BTSAMA

Q3 Computer Science
S. Lv, Liliya Pan
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引用次数: 0

摘要

针对个性化音乐推荐方法中存在的解释性低、推荐准确率低、信息难以有效提取等问题,提出了一种结合TextCNN和注意力的个性化音乐推荐方法。首先,将TextCNN模型与BERT相结合,获取局部音乐连续特征;其次,引入自关注来解决TextCNN未关注的剩余遗漏的非连续特征。最后,采用多头注意机制获取热点音乐和用户兴趣音乐的特征,并采用级联融合方法实现点击预测。实验结果表明,该模型可以有效地推荐个性化音乐,其在FMA和GTZAN数据集上的MAE值分别为0.156和0.146,比其他比较模型至少提高了6.6%和3.3%。其在FMA和GTZAN数据集上的RMSE结果值分别为0.185和0.164,比其他比较模型分别提高了12.4%和5.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BTSAMA
To deal with the problems of occurring personalized music recommendation methods, for instance, low explanation, low accuracy of recommendation, and difficulty extracting information effectively, a personalized music recommendation method combining TextCNN and attention is proposed. Firstly, TextCNN model and BERT are combined to capture local music continuous features. Secondly, self-attention is introduced to solve the remaining omitted non-continuous features that are not paid attention by TextCNN. Finally, multi-headed attention mechanism is used to get features of hotspot music and user's interest music, and cascading fusion method is used to achieve click prediction. Experimentally, the proposed model can effectively recommend personalized music, its MAE values on FMA and GTZAN datasets are 0.156 and 0.146, respectively, improving by at least 6.6% and 3.3% compared to other comparative models. And its RMSE result values on the FMA and GTZAN datasets are 0.185 and 0.164, respectively, improving by at least 12.4% and 5.2% compared to other comparative models.
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来源期刊
CiteScore
3.50
自引率
0.00%
发文量
30
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