Harness 发表于 2025-3-30 09:48:55
http://reply.papertrans.cn/103/10216/1021520/1021520_51.pngBILK 发表于 2025-3-30 15:52:26
Distinguishing Social Ties in Recommender Systems by Graph-Based Algorithmserformance. When predicting ratings for an active user, his/her taste is influenced by the ones of his/her friends. Intuitively, different friends have different influential power to the active user. Most existing social recommendation algorithms, however, fail to consider such differences, and unfaWater-Brash 发表于 2025-3-30 20:15:34
http://reply.papertrans.cn/103/10216/1021520/1021520_53.png出汗 发表于 2025-3-30 22:16:00
Entity Correspondence with Second-Order Markov Logicions between properties and enable interaction between entity correspondence and property relation discovery. We also prove that second-order Markov Logic can be rephrased to first-order in practice. Experiments on a real world knowledge base show promising entity correspondence results, particularly in recall.Interim 发表于 2025-3-31 04:42:21
Entity Correspondence with Second-Order Markov Logicions between properties and enable interaction between entity correspondence and property relation discovery. We also prove that second-order Markov Logic can be rephrased to first-order in practice. Experiments on a real world knowledge base show promising entity correspondence results, particularly in recall.Urologist 发表于 2025-3-31 06:17:49
http://reply.papertrans.cn/103/10216/1021520/1021520_56.png冰河期 发表于 2025-3-31 12:40:06
Heterogeneous Metric Learning for Cross-Modal Multimedia Retrievalg the smoothness of learning results, we integrate graph regularization with Bayesian personalized ranking. The experimental results on two publicly available datasets show the effectiveness of our method.Factorable 发表于 2025-3-31 14:34:56
Efficient Online Novelty Detection in News Streamsdocuments in the text stream, thus leading to faster execution times. At the same time, our proposed approach outperforms several commonly used baselines when applied on a real-world news articles dataset.harbinger 发表于 2025-3-31 18:50:12
http://reply.papertrans.cn/103/10216/1021520/1021520_59.pngFlagging 发表于 2025-4-1 00:47:58
Detecting Opinion Drift from Chinese Web Comments Based on Sentiment Distribution Computing in a real comment set of Chinese forum. The results show that drift timestamps determined and opinion drifts detected correspond to the real event, so the approach proposed in this paper is feasible and effective in the application of Web public opinion analysis.