How to build an adaptive learning/recommendation system for a question bank? [D]
热度趋势
趋势数据积累中
百分比基于当前可用热度信号，而非评论数或独立用户人数。
一位开发者正在寻求构建一个用于题库的自适应学习和推荐系统。该系统的目标是识别学生的强项和弱项，推荐针对薄弱领域的题目，同时避免让学生感到气馁。它还应重新引入旧主题以评估记忆情况，并根据表现调整推荐，最终帮助学生随着时间的推移提高解决问题的能力。
Hey! Can you tell me how you would go about building a recommendation engine for our question bank?
The idea is that it understands a student’s strengths and weaknesses and recommends questions accordingly — more questions around the areas they’re weak in, but without making them so difficult that they feel demotivated.
I also want it to occasionally bring back questions from older topics to check whether the student has forgotten something. Based on how they perform, it could then decide whether to recommend more questions from that topic or move on.
Basically, the goal is for the recommendation engine to continuously understand where the student is struggling and use that to help them become better at problem-solving over time.
I was learning some basics of AI/ML and this question came to my mind, so I was just curious — do you have any idea how something like this could be built?