透過社群關係與個人行為進行新聞推薦

Abstract

近年來網路媒體越來越多，傳統報章雜誌媒體也逐漸網路化，新聞讀者也因為網路的便利大都轉為使用網路閱讀新聞，新聞的產量也爆炸式的增長，讀者透過自身人力的搜尋，很難找到符合個人需求的內容，因此幫助讀者有效的篩選、自動的提供符合使用者興趣的新聞是一個非常重要的課題。 在新聞推薦系統上，因為新聞的變化性高又要許多的隱性需求，因此在推薦上遭遇許許多多的困難，包括使用者需求多元、使用者需求不明確、新聞的時效性短暫…等問題。 本研究在推薦系統中，提出篩選候選文章的方法，有效減少新聞的數量，且透過自動的方法計算出新聞的時效性，讓不同的媒體有獨有的時效性遞減參數，解決時效性短暫之問題，且系統中納入讀者的社群關係，滿足讀者閱讀新聞多元的需求，和透過使用者在系統中活動的紀錄，推薦出讀者感興趣和有價值的文章給讀者。

The past decade has witnessed a tremendous serge of network news media. Instead of relying on traditional media as the main source of news, more and more people have turned to online news. This explosive growth made it difficult for users to choose the articles that might be of interest to an individual reader, and how to recommend news that might be relevant to a reader becomes an interesting issue. Because of the implicit demand of individual readers and the variety of news, designing an effective news recommendation system has to overcome several problems, which include the multiple needs of a user, vagueness in one’s expectation, the potentially short time span of news, the vast amount of news articles that need to be considered, etc. In this thesis, we propose a method to recommend news to a user. Our algorithm takes into consideration the timeliness of a news article, the behavior of members of a user’s social network, the multiple needs and the past activities of the reader.