- You can build an effective recommender system with as little as two people.
- As you have more users, you tend to have more training data. Hence, you may have more accurate recommendations.
- More accurate recommendations may not be important to your users.
- Yes, we've know that very well - Prediction accuracy is not enough，前一篇談如何評估推薦系統時，我已經提到要花點時間想想 usefulness 是怎麼一回事兒了。Stay tuned.
- The exact count of your users may not matter as much as the diversity of your users.
- 我完全同意， diversity 是影響推薦系統更關鍵的因素
- A good rule of thumb is that you should have many more users than you have items to recommend.
- Given the right algorithms, your accuracy will improve monotonically with the number of users and the amount of training data.
- The users may enter feedback data to correct the assumptions of your recommender system and thus, improve it over time.
- Ya! 用戶的回饋是 CF 最重要的"資產"了
All in all, ，今日是戊子年大年初一，小弟在此向所有相識或不相識的您拜年，戊子大吉，新年快樂！