Possible Duplicate:
Where can I learn about recommendation systems?
我始终有兴趣地看到,根据我所讲的“类似”内容,网站如何向我推荐文章和用户。
在我浏览一个题为“相关条款”的项目时,它也可以提出项目建议。
我需要一些文章和图像来教我如何实施这种制度。 非常感谢。
最新情况:
我用一个关键词“Slope 1”
Possible Duplicate:
Where can I learn about recommendation systems?
我始终有兴趣地看到,根据我所讲的“类似”内容,网站如何向我推荐文章和用户。
在我浏览一个题为“相关条款”的项目时,它也可以提出项目建议。
我需要一些文章和图像来教我如何实施这种制度。 非常感谢。
最新情况:
我用一个关键词“Slope 1”
Wikipedia article, 是一个良好的开端。 此外,博客站有一些很好的信息和插图。
最简单的方法是使用“与本条一样的人”方法。 如果你跟踪每个用户的文章评级,并跟踪哪些人喜欢哪些条款,那么你就有一个建议制度的基础。
例如,说你重新看待A条。 该系统可以在其指数中研究所有像A条这样的用户。 从这份清单中,它可以编制一份清单,列出所有像A条这样的用户所喜欢的物品。 极有可能出现重大重叠(即有些文章是许多人喜欢的)。 你的算法跟踪了每一条有多少类似之处,然后显示最有投票权的N级。
这种简单的制度在许多情况中具有令人惊讶的效果,但并非完美。 你发现,这种特别受欢迎的文章占支配地位,即使它们与你重新思考的条款无关。 有办法防止大量民众的文章被污染。 一种办法是用浮动点编号来计算某一条分。 页: 1 因此,用户如喜欢100条,只给出<代码>1/10<>>>>>>,但只有4条条文的用户可向每一条提供<代码>1/2。 虽然这不错了“公平”,但它确实会削弱大量受欢迎但无关的物品的影响。
正如我所说的那样,这种做法是最简单的。 如果你重新寻找不以用户投入为基础的“相关”条款,那么你要么必须给每一条指定关键词,要么需要某种方式来审查某一条款并提取相关关键词。
有许多办法可以做你重新想做的事情。 你们选择什么取决于你的数据的性质、你是否重新合作过滤、你想花费多少时间加以开发,以及你希望结果如何好。
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