AGGER 发表于 2025-3-28 17:53:06
Jointly Learning Bilingual Sentiment and Semantic Representations for Cross-Language Sentiment Classesentations concatenated with their sentence-level sentiment representations. The proposed approach could capture rich sentiment and semantic information in BSSR learning process. Experiments on NLP&CC 2013 CLSC dataset show that our approach is competitive with the state-of-the-art results.吹气 发表于 2025-3-28 22:23:56
Network Structural Balance Analysis for Sina Microblog Based on Particle Swarm Optimization Algorithredefined the velocity and position updating rules of particles from a discrete perspective to solve the modeled optimization problem. Experiments on real data sets demonstrate our method is efficient.Popcorn 发表于 2025-3-28 23:40:08
http://reply.papertrans.cn/47/4652/465195/465195_43.pngstroke 发表于 2025-3-29 07:05:37
http://reply.papertrans.cn/47/4652/465195/465195_44.pngeuphoria 发表于 2025-3-29 11:00:50
http://reply.papertrans.cn/47/4652/465195/465195_45.pngPreserve 发表于 2025-3-29 12:18:50
The Impact of Profile Coherence on Recommendation Performance for Shared Accounts on Smart TVsn some applications, e.g., video recommendation on smart TVs, an account is often shared by multiple users who tend to have disparate interests. It poses great challenges for delivering personalized recommendations. In this paper, we propose the concept of profile coherence to measure the coherencevertebrate 发表于 2025-3-29 17:01:34
http://reply.papertrans.cn/47/4652/465195/465195_47.png准则 发表于 2025-3-29 21:19:05
http://reply.papertrans.cn/47/4652/465195/465195_48.pngCODA 发表于 2025-3-30 00:53:29
http://reply.papertrans.cn/47/4652/465195/465195_49.png专心 发表于 2025-3-30 07:08:33
User Preference Prediction in Mobile Search. Beyond traditional Cranfield paradigm, side-by-side user preference between two ranked lists does not rely on user behavior assumptions and has been shown to produce more accurate results comparing to traditional evaluation methods based on “query-document” relevance. On the other hand, result lis