基於動態語言模型的個人化閱讀推薦系統

Personalized Reading Recommendation System Based on Dynamic Language Models

邱中人(Chung-Jen Chiu) ； 沈民新(Min-Hsin Shen)

《電腦與通訊》 139期 (2011/06) Pp. 88-95

https://doi.org/10.29917/CCLTJ.201106.0016

摘要

一般推薦技術主要著眼於提高推薦資訊的正確率。但對數位閱讀而言，如何考慮推薦內容的語言風格則較少觸及。本篇論文提出根據推薦資訊是否符合使用者熟悉的語言風格來評量推薦資訊的優劣。為此，我們採用動態語言模型來描述使用者的語言風格，藉由動態語言模型估算推薦資訊的歧異度，作為推薦資訊的排序依據。實驗結果顯示動態語言模型確實可精準地描述使用者的語言風格，並提供使用者符合其偏好之語言風格的推薦資訊。

關鍵字

動態語言模型 ； 語言模型調適 ； 個人化服務 ； 推薦系統

並列摘要

Most recommendation system focuses on accuracy of recommendation results. However, language styles of suggested contents are rarely discussed for digital reading. This work proposes a recommendation approach where data items are ranked based on their familiarities with user-preferred language styles. Adaptable language models are applied for modeling language styles. Recommended items are rated according to their perplexities of the language models of target users. Experimental results show that dynamic language models can describe continuously changing language styles of users, thus the recommended information satisfies users with their familiar language styles.

並列關鍵字

Dynamic Language Model ； Language Model Adaptation ； Personalized Services ； Recommendation System