On the other hand, DRAM-based methods occupy large amounts of expensive DRAM space and have long index rebuilt time. To solve the ha