Tag-based collaborative filtering recommendation system

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Title:	Tag-based collaborative filtering recommendation system
Other Titles:	以項目屬性為基礎的協同過濾系統
Authors:	林易辰;LIN, YI-CHEN
Contributors:	淡江大學資訊工程學系碩士班陳以錚;Chen, Yi-Cheng
Keywords:	Collaborative Filtering;Recommendation System;Tag;協同過濾;推薦系統;項目屬性
Date:	2017
Issue Date:	2018-08-03 15:00:09 (UTC+8)
Abstract:	本文中，我們提出了一個基於項目屬性的協同過濾推薦系統，它結合了項目屬性、調合平均權重、分群來推薦項目給用戶。我們的系統使用項目屬性來描述用戶的偏好，以便我們緩解資料稀疏與冷啟動的問題，並做出更廣泛的預測。我們使用調合平均權和分群來調整傳統的預測函數，因為我們認為用戶是否在同一群中，以及用戶的評分次數、共同評分次數，都會與預測結果有關，調合平均權重由兩個用戶間的評分次數、共同評分次數來計算。我們利用CIM當中的分群法對用戶進行分群，分群的結果與調合平均權重分別是為一個權重來調整預測函數。我們的方法可以在用戶間沒有共同評分的項目時也可以計算相似度，這是傳統協同過濾系統所做不到的。 In this article, we propose a tag-based collaborative filtering recommendation system which combines items’ tags, harmonic mean weight and cluster to recommend items to user. Our system uses items’ tags to describe user’s preference, so that we ease up data sparsity problem and cold start problem and make more widely prediction. We use harmonic mean weight and clustering to improve the former prediction function because we think that whether user are in the same cluster or not, and rating, co-rating times are related to predicted results. Harmonic mean weight[11] is calculated by rating times of two user. We utilize clustering method in CIM[12] to group user and see cluster result as a weight in prediction function. Our method can make prediction in some situation that other method cannot predict.
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

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