#### Topic: Simple recommendatory system

I welcome. At once I speak - a subject I understand superficially. I.e. a principle of operation and any titles of algorithms I know, but is deeper, in mathematics, did not plunge. Therefore I ask to answer too, "on fingers". In general the idea of implementation of simple recommendatory system came to me for a long time. 1. The user receives the message. The message consists of words. All words are brought in basis if they in it missed. Except for the unions, pretexts, digits, etc. 2. The user estimates the message - "plus" or "minus": 2.1. If "plus" to each word of the message in basis the rating raises. 2.2. If "minus", on the contrary: to each word of the message in basis the rating is reduced. 3. The system receives the following message and on the basis of words of this message and a rating of these words from basis, forms a message rating. 3.1. We admit, the message rating is formed by stupid addition. Then, if the rating is more than zero the message is shown to the user if it is less is not present. 3.2. Let's admit, the message rating too is formed by stupid addition. All unread messages are built linearly depending on a rating: from high to the low. In this case the user can adjust "" messages. Is better should work on news sites. For the sake of experiment it would be possible and to implement, but laziness. How think, it will work?

#### Re: Simple recommendatory system

Hello, Real 3L0, you wrote: R3> 1. The user receives the message. The message consists of words. All words are brought in basis if they in it missed. Except for the unions, pretexts, digits, etc. R3> 2. The user estimates the message - "plus" or "minus": R3> 2.1. If "plus" to each word of the message in basis the rating raises. R3> 2.2. If "minus", on the contrary: to each word of the message in basis the rating is reduced. Nich-that I do not understand! () In sense, than is longer the message (more precisely, the in it more than significant words), the above or more low there can be a rating of all message? That is it is supposed to calculate or write off a rating for each word? If yes, what for so to do? After all there are cases when the short message is more useful and more readable than the long. And here suddenly it turns out that the rating for the short message certainly cannot be above, than for long...

#### Re: Simple recommendatory system

Hello, Real 3L0, you wrote: I Am afraid such simple algorithm will not guess though somehow well. Words can be same, but, as they say, with nuance... ps: here that you want to implement actually - https://en.wikipedia.org/wiki/Bag-of-words_model

#### Re: Simple recommendatory system

Hello, ZevS, you wrote: ZS> I Am afraid such simple algorithm will not guess though somehow well. Words can be same, but, as they say, with nuance... With the same success it is possible to compare human transfer to the machine. I think, coincidence of 90 % of significant words generally is possible.

#### Re: Simple recommendatory system

Hello, Lazytech, you wrote: L> In sense, than is longer the message (more precisely, the in it more than significant words), the above or more low there can be a rating of all message? Yes. But there are two variants. 1. Quite probably that if this long message the keyword will meet more often, therefore as a whole it does not affect a rating of the message. 2. It is possible to add any normalizing coefficient in algorithm of a rating. L> that is it is supposed to calculate or write off a rating for each word? Yes. L> If yes, what for so to do? After all there are cases when the short message is more useful and more readable than the long. And here suddenly it turns out that the rating for the short message certainly cannot be above, than for long... As I wrote, a basis are news sites. On them such normally does not happen. And the most important thing: it not a usefulness estimation. It is an estimation .

#### Re: Simple recommendatory system

R3> As think, it will work? I think that for such purpose it will be better to work the simplified variant: 1. The pool of the read messages will be clustered under the text (with selection of roots of words and the registration of word combinations), k-means or hierarchically 2. Depending on estimations each cluster is estimated on . 3. For each new message its cluster and  is defined. 4. After an estimation of the new message passage in point 1.

#### Re: Simple recommendatory system

Hello, Real 3L0, you wrote: R3> As think, it will work? As a whole, you described a principle of operation of that is called Content Based Filtering. But described superficially. Therefore to work as a whole will be, but it is possible is much better. It will be better to work, if words in the message are not stupid for pushing in basis, and to "weigh", for example, by means of TF-IDF metrics. A word, with a bicycle, you, the barin!

#### Re: Simple recommendatory system

Hello, De-Bill, you wrote: DB> 1. The pool of the read messages will be clustered under the text (with selection of roots of words and the registration of word combinations), k-means or hierarchically Clustering implies small division, for example, on "Windows setting" and "Windows reinstallation"? One can be interesting, another - is not present.

#### Re: Simple recommendatory system

R3> For the sake of experiment it would be possible and to implement, but laziness. If, how it was clarified, the algorithm is known, can eat implementation examples? And examples of the given algorithm, without the registration of estimations of other users.

#### Re: Simple recommendatory system

R3> Clustering implies small division, for example, on "Windows setting" and "Windows reinstallation"? One can be interesting, another - is not present. Depends how you define roots. If to cut only the terminations setting and reinstallation will be different words.

#### Re: Simple recommendatory system

R3> If how it was clarified, the algorithm is known, can eat implementation examples? And examples of the given algorithm, without the registration of estimations of other users. TfidfVectorizer in sklearn.feature_extraction.text

#### Re: Simple recommendatory system

Hello, De-Bill, you wrote: R3>> If how it was clarified, the algorithm is known, can eat implementation examples? And examples of the given algorithm, without the registration of estimations of other users. DB> TfidfVectorizer in sklearn.feature_extraction.text In sense, for the user, instead of the programmer?

#### Re: Simple recommendatory system

Hello, Real 3L0, you wrote:  forgot to fasten! Without  now does not fly up!... <<RSDN@Home 1.0.0 alpha 5 rev. 0>>