I would suggest looking into neural networks using unsupervised learning such as self organising maps. It's very difficult to use normal supervised neural networks to do what you want unless you can classify the data very precisely for learning. self organising maps don't have this problem because the network learns the classification groups all on their own.
First you have to decide what exactly you are recommending and under what circumstances. There are many things to take into account. Are you going to consider the "other users who bought X also bought Y?" Are you going to only recommend items that have a similar nature to each other? Are you recommending items that have a this-one-is-more-useful-with-that-one type of relationship?
I'm sure there are many more decisions, and each one of them has their own goals in mind. It would be very difficult to train one giant network to handle all of the above.
Neural networks all boil down to the same thing. You have a given set of inputs. You have a network topology. You have an activation function. You have weights on the nodes' inputs. You have outputs, and you have a means to measure and correct error. Each type of neural network might have its own way of doing each of those things, but they are present all the time (to my limited knowledge). Then, you train the network by feeding in a series of input sets that have known output results. You run this training set as much as you'd like without over or under training (which is as much your guess as it is the next guy's), and then you're ready to roll.
Essentially, your input set can be described as a certain set of qualities that you believe have relevance to the underlying function at hand (for instance: precipitation, humidity, temperature, illness, age, location, cost, skill, time of day, day of week, work status, and gender may all have an important role in deciding whether or not person will go golfing on a given day). You must therefore decide what exactly you are trying to recommend and under what conditions. Your network inputs can be boolean in nature (0.0 being false and 1.0 being true, for instance) or mapped in a pseudo-continuous space (where 0.0 may mean not at all, .45 means somewhat, .8 means likely, and 1.0 means yes). This second option may give you the tools to map confidence level for a certain input, or simple a math calculation you believe is relevant.
Hope this helped. You didn't give much to go on :)
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我建议使用无监督学习来研究神经网络，例如自组织映射。除非您可以非常精确地对数据进行分类以进行学习，否则很难使用普通的监督神经网络来做您想做的事情。自组织映射不存在这个问题，因为网络自己学习分类组。
看看这篇论文，它描述了一个音乐推荐系统
http://www.springerlink.com/content/xhcyn5rj35cvncvf/
以及更多有关的论文来自谷歌学者的主题
http://www.google. com.au/search?q=%09+A+自组织+地图+基于+知识+发现+for+音乐+推荐+系统+&ie=utf-8&oe=utf-8&aq=t& ;rls=com.ubuntu:en-US:official&client=firefox-a&safe=active
I would suggest looking into neural networks using unsupervised learning such as self organising maps. It's very difficult to use normal supervised neural networks to do what you want unless you can classify the data very precisely for learning. self organising maps don't have this problem because the network learns the classification groups all on their own.
have a look at this paper which describes a music recommendation system for music
http://www.springerlink.com/content/xhcyn5rj35cvncvf/
and many more papers written about the topic from google scholar
http://www.google.com.au/search?q=%09+A+Self-Organizing+Map+Based+Knowledge+Discovery+for+Music+Recommendation+Systems+&ie=utf-8&oe=utf-8&aq=t&rls=com.ubuntu:en-US:official&client=firefox-a&safe=active
首先，您必须决定您到底推荐什么以及在什么情况下推荐。有很多事情需要考虑。您是否会考虑“购买 X 的其他用户也购买了 Y”？您是否只会推荐性质相似的商品？您是否推荐具有“此一比彼一”关系的商品？
我确信还有更多的决定，每个决定都有自己的目标。训练一个庞大的网络来处理上述所有问题是非常困难的。
神经网络都归结为同一件事。您有一组给定的输入。您有一个网络拓扑。你有一个激活函数。您对节点的输入有权重。你有输出，并且有衡量和纠正错误的方法。每种类型的神经网络可能都有自己的方式来完成这些事情，但它们始终存在（据我有限的知识）。然后，通过输入一系列具有已知输出结果的输入集来训练网络。您可以按照自己的意愿运行此训练集，而无需过度或训练不足（这与您的猜测和下一个人的猜测一样多），然后您就可以开始了。
本质上，您的输入集可以描述为您认为与手头的基本功能相关的一组特定品质（例如：降水量、湿度、温度、疾病、年龄、位置、成本、技能、一天中的时间、一天中的时间）周数、工作状态和性别都可能在决定一个人是否会在某一天去打高尔夫球方面发挥重要作用）。因此，您必须决定您到底想推荐什么以及在什么条件下推荐。您的网络输入本质上可以是布尔值（例如，0.0 为假，1.0 为真），也可以映射到伪连续空间（其中 0.0 可能意味着根本不存在，0.45 意味着有些，0.8 意味着可能，1.0 意味着是的）。第二个选项可能为您提供工具来映射特定输入的置信度，或者简单地进行您认为相关的数学计算。
希望这有帮助。你没有付出太多继续下去:)
First you have to decide what exactly you are recommending and under what circumstances. There are many things to take into account. Are you going to consider the "other users who bought X also bought Y?" Are you going to only recommend items that have a similar nature to each other? Are you recommending items that have a this-one-is-more-useful-with-that-one type of relationship?
I'm sure there are many more decisions, and each one of them has their own goals in mind. It would be very difficult to train one giant network to handle all of the above.
Neural networks all boil down to the same thing. You have a given set of inputs. You have a network topology. You have an activation function. You have weights on the nodes' inputs. You have outputs, and you have a means to measure and correct error. Each type of neural network might have its own way of doing each of those things, but they are present all the time (to my limited knowledge). Then, you train the network by feeding in a series of input sets that have known output results. You run this training set as much as you'd like without over or under training (which is as much your guess as it is the next guy's), and then you're ready to roll.
Essentially, your input set can be described as a certain set of qualities that you believe have relevance to the underlying function at hand (for instance: precipitation, humidity, temperature, illness, age, location, cost, skill, time of day, day of week, work status, and gender may all have an important role in deciding whether or not person will go golfing on a given day). You must therefore decide what exactly you are trying to recommend and under what conditions. Your network inputs can be boolean in nature (0.0 being false and 1.0 being true, for instance) or mapped in a pseudo-continuous space (where 0.0 may mean not at all, .45 means somewhat, .8 means likely, and 1.0 means yes). This second option may give you the tools to map confidence level for a certain input, or simple a math calculation you believe is relevant.
Hope this helped. You didn't give much to go on :)