社群媒體現存兩個大問題，分別是資訊過多以及如何了解使用者的興趣。依據使用者喜好所建立的推薦系統可同時解決這兩個問題。因此發掘言在喜好資訊將成為了解社群媒體用戶的關鍵。所以我們提出結合時間與社群資訊的推薦系統用於探尋使用者的潛在喜好。

Social media is facing two big problems in their whole history, which are the information overload and how good they understand the interest of their users. One way to cope with those two problems all at once is applying a recommendation system that fits with user interest. Regarding all that matter, finding latent information has become crucial for better understanding of the user of social media.

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