Algorithm for autocomplete?

I am referring to the algorithm that is used to give query suggestions when a user types a search 开发者_如何学编程term in Google.

I am mainly interested in: 1. Most important results (most likely queries rather than anything that matches) 2. Match substrings 3. Fuzzy matches

I know you could use Trie or generalized trie to find matches, but it wouldn't meet the above requirements...

Similar questions asked earlier here

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For (heh) awesome fuzzy/partial string matching algorithms, check out Damn Cool Algorithms:

• http://blog.notdot.net/2007/4/Damn-Cool-Algorithms-Part-1-BK-Trees
• http://blog.notdot.net/2010/07/Damn-Cool-Algorithms-Levenshtein-Automata

These don't replace tries, but rather prevent brute-force lookups in tries - which is still a huge win. Next, you probably want a way to bound the size of the trie:

• keep a trie of recent/top N words used globally;
• for each user, keep a trie of recent/top N words for that user.

Finally, you want to prevent lookups whenever possible...

• cache lookup results: if the user clicks through on any search results, you can serve those very quickly and then asynchronously fetch the full partial/fuzzy lookup.
• precompute lookup results: if the user has typed "appl", they are likely to continue with "apple", "apply".
• prefetch data: for instance, a web app can send a smaller set of results to the browser, small enough to make brute-force searching in JS viable.

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I'd just like to say... A good solution to this problem is going to incorporate more than a Ternary Search Tree. Ngrams, and Shingles (Phrases) are needed. Word-boundary errors also need to be detected. "hell o" should be "hello" ... and "whitesocks" should be "white socks" - these are pre-processing steps. If you don't preprocess the data properly you aren't going to get valuable search results. Ternary search trees are a useful component in figuring out what is a word, and also for implementing related-word guessing when a word typed isn't a valid word in the index.

The google algorithm performs phrase suggestion and correction. The google algorithm also has some concept of context... if the first word you search for is weather related and you combine them "weatherforcst" vs "monsoonfrcst" vs "deskfrcst" - my guess is behind the scenes rankings are being changed in the suggestion based on the first word encountered - forecast and weather are related words therefore forecast get's a high rank in the Did-You-Mean guess.

word-partials (ngrams), phrase-terms (shingles), word-proximity (word-clustering-index), ternary-search-tree (word lookup).

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Google's exact algorithm is unknown, but it is said to work by statistical analysis of users input. An approach not suitable for most cases. More commonly auto completion is implemented using one of the following:

• Trees. By indexing the searchable text in a tree structure (prefix tree, suffix tree, dawg, etc..) one can execute very fast searches at the expense of memory storage. The tree traversal can be adapted for approximate matching.
• Pattern Partitioning. By partitioning the text into tokens (ngrams) one can execute searches for pattern occurrences using a simple hashing scheme.
• Filtering. Find a set of potential matches and then apply a sequential algorithm to check each candidate.

Take a look at completely, a Java autocomplete library that implements some of the latter concepts.

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There are tools like soundex and levenshtein distance that can be used to find fuzzy matches that are within a certain range.

Soundex finds words that sound similar and levenshtein distance finds words that are within a certain edit distance from another word.

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Take a look at Firefox's Awesome bar algorithm

Google suggest is useful, because it take the millions of popular queries + your past related queries into account.

It doesn't have a good completion algorithm / UI though:

1. Doesn't do substrings
2. Seems like a relatively simple word-boundary prefix algorithm.
   For example: Try tomcat tut --> correctly suggest "tomcat tutorial". Now try tomcat rial --> no suggestions )-:
3. Doesn't support "did you mean?" - as in google search results.

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For substrings and fuzzy matches, the Levenshtein distance algorithm has worked fairly well for me. Though I will admit it does not seem to be as perfect as industry implementations of autocomplete/suggest. Both Google and Microsoft's Intellisense do a better job, I think because they've refined this basic algorithm to weigh the kind of edit operations it takes to match the dissimilar strings. E.g. transposing two characters should probably only count as 1 operation, not 2 (an insert & delete).

But even so I find this is close enough. Here is it's implementation in C#...

// This is the traditional Levenshtein Distance algorithem, though I've tweaked it to make
// it more like Google's autocomplete/suggest.  It returns the number of operations 
// (insert/delete/substitute) required to change one string into another, with the 
// expectation that userTyped is only a partial version of fullEntry.
// Gives us a measurement of how similar the two strings are.
public static int EditDistance(string userTyped, string fullEntry)
{
    if (userTyped.Length == 0) // all entries are assumed to be fully legit possibilities 
        return 0; // at this point, because the user hasn't typed anything.

    var inx = fullEntry.IndexOf(userTyped[0]);
    if (inx < 0) // If the 1st character doesn't exist anywhere in the entry, it's not
        return Int32.MaxValue; // a possible match.

    var lastInx = inx;
    var lastMatchCount = 0;
TryAgain:
    // Is there a better starting point?
    var len = fullEntry.Length - inx;
    var matchCount = 1;
    var k = 1;
    for (; k < len; k++)
    {
        if (k == userTyped.Length || userTyped[k] != fullEntry[k + inx])
        {
            if (matchCount > lastMatchCount)
            {
                lastMatchCount = matchCount;
                lastInx = inx;
            }
            inx = fullEntry.IndexOf(userTyped[0], inx + 1);
            matchCount = 0;
            if (inx > 0)
                goto TryAgain;
            else
                break;
        }
        else
            matchCount++;
    }
    if (k == len && matchCount > lastMatchCount)
        lastInx = inx;

    if (lastInx > 0)
        fullEntry = fullEntry.Substring(lastInx); // Jump to 1st character match, ignoring previous values 

    // The start of the Levenshtein Distance algorithem.
    var m = userTyped.Length;
    var n = Math.Min(m, fullEntry.Length);

    int[,] d = new int[m + 1, n + 1]; // "distance" - meaning number of operations.

    for (var i = 0; i <= m; i++)
        d[i, 0] = i; // the distance of any first string to an empty second string
    for (var j = 0; j <= n; j++)
        d[0, j] = j; // the distance of any second string to an empty first string

    for (var j = 1; j <= n; j++)
        for (var i = 1; i <= m; i++)
            if (userTyped[i - 1] == fullEntry[j - 1])
                d[i, j] = d[i - 1, j - 1];       // no operation required
            else
                d[i, j] = Math.Min
                           (
                             d[i - 1, j] + 1,  // a deletion
                             Math.Min(
                             d[i, j - 1] + 1,  // an insertion
                             d[i - 1, j - 1] + 1 // a substitution
                             )
                           );

    return d[m, n];
}

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If you are looking for an overall design for the problem, try reading the content at https://www.interviewbit.com/problems/search-typeahead/.

They start by building autocomplete through a naive approach of using a trie and then build upon it. They also explain optimization techniques like sampling and offline updates to cater to specific use cases.

To keep the solution scalable, you would have to shard your trie data intelligently.

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I think that one might be better off constructing a specialized trie, rather than pursuing a completely different data structure.

I could see that functionality manifested in a trie in which each leaf had a field that reflected the frequency of searches of its corresponding word.

The search query method would display the descendant leaf nodes with the largest values calculated from multiplying the distance to each descendant leaf node by the search frequency associated with each descendant leaf node.

The data structure (and consequently the algorithm) Google uses are probably vastly more complicated, potentially taking into a large number of other factors, such as search frequencies from your own specific account (and time of day... and weather... season... and lunar phase... and... ). However, I believe that the basic trie data structure can be expanded to any kind of specialized search preference by including additional fields to each of the nodes and using those fields in the search query method.

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I don't know if this will answer your question but I made a very simple input-autocomplete code using C language back then. I haven't implemented machine learning and neural networks on this so it won't make probability calculations and whatnot. What it does is check the very first index that matches the input using sub-string checking algorithm.

You can provide match data into a "dict.txt" file.

/* Auto-complete input function in c
    @authors: James Vausch
    @date: 2018-5-23

    - This is a bona-fide self-created program which aims to
      stimulate an input auto-suggest or auto-complete function
      in C language. This is open source so you can use the code
      freely. However if you will use this, just acknowledge the
      creator as a sign of respect.
    - I'd also like to acknowledge Code with C team whom where I
      I got an answer how to have a colored output instead of 
      using system("color #"). Link down below

      https://www.codewithc.com/change-text-color-in-codeblocks-console-window/


    - THE GENERAL IDEA IS; WE READ A FILE WITH DICTIONARY WORDS
      OR SHALL WE SAY ALL WORDS. WE RUN A WHILE LOOP THAT WILL
      GET CHARACTER FROM THE USER USING "getch()" FUNCTION THEN
      STORE IT IN A CHARACTER ARRAY THEN IS PASSED ON A FUNCTION
      THAT CHECKS IF THE ANY DICTIONARY WORDS HAS A SUBSTRING
      THAT IS THE USER INPUT. IF YES(0), THE FUNCTION WILL COPY
      THE FOLLOWING STRING FROM THE DICTIONARY ARRAY THEN STORED
      IN A TEMP CHAR ARRAY AND PROCESSED. THE PROCESSING SHOULD
      BE SIMPLE. WE RUN A LOOP IN WHICH WILL CHECK THE AMOUNT OF
      CHARACTERS IN THE MATCHED STRING, THEN WE'LL RUN A LOOP
      THAT WILL SORT THE WORDS DECREMENTALLY BASED ON THE AMOUNT
      OF CHARACTERS OF THE INPUT SUBSTRING. THEN PRINT THE
      PROCESSED STRING ON THE FRONT OF THE INPUT STRING THEN RUN
      A LOOP BASED ON THE AMOUNT OF CHARACTERS PRESENT OR STRING
      LENGTH OF THE PROCESSED STRING PLUS 10 EXTRA CHARACTERS 
      ALONG WITH PRINTING SOME BACKWARD TRAVERSE CARET FUNCTION 
      TO MAKE THE CARET STAY WHERE IT SHOULD BE ALONG WITH 
      INPUTTING. SIMPLE.

    - <EXAMPLE>
        INPUT: COM
        AFTER LOOP RUN: MATCHED WITH WORD "COMMAND"
        AFTER LOOP RUN: INPUT HAS 3 CHARACTERS
        LOOP SEQUENCE:
            LOOP 0: OMMAND
            LOOP 1: MMAND
            LOOP 2: MAND
        AFTER LOOP: MAND
        PRINT: "MAND" AFTER INPUT BUT KEEP CARET ON THE INPUT "COM"

    NOTE:
    - You'll need the "dict.txt" file or you can create one and
      put some stuff there
    - Since C Programs run on.. say a terminal, I have not much of a way to
      efficiently make a way to use arrow keys for the job.
    - you should type your INPUT in LOWERCASE since pressing "Shift_Key + M"
      is equivalent to pressing the VK_Right(right arrow key) as well as
      the other arrow keys
    - the right arrow key has an ascii equivalent of <-32><77>, 77 = M
    - to complete the input, you'll need to press right arrow key
    - the left arrow key has an ascii equivalent of <-32><75>, 75 = K
    - to remove auto-complete suggestion, press left arrow key

    TO ADD:
    - UP arrow key and DOWN arrow key to cycle through suggestions
*/

//#include <headers.h> //My personal header file
#include <stdio.h>
#include <stdlib.h>
#include <conio.h>
#include <windows.h>

void main(){
    SetColor(6);
    start();
}

void start(){
    int rep = 0;
    char dictFile[] = "dict.txt";
    loadDictionaryEntries(dictFile);

    char inp[50];
    printf("\nAuto Complete Program : C");
    while(rep == 0){
        printf("\nInput: ");
        autoCompleteInput(inp);
        if(strcasecmp(inp, "exit") == 0){
            break;
        }
        printf("\nOutput: %s", inp);
    }
    printf("\n");
    system("pause");
}

int dictEntryCount = 0;
struct allWords{
    char entry[100];
}dictionary[60000];

//============================================================================//
void loadDictionaryEntries(char directory[]){
    FILE *file;
    int dex = 0;
    char str[100];
    if(file = fopen(directory, "r")){
        printf("File accessed.\n");
        while(!feof(file)){
            fscanf(file, "%s", str);
            //UN-COMMENT line 109 to check if the program is reading from file
            //printf("Adding entry %d: \"%s\" to dictionary\n",dex + 1, str);
            strcpy(dictionary[dex].entry, str);
            dex++;
            dictEntryCount++;
        }
        fclose(file);
        printf("[ADDED %d WORDS TO DICTIONARY]\n", dictEntryCount);
    }else{
        printf(" File cannot be accessed.");
        fclose(file);
    }
}

void printArray(){
    for(int i = 0; i < dictEntryCount; i++){
        printf("Index %d: %s\n", i + 1, dictionary[i].entry);
    }
}

//============================================================================//
void autoCompleteInput(char input[]){
    char matchedWord[100]; //STORAGE FOR THE WORD THAT MATCHES INPUT
    char ch; //STORAGE FOR EACH CHARACTER THAT THE USER INPUTS
    int i = 0; //COUNTER
    int words;
    while(i != 200){ //LOOP TO GET EACH CHARACTER FROM KEYBOARD PRESS
        SetColor(6);
        ch = getch();
        clsx(strlen(matchedWord));
        if(ch == 13){ //CONDITION TO CHECK IF INPUT IS "ENTER" KEY
            break; //BREAKS LOOP IF "ENTER IS PRESSED"
        }else if(ch == 8){ //CONDITION TO CHECK IF INPUT IS "BACKSPACE"
            if(i == 0){ //IF INPUT IS NULL, DO NOTHING, DONT ERASE ANYTHING
                //DO NOTHING
            }else{ //IF INPUT IS NOT NULL, ENABLE ERASING
                clsx(strlen(matchedWord));
                bksp();
                i--;
                input[i] = '\0';
                if(i > 2){
                    if(matchToDictionary(input, matchedWord) == 0){
                        words = 0;
                        processMatchedWord(i, matchedWord);
                        SetColor(8);
                        printf("%s", matchedWord);
                        words = getArrSizeChar(matchedWord);
                        for(int x = 0; x < words; x++){
                            printf("\b");
                        }
                    }
                }
            }
        }else if(ch == 77){ //CONDITION TO CHECK IF INPUT IS RIGHT ARROW KEY
            printf("%s", matchedWord); //PRINT SUGESTED WORD WITH CARET AT FRONT
            strcat(input, matchedWord); //CONCATENATE SUGGESTION TO INPUT
            i = i + words - 1; //SETS INDEX AT THE END OF INPUT
            words = 0; //
        }else if(ch == 75){ //CONDITION TO CHECK IS INPUT IS LEFT ARROW KEY
            clsx(strlen(matchedWord)); //ERASE SUGGESTION
            i--; //DECREMENT INDEX
        }else{ //IF CONDITIONS ABOVE ARE NOT MET, DO THIS
            input[i] = ch; //INSERT CH AT THE INDEX OF INPUT
            printf("%c", ch); //PRINT CHARACTER
            input[i + 1] = '\0'; //SET END OF CURRENT INPUT TO NULL
            i++;
            if(i >= 2){
                if(matchToDictionary(input, matchedWord) == 0){
                    words = 0;
                    processMatchedWord(i, matchedWord);
                    SetColor(8);
                    printf("%s", matchedWord);
                    words = getArrSizeChar(matchedWord);
                    for(int x = 0; x < words; x++){
                        printf("\b");
                    }
                }else{
                    clsx(strlen(matchedWord));
                }
            }
        }

    }
    input[i] = '\0'; //NULL ENDING VALUE TO PREVENT UNNECESSARY CHARACTERS
}

int getArrSizeChar(char array[]){
    int size = 0;
    while(array[size] != '\0'){size++;}
    return size;
}

void clsx(int maxVal){
    for(int i = 0; i < maxVal + 10; i++){
        printf(" ");
    }
    for(int i = 0; i < maxVal + 10; i++){
        printf("\b");
    }
}

int matchToDictionary(char input[], char matchedWord[]){
    int found = 0;
    int dex = dictEntryCount; //LIMIT OF ARRAY / ARRAY BOUND/S
    //while(dictionary[dex] != '\0'){ //LOOP TO DETERMINE ARRAY BOUND
        //printf("%d", dex);
        //dex++; //INCREMENT IF INDEX OF ARRAY IS NOT NULL
    //}
    //printf("%d", dex);
    for(int i = 0; i < dex; i++){ //LOOP TROUGH ALL INDEXES OF DICTIONARY
        //CHECKS IF THE INDEX OF DICTIONARY HAS A SUBSTRING INPUT
        //printf(" Matching %s and %s\n", dictionary[i], input);
        if(containsIgnoreCase(dictionary[i].entry, input) == 0){
            //CHECKS IF THE INDEX OF DICTIONARY TOTALLY MATCHES THE INPUT
            //IT IS TO PREVENT ERRORS IN AUTO-COMPLETING PROCESS
            if(strcasecmp(dictionary[i].entry, input) == 1){
                //IF NOT, STORE INDEX OF DICTIONARY TO MATCHED WORD
                strcpy(matchedWord, dictionary[i].entry);
                found++;
                break; //BREAK LOOP
            }
        }
    }

    if(found == 1){
        return 0;
    }else{
        return 1;
    }
}

void processMatchedWord(int rep, char str[]){
    int lim = 0;
    int i;
    char temp[50];
    while(str[lim] != '\0'){
        lim++;
    }

    while(rep != 0){
        for(i = 0; i < lim; i++){
            str[i] = str[i + 1];
        }
        rep--;
    }
}

//===================================================================//
void bksp(){
    printf("\b "); //first backsapce to print an emtpy character
    printf("\b"); //second backspace to erase printed character
}

int containsIgnoreCase(char str1[], char str2[]){
    char tmp1[100];
    char tmp2[100];
    toLowerCase(tmp1, str1);
    toLowerCase(tmp2, str2);
    int i, j = 0, k;

    for(i = 0; tmp1[i]; i++){
        if(tmp1[i] == tmp2[j]){
            for(k = i, j = 0; tmp1[k] && tmp2[j]; j++, k++){
                if(tmp1[k] != tmp2[j]){
                    break;
                }
            }
            if(!tmp2[j]){
                return 0;
            }
        }
    }

    return 1;
}

void toLowerCase(char destination[], char source[]){
    int lim = 0;
    int i;

    while(source[lim] != '\0'){
        lim++;
    }

    for(i = 0; i < lim; i++){
        destination[i] = tolower(source[i]);
    }
    destination[i] = '\0';
}


/*Console Colors: Windows

    0 = Black        8 = Gray
    1 = Blue         9 = LBlue
    2 = Green       10 = LGreen
    3 = Aqua        11 = LAqua
    4 = Red         12 = LRed
    5 = Purple      13 = LPurple
    6 = Yellow      14 = LYellow
    7 = White       15 = Bright White
}*/

void SetColor(int ForgC){ //CODE SNIPPET FROM WWW.CODEWITHC.COM
    WORD wColor;
    //This handle is needed to get the current background attribute

    HANDLE hStdOut = GetStdHandle(STD_OUTPUT_HANDLE);
    CONSOLE_SCREEN_BUFFER_INFO csbi;
    //csbi is used for wAttributes word

    if(GetConsoleScreenBufferInfo(hStdOut, &csbi)){
        //To mask out all but the background attribute, and to add the color
        wColor = (csbi.wAttributes & 0xF0) + (ForgC & 0x0F);
        SetConsoleTextAttribute(hStdOut, wColor);
    }
    return;
}

If you're referring to a program that has a null initialized matches then saves an input from user into an array or file then when the user types the same word the program matches it to a previous input, maybe I can work into that.