搜索中的带区域化和个性化的自动补全和自动建议技术 叶旭刚

Transcription

1 搜索中的带区域化和个性化的自动补全和自动建议技术 叶旭刚

2 Probabilistic Autocomplete Suggestion Ranking with Localization and Personalization Xugang Ye

3 General Search vs. Vertical Search General Search General data General users Many categories Difficult in query understanding Vertical search Vertical data Specific user group Much less categories Easier in query understanding

4 General Map Search

5 Real-Estate Search

6 Real-Estate Search

7 Real-Estate Search

8 Business Logic Home for sale Seller s agent Listing services Technological platform Home owner/seller Buyer s agent Home shoppers/buyers

9 Problem Formulation Autocomplete suggestion Search functionality Probabilistic ranking model Localization Personalization

10 Key Quantity: (,l, ) :probability : suggestion : user typed input l: user location (LatLong) : user stats Computing Method Approximation:,l, (,l ) ( ) ( ) =,l ( ) (,l) ( ) ( ) Problem Formulation Conditioning:,l =, ( l), : shard;, =, (, ), ( ) : expression space of shard ( ) ( ) ( ) =, : -thfeature

11 Two-phase greedy ranking Localization:,l = ( l) Personalization: ( ) Score, = ln Ranking, (, ), l = ( (,l)) ( (,l))

12 Pre-count knowledge: ( ), =, ; ( ) =, ;, ; Post-count update: ( ), ; =, ;, ; Ranking ( ) ( ),, = ; ( ) ; for all =, ;, ;, ; ( ), ; ( ) = ;. ( ) ( ), ;, ;, ; ( ), ;, ; ( ),, ; ( ) ; = ; ; for all = ; ; ; ( ), ; ; ( ) ( ) ; ; ( ) ( ) ; ; ;

13 Global Items,l =, ( l) =, ( l) :, What if ( l)is very small for all such that, >0? Method 1: is put into all shards (accurate, but computationally expensive) Method 2: Localization formula adjusted (less accurate, but computationally cheap):,l =, l;, ( l; )= 1 l

14 Engineering Support,l = l; Location weight ( ), (, ) Per-shard suggestion index Score, = ln Per-shard Trie : quad tree geo-sharding(by population) l; : (geo-code, geo shard)- probability table, list of global items, : (expression, suggestion)- probability table for shard (, ): expression-trie for shard : (suggestion, feature)-probability table : feature-probability table : (user, feature)-probability table Global feature index User activities profile

15 System Architect Data sources: Offline Online Location entities: Addresses Feedback counting model Feedback repository Dynamic parser Users events log Regions Schools Geo-based property counting model Metrics report Dashboard Autocomplete suggestion results Instrumentation module Points of interest Property features: Expression/ synonym expansion model Data integration module Model files Execution module Map & search front end Attributes Descriptive phrases Text-based property counting model Training data (unified format) Probabilistic ranking model Users typed inputs

16 Evaluation Metrics Runtime execution time Retrieval time (in ms.) by length of typehead Click-based measurements Typing effort, which is measured by number of chars typed upon click. Clicked position. Click-recall of top positions, defined as: = Click-precision of top k positions, defined as: = ( ), ( ) where ( ) is the clicked position of the click, ( ) is the indicator of whether ( ). Session success, which is measured by number of chars entered upon click on search button.

17 Result Demo

18 Result Demo

19 Result Demo

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23 Result Demo

24 Metrics Results Length of typehead = 2: Min. 1st Qu. Median Mean3rd Qu. Max Length of typehead = 3 Min. 1st Qu. Median Mean3rd Qu. Max Length of typehead = 4 Min. 1st Qu. Median Mean3rd Qu. Max Length of typehead = 5 Min. 1st Qu. Median Mean3rd Qu. Max typeheads or abbreviations were randomly generated from the expression space and the retrieval time (in ms.) for typehead lengths: 2, 3, 4, 5 were kept track of

25 Metrics Results Three months experimental user click-logs for the three types of settings: general ranking (GR), localized ranking (LR), localized and personalized ranking (LPR)

26 Questions? Thanks