我們在此利用人類大腦記憶原理來給予不同的時間區段相對應的衰退函數:瞬時記憶等級、短期記憶等級、長期記憶等級，每當有一筆新的評級進來時，與其相關的項目群集就會被激活來到新的衰退等級(瞬時記憶等級)，我們會設置一個門檻值，若是在一定時間內的激活次數小於門檻值的話，我們會給予相對應的懲罰，反之若是一個群集持續被激活的話，那我們會將他的衰退函數提升至更高的等級(短期記憶等級)，同樣的若是在一定時間內激活次數小於門檻值的話，我們還是會給予他懲罰，而最後若是這個群集有持續被激活，我們會將他的衰退等級提升至最高級(長期記憶等級)，一旦達到這個等級，就算之後的激活次數沒有達到門檻值也不會有懲罰，只是會讓它隨著他的衰退函數而下降。最後將衰退的權重加權在以項目為基礎的協同過濾系統的預測函數內，這是屬於後處理的方式。

Traditional time weighted collaborative filtering systems have a single decay function. But, it is not reasonable that lets the weight decay by only function. We propose a method to solve it. In this paper, we propose a new method improve on time weighted collaborative filtering. We use the principle of human brain memory to give different time segments corresponding to the recession function: instantaneous memory level, short-term memory level, long-term memory level, whenever there is a new rating come in, and its related item cluster will be activated to a new recession level (instantaneous memory level). We set a threshold value. If the number of activations is less than the threshold for a certain period of time, we will give the corresponding penalty, otherwise we will raise his decay function to a higher level (short-term memory level), and so on. Once the long-term memory level is reached, even if the number of activations does not reach the threshold, there will be no penalty, but will let it fall with his decay function. Finally, the weight of the decay is weighted within the Item-based predictive function, which is a post-processing approach.

Table of Contents

Chinese Abstract……………………………………………………….. I
Abstract……………………………………………………………… II
Table of Contents…………………………………………………….. IV
List of Figures……………………………………………………….. VI
List of Tables……………………………………………………….VII
Chapter 1	1
Introduction	1
Chapter 2	5
Related work	5
2.1Base on Item-based Collaborative Filtering	5
2.2Base on User-based Collaborative Filtering	6
2.3 Base on Matrix factorization	6
2.4Hybrid Method	7
2.5 Dynamic collaborative filtering	8
2.6 Memory of human brain	8
2.7 Decay functions	9
Chapter 3	10
Dynamic Time Weighted Collaborative Filtering	10
3.1	Item-based Collaborative Filtering Algorithms	11
3.1.1 Similarity calculation	11
3.1.2 Preference prediction	12
3.2	Baseline Estimates	12
3.3	H_clustering	12
3.4	Decay Function	15
3.5	Dynamic Time Weighted Model	17
3.6	Prediction	18
Chapter 4	20
Experiments	20
4.1 Experimental Setting	20
4.1.1 Data set	20
4.1.2 Metrics	20
4.2Experiment: MAE and RMSE	21
4.2.1 MAE and RMSE in different data ratio	21
4.2.2 MAE and RMSE in different data size	23
4.2.3 MAE and RMSE in different similarity function in our method	24
Chapter 5	26
Conclusion	26
Reference	27

 
List of Figures 

Figure 1: Program flow hart………………………….……………… 10
Figure 2: H Clustering......……………………………………….……15
Figure 3: decay function………………………………………………17
Figure 4: Dynamic Time Weighted Model flow chart………………18
Figure 5: MAE on MovieLens.………………………………………..22
Figure 6: RMSE on MovieLens.………………………………………22
  
List of Table 


Table 1: characteristics of MovieLens……………….……………….20
Table 2: MAE and RMSE in MovieLens(100K)……………………..23
Table 3: MAE and RMSE in MovieLens(1M)..…………….………..23
Table 4: Different similarity function in our method…….………….24

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