background - codemobile · background 5 supla audio podcast application for finland’s largest...

BACKGROUND

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18 apps and counting

REAKTOR OCTOBER 2015 — CONFIDENTIAL

BACKGROUND

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SuplaAudio podcast application for Finland’s largest commercial radio broadcasting company producing both original content and on-demand episodes of radio programming.

Awarded Best Digital Implementation two years in a row.

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BACKGROUND

SEARCH

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INGREDIENTS OF SEARCH

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Ingredients for “Good” Search UX

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ContentUI Index

GOOD SEARCH UX

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UI

yours truly

I type some words andthe app gives me relevant content despite minor spelling differences, sorted in a way that makes sense.

GOOD SEARCH UX

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Search UI Do’s and Don’ts


GOOD SEARCH UX

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• Don’t block the UI thread with searches.


GOOD SEARCH UX

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• Don’t block the database with updates.


GOOD SEARCH UX

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• Don’t block new searches with old ones.


GOOD SEARCH UX

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• Don’t delay showing results any longer than you must.


GOOD SEARCH UX

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• Indicate activity while a search is ongoing.


GOOD SEARCH UX

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• Indicate activity while a search is ongoing.

• Differentiate “no matches” from “didn’t do anything”.


GOOD SEARCH UX

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Ranking Makes a Good Search Great


GOOD SEARCH UX

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Ranking Makes a Good Search Great• Number of

occurrences


GOOD SEARCH UX

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occurrences• Length of

match vs length of text


GOOD SEARCH UX

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• Starting position of first match


GOOD SEARCH UX

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• Full word match vs prefix match


GOOD SEARCH UX

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• Match in title vs match in body


GOOD SEARCH UX

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• Date of matching document


GOOD SEARCH UX

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• Popularity amongst other users


GOOD SEARCH UX

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• Popularity amongst other users

• Behavioural conditioning


GOOD SEARCH UX

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Don’t make me scrollThe goal is to present relevant matches in a sensible order, minimising the need to scroll down the search results.

You know more about your data than Google does about theirs. Use that knowledge to your advantage.

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ContentUI Index

INDEX

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Ingredients for “Good” local Search UX


Index

Why not use[insert database product here]?

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SQLite FTS tables

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SQLITE FTS

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SQLite FTS4 tables: Indexing documents// create an FTS table for the index CREATE VIRTUAL TABLE conferences USING fts4(name, tagline, tokenize=icu fi_FI);

// add a document to the index INSERT INTO conferences (docid, name, tagline) VALUES(42, 'CodeMobile', 'Pure awesomeness');

// optimise the index when the app is idle INSERT INTO conferences(conferences) VALUES('optimize');

REAKTOR OCTOBER 2015 — CONFIDENTIAL

SQLITE FTS

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SQLite FTS4 tables: Searching Documents -- search across all columns, order by "matchinfo"01 SELECT * FROM conferences WHERE conferences 02 MATCH 'ios* OR android* OR mobile*' 03 ORDER BY matchinfo(conferences) DESC;

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SQLITE FTS

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SQLite FTS4 tables: Searching documents

02 JOIN (03 SELECT docid,04 rank(matchinfo(conferences,'pcxnal'), 10, 1) AS rank05 -- 'rank' is a custom SQL function06 FROM conferences WHERE conferences MATCH 'ios* OR android*'07 ORDER BY rank DESC LIMIT 1000 OFFSET 009 ) AS ranktable USING(docid)1011 ORDER BY ranktable.rank DESC

01 SELECT name, docid FROM conferences 02 03 04 05 06 07 09 10 WHERE conferences MATCH 'ios* OR android*'

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CLucenepod ‘BRFullTextSearch’


CLUCENE + BRFULLTEXTSEARCH

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CLucene + BRFullTextSearch: Indexing documents#import <BRFullTextSearch/BRFullTextSearch.h> #import <BRFullTextSearch/CLuceneSearchService.h> …

let lucene = CLuceneSearchService(indexPath: "MyIndex.index") …

let fields = [ "t": "CodeMobile 2017, April 18-20", "v": "Fantastic conference in Chester, UK" ] let doc = BRSimpleIndexable(identifier: "doc1", data: fields) lucene.addObjectToIndex(doc, queue: nil, finished: nil)

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CLucene + BRFullTextSearch: Searchinglet query = "t:(surf* OR board*) OR v:(surf* OR board*)"

let boostedQuery = "t:(\"surf*\") OR v:(\"board*\"^10)"

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CLucene + BRFullTextSearch: Searchinglet query = "t:(surf* OR board*) OR v:(surf* OR board*)"

let boostedQuery = "t:(\"surf*\") OR v:(\"board*\"^10)"

let results = lucene.search(query)

results.iterateWithBlock({ (i, result, stop) in print("Match: \(result.identifier): \(result.dictionaryRepresentation())") })

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Core Spotlightimport CoreSpotlight


CORE SPOTLIGHT

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Core Spotlight: Indexing documentslet type = kUTTypeAudio as String // Tells iOS the type of content let attributeSet = CSSearchableItemAttributeSet(itemContentType: type) attributeSet.kind = "..." // A name for the content type attributeSet.title = "..." // This is the indexed title attributeSet.displayName = "..." // This is the displayed title attributeSet.textContent = "..." // This is the indexed description attributeSet.contentDescription = "..." // This is the displayed descriptionattributeSet.thumbnailURL = "..." // The image displayed in search

let item = CSSearchableItem(uniqueIdentifier: "video1", domainIdentifier: "videos", attributeSet: attributeSet) item.expirationDate = Date().addingTimeInterval(60 * 60 * 24 * 15)

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CORE SPOTLIGHT

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Core Spotlight: Indexing documentslet index = CSSearchableIndex.default() index.indexSearchableItems( [ item1, item2, item3 ] )

index.deleteAllSearchableItems()

index.deleteSearchableItems(withIdentifiers: [“video1"])

index.deleteSearchableItems(withDomainIdentifiers: ["media.videos.cats"])

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CORE SPOTLIGHT

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Core Spotlight: reacting to Spotlight searchfunc application(application: UIApplication, continueUserActivity userActivity: NSUserActivity, restorationHandler: @escaping ([Any]?) -> Void) -> Bool { if let userInfo = userActivity.userInfo, let id = userInfo[CSSearchableItemActivityIdentifier] as? String { // id now contains "video1" which we can lookup from our repository // and launch in the UI handleLaunchFromCoreSpotlight(id) return true } return false }

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ContentUI Index


Content

COMPOUNDS


surf

board

surfboard

boa

boar

COMPOUNDS


01 def split(word, dictionary):02 splits, min_length = [], 303 if len(word) >= (min_length * 2):04 for i in range(min_length, len(word) - min_length):05 head, tail = word[:i], word[i:]06 if (head in dictionary):07 if (tail in dictionary):08 splits.append(head + " " + tail)09 splits.extend(split(tail, dictionary))10 return splits

COMPOUNDS


func split(_ word: String, dict: SplittingDictionary, seed: String? = nil) -> [String] { let minimumWordLength = 2 var splits = [String]() guard word.characters.count >= (2*minimumWordLength) else { return splits } (minimumWordLength...(word.characters.count-minimumWordLength)).forEach { offset in let head = word.substring(to: word.index(word.startIndex, offsetBy: offset)) let isPrefix = dict.contains(prefix: head) if dict.contains(word: head) || isPrefix { let tail = word.substring(from: word.index(word.startIndex, offsetBy: offset)) if dict.contains(word: tail) { let inclHead: String? = isPrefix ? nil : head splits.append([seed, inclHead, tail].flatMap { $0 }.joined(separator: " ")) } let tailDict = dict.withoutPrefixes() let tailSeed = [seed, head].flatMap { $0 }.joined(separator: " ") splits.append(contentsOf: split(tail, using: tailDict, seed: tailSeed)) } } return splits}

COMPOUNDS


windsurfing " w + indsurfingwindsurfing " wi + ndsurfingwindsurfing " win + dsurfing ✗ only HEAD matcheswindsurfing " wind + surfing ✓ HEAD and TAIL match!windsurfing " winds + urfing ✗ only HEAD matcheswindsurfing " windsu + rfingwindsurfing " windsur + fingwindsurfing " windsurf + ing ✗ only HEAD matcheswindsurfing " windsurfi + ngwindsurfing " windsurfin + g

windsurfing ~ windsurfbunnies ~ bunniThe Snowball Project - http://showballstem.org

STEMMING

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Stemming reduces a word to its base (a stem)

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http://showballstem.org

STEMMING

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Stemming reduces a word to its base (a stem)#import "libstemmer.h" // http://showballstem.org @implementation SnowballStemmer - (NSString *)stem:(NSString *)word { struct sb_stemmer * stemmer = sb_stemmer_new("en", "UTF_8"); const char * originalWordCString = [word UTF8String]; unsigned long originalLength = strlen(originalWordCString); const sb_symbol * stemmedWord = sb_stemmer_stem(stemmer, (const sb_symbol *)originalWordCString, (int) originalLength); return [NSString stringWithUTF8String:(const char *)stemmedWord]; } @end

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windsurfing ~ windsurfingbunnies ~ bunny

LEMMATISATION

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lemmatisation turns a word to its basic form(a lemma)

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LEMMATISATION

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lemmatisation turns a word to its basic form(a lemma)

// pod 'Parsimmon' - wraps NSLinguisticTaggerimport Parsimmon let phrase = "I'm eating chocolate bunnies." let stems = Lemmatizer().lemmatizeWordsInText(phrase) print(stems) // => ["I", "eat", "chocolate", "bunny"]

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I type some words andthe app gives me relevant contentdespite minor spelling differences,sorted in a way that makes sense.

[email protected]

lassekoskela

+ [email protected]