考量適當時機的推薦方法

我們針對評分的時間戳提出一改進方法，使現有以項目為基礎的（以電影為基礎的）協同過濾算法，可考量在適當的時機推薦。在過去的幾年中，研究人員大多著重在研究使用者所給的評分分數；他們分析使用者過去給的評分分數，來預測未知的評分分數。然而我們發現評分分數，不應是我們所關注的唯一問題，「該什麼時候推薦電影給使用者？」對推薦系統來說也是重要的，因為使用者的消費習慣是因人而異的。為了能在適當時機將電影推薦給使用者，我們針對每部電影，依據評分的時間戳來分析評分分布，發現用戶傾向觀看類似電影在類似的時機。我們將實驗做在MovieLens Data Sets上，系統的表現是藉由評估一個特定時機內經由不同方法所產生的推薦名單，實驗結果顯示我們系統在適當時機推薦方面的實用性。

關鍵字

推薦系統；協同過濾；廣告

並列摘要

We propose an approach to adapt the existing item-based (movie-based) collaborative filtering algorithm based on the timestamps of the ratings to recommend movies to users at opportune moments. Over the last few years, researchers focused recommendation problems on rating scores mostly. They analyzed users’ previous rating scores and predicted those unknown rating scores. However, we found rating scores are not the only problem we have to concern about. When to recommend movies to users is also important for a recommender system since users’ shopping habits vary from person to person. To recommend movies to users at opportune moments, we analyzed the rating distribution of each movie by the timestamps and found that a user tends to watch similar movies at similar moments. Several experiments have been conducted on MovieLens Data Sets . The system is evaluated by different recommendation lists during a specific period of time - tspecific, and the experimental results show the usefulness of our system.

並列關鍵字

Recommender Systems ； Collaborative Filtering ； Advertising

參考文獻

[1] B. Sarwar, G. Karypis, J. Konstan and J. Riedl. Item-based collaborative filtering recommendation algorithms. WWW10, 2001.

[2] Bass, Frank. A new product growth model for consumer durables. Management Science 15 (5), (1969), pp.215-227

[3] C.-N. Ziegler, S. M. McNee, J. A. Konstan and G. Lausen. Improving recommendation lists through topic diversification. WWW’05, pp.22-32.

[4] D. Kempe, J. Kleinberg and E. Tardos. Maximizing the spread of influence through a social network. ACM SIGKDD’03.

[5] E. M. Rogers. Diffusion of Innovations. The Free Press: New York, 1995.

國際替代計量

考量適當時機的推薦方法

全文下載

主題瀏覽