本文選題：網(wǎng)絡(luò)電視直播 + 實(shí)時(shí)推薦��； 參考：《微電子學(xué)與計(jì)算機(jī)》2016年12期

【摘要】：提出一種基于語(yǔ)義和用戶(hù)偏好的網(wǎng)絡(luò)電視直播實(shí)時(shí)推薦方法.該方法首先基于用戶(hù)的歷史記錄構(gòu)建用戶(hù)偏好模型,然后使用基于詞向量的語(yǔ)義相似度計(jì)算方法,分別計(jì)算待推薦節(jié)目和用戶(hù)記錄或待推薦節(jié)目和用戶(hù)當(dāng)前觀看節(jié)目間的相似度,再結(jié)合該相似度和用戶(hù)偏好求取用戶(hù)對(duì)待推薦節(jié)目的虛擬興趣,最后選出虛擬興趣較高的一組節(jié)目作為對(duì)用戶(hù)的實(shí)時(shí)推薦.實(shí)驗(yàn)結(jié)果表明,此方法的命中率在實(shí)時(shí)預(yù)測(cè)推薦的場(chǎng)景下較對(duì)比方法提高了10%以上,且在實(shí)時(shí)節(jié)目推薦的場(chǎng)景下有更好的推薦效果.
[Abstract]:A real-time broadcast real-time recommendation method based on semantic and user preferences is proposed. This method first constructs user preference model based on user history records, and then uses semantic similarity calculation method based on word vectors to calculate respectively the recommended programs and user records or the recommended programs and the user's current viewing programs. The similarity degree is combined with the similarity degree and the user preference to obtain the user's virtual interest in the recommended program. Finally, a group of programs with higher virtual interest is selected as the real-time recommendation for the user. The experimental results show that the hit rate of this method is 10% higher than the comparison method under the real-time prediction recommendation scene, and is recommended in the real-time program. There is a better recommendation in the scene.
【作者單位】： 中國(guó)科學(xué)技術(shù)大學(xué)信息科學(xué)技術(shù)學(xué)院;上海文廣互動(dòng)電視有限公司;
【基金】：中科院先導(dǎo)課題(XDA060112030)
【分類(lèi)號(hào)】：TP391.3

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