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1 // Copyright (c) 2012 The Chromium Authors. All rights reserved. | 1 // Copyright (c) 2012 The Chromium Authors. All rights reserved. |
2 // Use of this source code is governed by a BSD-style license that can be | 2 // Use of this source code is governed by a BSD-style license that can be |
3 // found in the LICENSE file. | 3 // found in the LICENSE file. |
4 | 4 |
5 #include "chrome/browser/autocomplete/scored_history_match.h" | 5 #include "chrome/browser/autocomplete/scored_history_match.h" |
6 | 6 |
7 #include <math.h> | |
8 | |
9 #include <algorithm> | |
10 #include <vector> | |
11 | |
12 #include "base/logging.h" | |
13 #include "base/metrics/histogram.h" | |
Mark P
2015/03/10 00:28:12
Why do you need this?
sdefresne
2015/03/10 10:43:28
Removed.
| |
14 #include "base/numerics/safe_conversions.h" | |
15 #include "base/strings/string_number_conversions.h" | |
16 #include "base/strings/string_split.h" | |
17 #include "base/strings/string_util.h" | |
Mark P
2015/03/10 00:28:12
Why do you need this?
sdefresne
2015/03/10 10:43:28
string_util.h is required for IsWhitespace.
utf_s
| |
18 #include "base/strings/utf_string_conversions.h" | |
19 #include "chrome/browser/autocomplete/history_url_provider.h" | |
20 #include "components/bookmarks/browser/bookmark_utils.h" | |
Mark P
2015/03/10 00:28:12
Why do you need this?
sdefresne
2015/03/10 10:43:28
bookmark_utils.h is needed for
bookmarks::CleanU
| |
21 #include "components/history/core/browser/history_client.h" | |
Mark P
2015/03/10 00:28:12
why do you need this?
In general, where did you g
sdefresne
2015/03/10 10:43:28
I copied them from chrome/browser/autocomplete/sco
| |
22 #include "components/omnibox/omnibox_field_trial.h" | |
23 #include "components/omnibox/url_prefix.h" | |
24 #include "content/public/browser/browser_thread.h" | |
25 | |
26 namespace { | |
27 | |
28 // The number of days of recency scores to precompute. | |
Mark P
2015/03/10 00:28:12
Did you modify any of this anonymous namespace stu
sdefresne
2015/03/10 10:43:28
No.
| |
29 const int kDaysToPrecomputeRecencyScoresFor = 366; | |
30 | |
31 // The number of raw term score buckets use; raw term scores greater this are | |
32 // capped at the score of the largest bucket. | |
33 const int kMaxRawTermScore = 30; | |
34 | |
35 // If true, assign raw scores to be max(whatever it normally would be, a score | |
36 // that's similar to the score HistoryURL provider would assign). This variable | |
37 // is set in the constructor by examining the field trial state. | |
38 const bool kAlsoDoHupLikeScoring = false; | |
39 | |
40 // Pre-computed information to speed up calculating recency scores. | |
41 // |days_ago_to_recency_score| is a simple array mapping how long ago a page was | |
42 // visited (in days) to the recency score we should assign it. This allows easy | |
43 // lookups of scores without requiring math. This is initialized by | |
44 // InitDaysAgoToRecencyScoreArray called by | |
45 // ScoredHistoryMatch::Init(). | |
46 float days_ago_to_recency_score[kDaysToPrecomputeRecencyScoresFor]; | |
47 | |
48 // Pre-computed information to speed up calculating topicality scores. | |
49 // |raw_term_score_to_topicality_score| is a simple array mapping how raw terms | |
50 // scores (a weighted sum of the number of hits for the term, weighted by how | |
51 // important the hit is: hostname, path, etc.) to the topicality score we should | |
52 // assign it. This allows easy lookups of scores without requiring math. This | |
53 // is initialized by InitRawTermScoreToTopicalityScoreArray() called from | |
54 // ScoredHistoryMatch::Init(). | |
55 float raw_term_score_to_topicality_score[kMaxRawTermScore]; | |
56 | |
57 // The maximum score that can be assigned to non-inlineable matches. This is | |
58 // useful because often we want inlineable matches to come first (even if they | |
59 // don't sometimes score as well as non-inlineable matches) because if a | |
60 // non-inlineable match comes first than all matches will get demoted later in | |
61 // HistoryQuickProvider to non-inlineable scores. Set to -1 to indicate no | |
62 // maximum score. | |
63 int max_assigned_score_for_non_inlineable_matches = -1; | |
64 | |
65 // Whether ScoredHistoryMatch::Init() has been called. | |
66 bool initialized = false; | |
67 | |
68 // Precalculates raw_term_score_to_topicality_score, used in | |
69 // GetTopicalityScore(). | |
70 void InitRawTermScoreToTopicalityScoreArray() { | |
71 for (int term_score = 0; term_score < kMaxRawTermScore; ++term_score) { | |
72 float topicality_score; | |
73 if (term_score < 10) { | |
74 // If the term scores less than 10 points (no full-credit hit, or | |
75 // no combination of hits that score that well), then the topicality | |
76 // score is linear in the term score. | |
77 topicality_score = 0.1 * term_score; | |
78 } else { | |
79 // For term scores of at least ten points, pass them through a log | |
80 // function so a score of 10 points gets a 1.0 (to meet up exactly | |
81 // with the linear component) and increases logarithmically until | |
82 // maxing out at 30 points, with computes to a score around 2.1. | |
83 topicality_score = (1.0 + 2.25 * log10(0.1 * term_score)); | |
84 } | |
85 raw_term_score_to_topicality_score[term_score] = topicality_score; | |
86 } | |
87 } | |
88 | |
89 // Pre-calculates days_ago_to_recency_score, used in GetRecencyScore(). | |
90 void InitDaysAgoToRecencyScoreArray() { | |
91 for (int days_ago = 0; days_ago < kDaysToPrecomputeRecencyScoresFor; | |
92 days_ago++) { | |
93 int unnormalized_recency_score; | |
94 if (days_ago <= 4) { | |
95 unnormalized_recency_score = 100; | |
96 } else if (days_ago <= 14) { | |
97 // Linearly extrapolate between 4 and 14 days so 14 days has a score | |
98 // of 70. | |
99 unnormalized_recency_score = 70 + (14 - days_ago) * (100 - 70) / (14 - 4); | |
100 } else if (days_ago <= 31) { | |
101 // Linearly extrapolate between 14 and 31 days so 31 days has a score | |
102 // of 50. | |
103 unnormalized_recency_score = 50 + (31 - days_ago) * (70 - 50) / (31 - 14); | |
104 } else if (days_ago <= 90) { | |
105 // Linearly extrapolate between 30 and 90 days so 90 days has a score | |
106 // of 30. | |
107 unnormalized_recency_score = 30 + (90 - days_ago) * (50 - 30) / (90 - 30); | |
108 } else { | |
109 // Linearly extrapolate between 90 and 365 days so 365 days has a score | |
110 // of 10. | |
111 unnormalized_recency_score = | |
112 10 + (365 - days_ago) * (20 - 10) / (365 - 90); | |
113 } | |
114 days_ago_to_recency_score[days_ago] = unnormalized_recency_score / 100.0; | |
115 if (days_ago > 0) { | |
116 DCHECK_LE(days_ago_to_recency_score[days_ago], | |
117 days_ago_to_recency_score[days_ago - 1]); | |
118 } | |
119 } | |
120 } | |
121 | |
122 } // namespace | |
123 | |
7 // static | 124 // static |
8 const size_t ScoredHistoryMatch::kMaxVisitsToScore = 10; | 125 const size_t ScoredHistoryMatch::kMaxVisitsToScore = 10; |
126 int ScoredHistoryMatch::bookmark_value_ = 1; | |
127 bool ScoredHistoryMatch::allow_tld_matches_ = false; | |
128 bool ScoredHistoryMatch::allow_scheme_matches_ = false; | |
129 bool ScoredHistoryMatch::hqp_experimental_scoring_enabled_ = false; | |
130 float ScoredHistoryMatch::topicality_threshold_ = -1; | |
131 std::vector<ScoredHistoryMatch::ScoreMaxRelevance>* | |
132 ScoredHistoryMatch::hqp_relevance_buckets_ = nullptr; | |
9 | 133 |
10 ScoredHistoryMatch::ScoredHistoryMatch() : raw_score(0), can_inline(false) { | 134 ScoredHistoryMatch::ScoredHistoryMatch() : raw_score(0), can_inline(false) { |
11 } | 135 } |
12 | 136 |
13 ScoredHistoryMatch::ScoredHistoryMatch(const history::URLRow& url_info, | 137 ScoredHistoryMatch::ScoredHistoryMatch( |
Mark P
2015/03/10 00:28:12
I assume you didn't change the content of this fun
sdefresne
2015/03/10 10:43:28
The function was creating a local variable scored_
| |
14 size_t input_location, | 138 const history::URLRow& row, |
15 bool match_in_scheme, | 139 const VisitInfoVector& visits, |
16 bool innermost_match, | 140 const std::string& languages, |
17 int raw_score, | 141 const base::string16& lower_string, |
18 const TermMatches& url_matches, | 142 const String16Vector& terms_vector, |
19 const TermMatches& title_matches, | 143 const WordStarts& terms_to_word_starts_offsets, |
20 bool can_inline) | 144 const RowWordStarts& word_starts, |
21 : HistoryMatch(url_info, input_location, match_in_scheme, innermost_match), | 145 bool is_url_bookmarked, |
22 raw_score(raw_score), | 146 base::Time now) |
23 url_matches(url_matches), | 147 : HistoryMatch(row, 0, false, false), raw_score(0), can_inline(false) { |
24 title_matches(title_matches), | 148 GURL gurl = row.url(); |
25 can_inline(can_inline) { | 149 if (!gurl.is_valid()) |
150 return; | |
151 | |
152 ScoredHistoryMatch::Init(); | |
153 | |
154 // Figure out where each search term appears in the URL and/or page title | |
155 // so that we can score as well as provide autocomplete highlighting. | |
156 base::OffsetAdjuster::Adjustments adjustments; | |
157 base::string16 url = | |
158 bookmarks::CleanUpUrlForMatching(gurl, languages, &adjustments); | |
159 base::string16 title = bookmarks::CleanUpTitleForMatching(row.title()); | |
160 int term_num = 0; | |
161 for (const auto& term : terms_vector) { | |
162 TermMatches url_term_matches = MatchTermInString(term, url, term_num); | |
163 TermMatches title_term_matches = MatchTermInString(term, title, term_num); | |
164 if (url_term_matches.empty() && title_term_matches.empty()) { | |
165 // A term was not found in either URL or title - reject. | |
166 return; | |
167 } | |
168 url_matches.insert(url_matches.end(), url_term_matches.begin(), | |
169 url_term_matches.end()); | |
170 title_matches.insert(title_matches.end(), title_term_matches.begin(), | |
171 title_term_matches.end()); | |
172 ++term_num; | |
173 } | |
174 | |
175 // Sort matches by offset and eliminate any which overlap. | |
176 // TODO(mpearson): Investigate whether this has any meaningful | |
177 // effect on scoring. (It's necessary at some point: removing | |
178 // overlaps and sorting is needed to decide what to highlight in the | |
179 // suggestion string. But this sort and de-overlap doesn't have to | |
180 // be done before scoring.) | |
181 url_matches = SortAndDeoverlapMatches(url_matches); | |
182 title_matches = SortAndDeoverlapMatches(title_matches); | |
183 | |
184 // We can inline autocomplete a match if: | |
185 // 1) there is only one search term | |
186 // 2) AND the match begins immediately after one of the prefixes in | |
187 // URLPrefix such as http://www and https:// (note that one of these | |
188 // is the empty prefix, for cases where the user has typed the scheme) | |
189 // 3) AND the search string does not end in whitespace (making it look to | |
190 // the IMUI as though there is a single search term when actually there | |
191 // is a second, empty term). | |
192 // |best_inlineable_prefix| stores the inlineable prefix computed in | |
193 // clause (2) or NULL if no such prefix exists. (The URL is not inlineable.) | |
194 // Note that using the best prefix here means that when multiple | |
195 // prefixes match, we'll choose to inline following the longest one. | |
196 // For a URL like "http://www.washingtonmutual.com", this means | |
197 // typing "w" will inline "ashington..." instead of "ww.washington...". | |
198 if (!url_matches.empty() && (terms_vector.size() == 1) && | |
199 !IsWhitespace(*lower_string.rbegin())) { | |
200 const base::string16 gurl_spec = base::UTF8ToUTF16(gurl.spec()); | |
201 const URLPrefix* best_inlineable_prefix = | |
202 URLPrefix::BestURLPrefix(gurl_spec, terms_vector[0]); | |
203 if (best_inlineable_prefix) { | |
204 // Initialize innermost_match. | |
205 // The idea here is that matches that occur in the scheme or | |
206 // "www." are worse than matches which don't. For the URLs | |
207 // "http://www.google.com" and "http://wellsfargo.com", we want | |
208 // the omnibox input "w" to cause the latter URL to rank higher | |
209 // than the former. Note that this is not the same as checking | |
210 // whether one match's inlinable prefix has more components than | |
211 // the other match's, since in this example, both matches would | |
212 // have an inlinable prefix of "http://", which is one component. | |
213 // | |
214 // Instead, we look for the overall best (i.e., most components) | |
215 // prefix of the current URL, and then check whether the inlinable | |
216 // prefix has that many components. If it does, this is an | |
217 // "innermost" match, and should be boosted. In the example | |
218 // above, the best prefixes for the two URLs have two and one | |
219 // components respectively, while the inlinable prefixes each | |
220 // have one component; this means the first match is not innermost | |
221 // and the second match is innermost, resulting in us boosting the | |
222 // second match. | |
223 // | |
224 // Now, the code that implements this. | |
225 // The deepest prefix for this URL regardless of where the match is. | |
226 const URLPrefix* best_prefix = | |
227 URLPrefix::BestURLPrefix(gurl_spec, base::string16()); | |
228 DCHECK(best_prefix); | |
229 // If the URL is inlineable, we must have a match. Note the prefix that | |
230 // makes it inlineable may be empty. | |
231 can_inline = true; | |
232 innermost_match = | |
233 best_inlineable_prefix->num_components == best_prefix->num_components; | |
234 } | |
235 } | |
236 | |
237 const float topicality_score = GetTopicalityScore( | |
238 terms_vector.size(), url, terms_to_word_starts_offsets, word_starts); | |
239 const float frequency_score = GetFrequency(now, is_url_bookmarked, visits); | |
240 raw_score = base::saturated_cast<int>(GetFinalRelevancyScore( | |
241 topicality_score, frequency_score, *hqp_relevance_buckets_)); | |
242 | |
243 if (kAlsoDoHupLikeScoring && can_inline) { | |
244 // HistoryURL-provider-like scoring gives any match that is | |
245 // capable of being inlined a certain minimum score. Some of these | |
246 // are given a higher score that lets them be shown in inline. | |
247 // This test here derives from the test in | |
248 // HistoryURLProvider::PromoteMatchForInlineAutocomplete(). | |
249 const bool promote_to_inline = | |
250 (row.typed_count() > 1) || (IsHostOnly() && (row.typed_count() == 1)); | |
251 int hup_like_score = | |
252 promote_to_inline | |
253 ? HistoryURLProvider::kScoreForBestInlineableResult | |
254 : HistoryURLProvider::kBaseScoreForNonInlineableResult; | |
255 | |
256 // Also, if the user types the hostname of a host with a typed | |
257 // visit, then everything from that host get given inlineable scores | |
258 // (because the URL-that-you-typed will go first and everything | |
259 // else will be assigned one minus the previous score, as coded | |
260 // at the end of HistoryURLProvider::DoAutocomplete(). | |
261 if (base::UTF8ToUTF16(gurl.host()) == terms_vector[0]) | |
262 hup_like_score = HistoryURLProvider::kScoreForBestInlineableResult; | |
263 | |
264 // HistoryURLProvider has the function PromoteOrCreateShorterSuggestion() | |
265 // that's meant to promote prefixes of the best match (if they've | |
266 // been visited enough related to the best match) or | |
267 // create/promote host-only suggestions (even if they've never | |
268 // been typed). The code is complicated and we don't try to | |
269 // duplicate the logic here. Instead, we handle a simple case: in | |
270 // low-typed-count ranges, give host-only matches (i.e., | |
271 // http://www.foo.com/ vs. http://www.foo.com/bar.html) a boost so | |
272 // that the host-only match outscores all the other matches that | |
273 // would normally have the same base score. This behavior is not | |
274 // identical to what happens in HistoryURLProvider even in these | |
275 // low typed count ranges--sometimes it will create/promote when | |
276 // this test does not (indeed, we cannot create matches like HUP | |
277 // can) and vice versa--but the underlying philosophy is similar. | |
278 if (!promote_to_inline && IsHostOnly()) | |
279 hup_like_score++; | |
280 | |
281 // All the other logic to goes into hup-like-scoring happens in | |
282 // the tie-breaker case of MatchScoreGreater(). | |
283 | |
284 // Incorporate hup_like_score into raw_score. | |
285 raw_score = std::max(raw_score, hup_like_score); | |
286 } | |
287 | |
288 // If this match is not inlineable and there's a cap on the maximum | |
289 // score that can be given to non-inlineable matches, apply the cap. | |
290 if (!can_inline && (max_assigned_score_for_non_inlineable_matches != -1)) { | |
291 raw_score = | |
292 std::min(raw_score, max_assigned_score_for_non_inlineable_matches); | |
293 } | |
294 | |
295 // Now that we're done processing this entry, correct the offsets of the | |
296 // matches in |url_matches| so they point to offsets in the original URL | |
297 // spec, not the cleaned-up URL string that we used for matching. | |
298 std::vector<size_t> offsets = OffsetsFromTermMatches(url_matches); | |
299 base::OffsetAdjuster::UnadjustOffsets(adjustments, &offsets); | |
300 url_matches = ReplaceOffsetsInTermMatches(url_matches, offsets); | |
26 } | 301 } |
27 | 302 |
28 ScoredHistoryMatch::~ScoredHistoryMatch() { | 303 ScoredHistoryMatch::~ScoredHistoryMatch() { |
29 } | 304 } |
30 | 305 |
31 // Comparison function for sorting ScoredMatches by their scores with | 306 // Comparison function for sorting ScoredMatches by their scores with |
32 // intelligent tie-breaking. | 307 // intelligent tie-breaking. |
33 bool ScoredHistoryMatch::MatchScoreGreater(const ScoredHistoryMatch& m1, | 308 bool ScoredHistoryMatch::MatchScoreGreater(const ScoredHistoryMatch& m1, |
34 const ScoredHistoryMatch& m2) { | 309 const ScoredHistoryMatch& m2) { |
35 if (m1.raw_score != m2.raw_score) | 310 if (m1.raw_score != m2.raw_score) |
(...skipping 23 matching lines...) Expand all Loading... | |
59 return m1.IsHostOnly(); | 334 return m1.IsHostOnly(); |
60 } | 335 } |
61 | 336 |
62 // URLs that have been visited more often are better. | 337 // URLs that have been visited more often are better. |
63 if (m1.url_info.visit_count() != m2.url_info.visit_count()) | 338 if (m1.url_info.visit_count() != m2.url_info.visit_count()) |
64 return m1.url_info.visit_count() > m2.url_info.visit_count(); | 339 return m1.url_info.visit_count() > m2.url_info.visit_count(); |
65 | 340 |
66 // URLs that have been visited more recently are better. | 341 // URLs that have been visited more recently are better. |
67 return m1.url_info.last_visit() > m2.url_info.last_visit(); | 342 return m1.url_info.last_visit() > m2.url_info.last_visit(); |
68 } | 343 } |
344 | |
345 // static | |
Mark P
2015/03/10 00:28:12
I assume you didn't change the content of any of t
sdefresne
2015/03/10 10:43:28
As mentioned in the previous comment, I did change
| |
346 TermMatches ScoredHistoryMatch::FilterTermMatchesByWordStarts( | |
347 const TermMatches& term_matches, | |
348 const WordStarts& terms_to_word_starts_offsets, | |
349 const WordStarts& word_starts, | |
350 size_t start_pos, | |
351 size_t end_pos) { | |
352 // Return early if no filtering is needed. | |
353 if (start_pos == std::string::npos) | |
354 return term_matches; | |
355 TermMatches filtered_matches; | |
356 WordStarts::const_iterator next_word_starts = word_starts.begin(); | |
357 WordStarts::const_iterator end_word_starts = word_starts.end(); | |
358 for (const auto& term_match : term_matches) { | |
359 const size_t term_offset = | |
360 terms_to_word_starts_offsets[term_match.term_num]; | |
361 // Advance next_word_starts until it's >= the position of the term we're | |
362 // considering (adjusted for where the word begins within the term). | |
363 while ((next_word_starts != end_word_starts) && | |
364 (*next_word_starts < (term_match.offset + term_offset))) | |
365 ++next_word_starts; | |
366 // Add the match if it's before the position we start filtering at or | |
367 // after the position we stop filtering at (assuming we have a position | |
368 // to stop filtering at) or if it's at a word boundary. | |
369 if ((term_match.offset < start_pos) || | |
370 ((end_pos != std::string::npos) && (term_match.offset >= end_pos)) || | |
371 ((next_word_starts != end_word_starts) && | |
372 (*next_word_starts == term_match.offset + term_offset))) | |
373 filtered_matches.push_back(term_match); | |
374 } | |
375 return filtered_matches; | |
376 } | |
377 | |
378 // static | |
379 void ScoredHistoryMatch::Init() { | |
380 // Because the code below is not thread safe, we check that we're only calling | |
381 // it from one thread: the UI thread. Specifically, we check "if we've heard | |
382 // of the UI thread then we'd better be on it." The first part is necessary | |
383 // so unit tests pass. (Many unit tests don't set up the threading naming | |
384 // system; hence CurrentlyOn(UI thread) will fail.) | |
385 using content::BrowserThread; | |
386 DCHECK(!BrowserThread::IsThreadInitialized(BrowserThread::UI) || | |
387 BrowserThread::CurrentlyOn(BrowserThread::UI)); | |
388 | |
389 if (initialized) | |
390 return; | |
391 | |
392 initialized = true; | |
393 | |
394 // When doing HUP-like scoring, don't allow a non-inlineable match | |
395 // to beat the score of good inlineable matches. This is a problem | |
396 // because if a non-inlineable match ends up with the highest score | |
397 // from HistoryQuick provider, all HistoryQuick matches get demoted | |
398 // to non-inlineable scores (scores less than 1200). Without | |
399 // HUP-like-scoring, these results would actually come from the HUP | |
400 // and not be demoted, thus outscoring the demoted HQP results. | |
401 // When the HQP provides these, we need to clamp the non-inlineable | |
402 // results to preserve this behavior. | |
403 if (kAlsoDoHupLikeScoring) { | |
404 max_assigned_score_for_non_inlineable_matches = | |
405 HistoryURLProvider::kScoreForBestInlineableResult - 1; | |
406 } | |
407 bookmark_value_ = OmniboxFieldTrial::HQPBookmarkValue(); | |
408 allow_tld_matches_ = OmniboxFieldTrial::HQPAllowMatchInTLDValue(); | |
409 allow_scheme_matches_ = OmniboxFieldTrial::HQPAllowMatchInSchemeValue(); | |
410 | |
411 InitRawTermScoreToTopicalityScoreArray(); | |
412 InitDaysAgoToRecencyScoreArray(); | |
413 InitHQPExperimentalParams(); | |
414 } | |
415 | |
416 float ScoredHistoryMatch::GetTopicalityScore( | |
417 const int num_terms, | |
418 const base::string16& url, | |
419 const WordStarts& terms_to_word_starts_offsets, | |
420 const RowWordStarts& word_starts) { | |
421 ScoredHistoryMatch::Init(); | |
422 // A vector that accumulates per-term scores. The strongest match--a | |
423 // match in the hostname at a word boundary--is worth 10 points. | |
424 // Everything else is less. In general, a match that's not at a word | |
425 // boundary is worth about 1/4th or 1/5th of a match at the word boundary | |
426 // in the same part of the URL/title. | |
427 DCHECK_GT(num_terms, 0); | |
428 std::vector<int> term_scores(num_terms, 0); | |
429 WordStarts::const_iterator next_word_starts = | |
430 word_starts.url_word_starts_.begin(); | |
431 WordStarts::const_iterator end_word_starts = | |
432 word_starts.url_word_starts_.end(); | |
433 const size_t question_mark_pos = url.find('?'); | |
434 const size_t colon_pos = url.find(':'); | |
435 // The + 3 skips the // that probably appears in the protocol | |
436 // after the colon. If the protocol doesn't have two slashes after | |
437 // the colon, that's okay--all this ends up doing is starting our | |
438 // search for the next / a few characters into the hostname. The | |
439 // only times this can cause problems is if we have a protocol without | |
440 // a // after the colon and the hostname is only one or two characters. | |
441 // This isn't worth worrying about. | |
442 const size_t end_of_hostname_pos = (colon_pos != std::string::npos) | |
443 ? url.find('/', colon_pos + 3) | |
444 : url.find('/'); | |
445 size_t last_part_of_hostname_pos = (end_of_hostname_pos != std::string::npos) | |
446 ? url.rfind('.', end_of_hostname_pos) | |
447 : url.rfind('.'); | |
448 // Loop through all URL matches and score them appropriately. | |
449 // First, filter all matches not at a word boundary and in the path (or | |
450 // later). | |
451 url_matches = FilterTermMatchesByWordStarts( | |
452 url_matches, terms_to_word_starts_offsets, word_starts.url_word_starts_, | |
453 end_of_hostname_pos, std::string::npos); | |
454 if (colon_pos != std::string::npos) { | |
455 // Also filter matches not at a word boundary and in the scheme. | |
456 url_matches = FilterTermMatchesByWordStarts( | |
457 url_matches, terms_to_word_starts_offsets, word_starts.url_word_starts_, | |
458 0, colon_pos); | |
459 } | |
460 for (const auto& url_match : url_matches) { | |
461 const size_t term_offset = terms_to_word_starts_offsets[url_match.term_num]; | |
462 // Advance next_word_starts until it's >= the position of the term we're | |
463 // considering (adjusted for where the word begins within the term). | |
464 while ((next_word_starts != end_word_starts) && | |
465 (*next_word_starts < (url_match.offset + term_offset))) { | |
466 ++next_word_starts; | |
467 } | |
468 const bool at_word_boundary = | |
469 (next_word_starts != end_word_starts) && | |
470 (*next_word_starts == url_match.offset + term_offset); | |
471 if ((question_mark_pos != std::string::npos) && | |
472 (url_match.offset > question_mark_pos)) { | |
473 // The match is in a CGI ?... fragment. | |
474 DCHECK(at_word_boundary); | |
475 term_scores[url_match.term_num] += 5; | |
476 } else if ((end_of_hostname_pos != std::string::npos) && | |
477 (url_match.offset > end_of_hostname_pos)) { | |
478 // The match is in the path. | |
479 DCHECK(at_word_boundary); | |
480 term_scores[url_match.term_num] += 8; | |
481 } else if ((colon_pos == std::string::npos) || | |
482 (url_match.offset > colon_pos)) { | |
483 // The match is in the hostname. | |
484 if ((last_part_of_hostname_pos == std::string::npos) || | |
485 (url_match.offset < last_part_of_hostname_pos)) { | |
486 // Either there are no dots in the hostname or this match isn't | |
487 // the last dotted component. | |
488 term_scores[url_match.term_num] += at_word_boundary ? 10 : 2; | |
489 } else { | |
490 // The match is in the last part of a dotted hostname (usually this | |
491 // is the top-level domain .com, .net, etc.). | |
492 if (allow_tld_matches_) | |
493 term_scores[url_match.term_num] += at_word_boundary ? 10 : 0; | |
494 } | |
495 } else { | |
496 // The match is in the protocol (a.k.a. scheme). | |
497 // Matches not at a word boundary should have been filtered already. | |
498 DCHECK(at_word_boundary); | |
499 match_in_scheme = true; | |
500 if (allow_scheme_matches_) | |
501 term_scores[url_match.term_num] += 10; | |
502 } | |
503 } | |
504 // Now do the analogous loop over all matches in the title. | |
505 next_word_starts = word_starts.title_word_starts_.begin(); | |
506 end_word_starts = word_starts.title_word_starts_.end(); | |
507 int word_num = 0; | |
508 title_matches = FilterTermMatchesByWordStarts( | |
509 title_matches, terms_to_word_starts_offsets, | |
510 word_starts.title_word_starts_, 0, std::string::npos); | |
511 for (const auto& title_match : title_matches) { | |
512 const size_t term_offset = | |
513 terms_to_word_starts_offsets[title_match.term_num]; | |
514 // Advance next_word_starts until it's >= the position of the term we're | |
515 // considering (adjusted for where the word begins within the term). | |
516 while ((next_word_starts != end_word_starts) && | |
517 (*next_word_starts < (title_match.offset + term_offset))) { | |
518 ++next_word_starts; | |
519 ++word_num; | |
520 } | |
521 if (word_num >= 10) | |
522 break; // only count the first ten words | |
523 DCHECK(next_word_starts != end_word_starts); | |
524 DCHECK_EQ(*next_word_starts, title_match.offset + term_offset) | |
525 << "not at word boundary"; | |
526 term_scores[title_match.term_num] += 8; | |
527 } | |
528 // TODO(mpearson): Restore logic for penalizing out-of-order matches. | |
529 // (Perhaps discount them by 0.8?) | |
530 // TODO(mpearson): Consider: if the earliest match occurs late in the string, | |
531 // should we discount it? | |
532 // TODO(mpearson): Consider: do we want to score based on how much of the | |
533 // input string the input covers? (I'm leaning toward no.) | |
534 | |
535 // Compute the topicality_score as the sum of transformed term_scores. | |
536 float topicality_score = 0; | |
537 for (int term_score : term_scores) { | |
538 // Drop this URL if it seems like a term didn't appear or, more precisely, | |
539 // didn't appear in a part of the URL or title that we trust enough | |
540 // to give it credit for. For instance, terms that appear in the middle | |
541 // of a CGI parameter get no credit. Almost all the matches dropped | |
542 // due to this test would look stupid if shown to the user. | |
543 if (term_score == 0) | |
544 return 0; | |
545 topicality_score += raw_term_score_to_topicality_score[std::min( | |
546 term_score, kMaxRawTermScore - 1)]; | |
547 } | |
548 // TODO(mpearson): If there are multiple terms, consider taking the | |
549 // geometric mean of per-term scores rather than the arithmetic mean. | |
550 | |
551 const float final_topicality_score = topicality_score / num_terms; | |
552 | |
553 // Demote the URL if the topicality score is less than threshold. | |
554 if (hqp_experimental_scoring_enabled_ && | |
555 (final_topicality_score < topicality_threshold_)) { | |
556 return 0.0; | |
557 } | |
558 | |
559 return final_topicality_score; | |
560 } | |
561 | |
562 // static | |
563 float ScoredHistoryMatch::GetRecencyScore(int last_visit_days_ago) { | |
564 ScoredHistoryMatch::Init(); | |
565 // Lookup the score in days_ago_to_recency_score, treating | |
566 // everything older than what we've precomputed as the oldest thing | |
567 // we've precomputed. The std::max is to protect against corruption | |
568 // in the database (in case last_visit_days_ago is negative). | |
569 return days_ago_to_recency_score[std::max( | |
570 std::min(last_visit_days_ago, kDaysToPrecomputeRecencyScoresFor - 1), 0)]; | |
571 } | |
572 | |
573 // static | |
574 float ScoredHistoryMatch::GetFrequency(const base::Time& now, | |
575 const bool bookmarked, | |
576 const VisitInfoVector& visits) { | |
577 // Compute the weighted average |value_of_transition| over the last at | |
578 // most kMaxVisitsToScore visits, where each visit is weighted using | |
579 // GetRecencyScore() based on how many days ago it happened. Use | |
580 // kMaxVisitsToScore as the denominator for the average regardless of | |
581 // how many visits there were in order to penalize a match that has | |
582 // fewer visits than kMaxVisitsToScore. | |
583 float summed_visit_points = 0; | |
584 const size_t max_visit_to_score = | |
585 std::min(visits.size(), ScoredHistoryMatch::kMaxVisitsToScore); | |
586 for (size_t i = 0; i < max_visit_to_score; ++i) { | |
587 int value_of_transition = | |
588 (visits[i].second == ui::PAGE_TRANSITION_TYPED) ? 20 : 1; | |
589 if (bookmarked) | |
590 value_of_transition = std::max(value_of_transition, bookmark_value_); | |
591 const float bucket_weight = | |
592 GetRecencyScore((now - visits[i].first).InDays()); | |
593 summed_visit_points += (value_of_transition * bucket_weight); | |
594 } | |
595 return visits.size() * summed_visit_points / | |
596 ScoredHistoryMatch::kMaxVisitsToScore; | |
597 } | |
598 | |
599 // static | |
600 float ScoredHistoryMatch::GetFinalRelevancyScore( | |
601 float topicality_score, | |
602 float frequency_score, | |
603 const std::vector<ScoreMaxRelevance>& hqp_relevance_buckets) { | |
604 DCHECK(hqp_relevance_buckets.size() > 0); | |
605 DCHECK_EQ(hqp_relevance_buckets[0].first, 0.0); | |
606 | |
607 if (topicality_score == 0) | |
608 return 0; | |
609 // Here's how to interpret intermediate_score: Suppose the omnibox | |
610 // has one input term. Suppose we have a URL for which the omnibox | |
611 // input term has a single URL hostname hit at a word boundary. (This | |
612 // implies topicality_score = 1.0.). Then the intermediate_score for | |
613 // this URL will depend entirely on the frequency_score with | |
614 // this interpretation: | |
615 // - a single typed visit more than three months ago, no other visits -> 0.2 | |
616 // - a visit every three days, no typed visits -> 0.706 | |
617 // - a visit every day, no typed visits -> 0.916 | |
618 // - a single typed visit yesterday, no other visits -> 2.0 | |
619 // - a typed visit once a week -> 11.77 | |
620 // - a typed visit every three days -> 14.12 | |
621 // - at least ten typed visits today -> 20.0 (maximum score) | |
622 // | |
623 // The below code maps intermediate_score to the range [0, 1399]. | |
624 // For example: | |
625 // HQP default scoring buckets: "0.0:400,1.5:600,12.0:1300,20.0:1399" | |
626 // We will linearly interpolate the scores between: | |
627 // 0 to 1.5 --> 400 to 600 | |
628 // 1.5 to 12.0 --> 600 to 1300 | |
629 // 12.0 to 20.0 --> 1300 to 1399 | |
630 // >= 20.0 --> 1399 | |
631 // | |
632 // The score maxes out at 1399 (i.e., cannot beat a good inlineable result | |
633 // from HistoryURL provider). | |
634 const float intermediate_score = topicality_score * frequency_score; | |
635 | |
636 // Find the threshold where intermediate score is greater than bucket. | |
637 size_t i = 1; | |
638 for (; i < hqp_relevance_buckets.size(); ++i) { | |
639 const ScoreMaxRelevance& hqp_bucket = hqp_relevance_buckets[i]; | |
640 if (intermediate_score >= hqp_bucket.first) { | |
641 continue; | |
642 } | |
643 const ScoreMaxRelevance& previous_bucket = hqp_relevance_buckets[i - 1]; | |
644 const float slope = ((hqp_bucket.second - previous_bucket.second) / | |
645 (hqp_bucket.first - previous_bucket.first)); | |
646 return (previous_bucket.second + | |
647 (slope * (intermediate_score - previous_bucket.first))); | |
648 } | |
649 // It will reach this stage when the score is > highest bucket score. | |
650 // Return the highest bucket score. | |
651 return hqp_relevance_buckets[i - 1].second; | |
652 } | |
653 | |
654 // static | |
655 void ScoredHistoryMatch::InitHQPExperimentalParams() { | |
656 // These are default HQP relevance scoring buckets. | |
657 // See GetFinalRelevancyScore() for details. | |
658 std::string hqp_relevance_buckets_str = "0.0:400,1.5:600,12.0:1300,20.0:1399"; | |
659 | |
660 // Fetch the experiment params if they are any. | |
661 hqp_experimental_scoring_enabled_ = | |
662 OmniboxFieldTrial::HQPExperimentalScoringEnabled(); | |
663 | |
664 if (hqp_experimental_scoring_enabled_) { | |
665 // Add the topicality threshold from experiment params. | |
666 float hqp_experimental_topicality_threhold = | |
667 OmniboxFieldTrial::HQPExperimentalTopicalityThreshold(); | |
668 topicality_threshold_ = hqp_experimental_topicality_threhold; | |
669 | |
670 // Add the HQP experimental scoring buckets. | |
671 std::string hqp_experimental_scoring_buckets = | |
672 OmniboxFieldTrial::HQPExperimentalScoringBuckets(); | |
673 if (!hqp_experimental_scoring_buckets.empty()) | |
674 hqp_relevance_buckets_str = hqp_experimental_scoring_buckets; | |
675 } | |
676 | |
677 // Parse the hqp_relevance_buckets_str string once and store them in vector | |
678 // which is easy to access. | |
679 hqp_relevance_buckets_ = | |
680 new std::vector<ScoredHistoryMatch::ScoreMaxRelevance>(); | |
681 | |
682 bool is_valid_bucket_str = GetHQPBucketsFromString(hqp_relevance_buckets_str, | |
683 hqp_relevance_buckets_); | |
684 DCHECK(is_valid_bucket_str); | |
685 } | |
686 | |
687 // static | |
688 bool ScoredHistoryMatch::GetHQPBucketsFromString( | |
689 const std::string& buckets_str, | |
690 std::vector<ScoreMaxRelevance>* hqp_buckets) { | |
691 DCHECK(hqp_buckets != NULL); | |
692 DCHECK(!buckets_str.empty()); | |
693 | |
694 base::StringPairs kv_pairs; | |
695 if (base::SplitStringIntoKeyValuePairs(buckets_str, ':', ',', &kv_pairs)) { | |
696 for (base::StringPairs::const_iterator it = kv_pairs.begin(); | |
697 it != kv_pairs.end(); ++it) { | |
698 ScoreMaxRelevance bucket; | |
699 bool is_valid_intermediate_score = | |
700 base::StringToDouble(it->first, &bucket.first); | |
701 DCHECK(is_valid_intermediate_score); | |
702 bool is_valid_hqp_score = base::StringToInt(it->second, &bucket.second); | |
703 DCHECK(is_valid_hqp_score); | |
704 hqp_buckets->push_back(bucket); | |
705 } | |
706 return true; | |
707 } | |
708 return false; | |
709 } | |
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