Firekqsps
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This deck was presented at FIRE 2017 to describe academic research in understanding and enriching queries into online shopping platforms
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- 3. Product search: query understanding 5 Classifica?on Tagging User intent Facets Segmenta?on Query understanding: agenda • Classifica.on • Tagging • Segmenta?on • Facets 6
- 4. 7 Classifica?on: types Informa?onal Naviga?onal Transac?onal vs. topic 8 Classifica?on: customer need • Predefined taxonomy – Ranked list of categories • Relevant informa?on – Even if no exact match
- 5. Classifica?on: technical challenges • Brevity – Not enough clues – Few features • Ambiguity – Belong to mul?ple categories 9 10 Classifica?on: query enrichment [Shen ’06a, ‘06b] • Search results – True meaning from Web – Ensemble learning to remove engine bias • Category word match – Extended with WordNet – (e.g., hardware -> device)
- 6. 11 Classifica?on: linguis?c analysis • [Beitzel ‘07] • Selec?onal preference rules – Mined from unlabeled query logs • Syntac?c arguments of words – Belonging to seman?c classes – E.g., object of Eat is edible 12 Classifica?on: training data [Acero ‘08] • Click-thru log – Infer class membership of unlabeled queries – Through proximity to labeled ones
- 7. 13 Classifica?on: product query 1. Enrichment through snippets 2. Expansion through similar queries 3. Transla?ng labeled product name to pseudo queries Classifica?on: search snippet • [Shen ‘09] • Context: surrounding text – E.g., sx430 20.0 megapixel • Similar to pseudo- relevance feedback – Even if no exact match between query/result 14
- 8. 15 Classifica?on: related query • Bipar?te graph – Query/URL • Link weighted by click number – Propor?onal transi?on probability • Random walk for finding similarity [Mei ‘08] – Hiing ?me: from one node to another Classifica?on: pseudo query • Language difference between product name/ query – Feature space of training/ test data • Tuple ngram based transla?on model – Joint probability of source/ target sentence pairs 16
- 9. 17 Classifica?on: incorpora?ng context • [Cao ‘09] • CRF captures session intent – neighboring queries and clicked URLS • More flexible than HMM – richer features:Web directory 18 Classifica?on: local features • Query term – Weights learned in training phase – Sparse to associate with category labels • Feedback from external Web directory – Pseudo: top M results – Implicit: clicked URL
- 10. 19 Classifica?on: contextual features • Direct associa?on – Adjacent labels • Taxonomy-based – Siblings Query understanding: agenda • Classifica?on • Tagging • Segmenta?on • Facets 20
- 11. Tagging: beyond keyword search • Instant answers – directly address user informa?on needs • Avoid clicking – into an external document 21 Tagging: structured search • Precise results – within a domain • Unstructured queries – free word order • Natural language commands – virtual assistant applica?on 22
- 12. Tagging: business problem • Crawl data from RDB – Index into less ambiguous content • Extract informa?on – Format consistent with backend 23 24 Tagging: task defini?on • Analyze query – structure/seman?cs • Assign label to tokens – Indica?ng which field it belongs to
- 13. 25 Tagging: seman?c features • Intent – Head: aQribute, Modifier: value • Lexicon – Structured data sources – Query log mining Lexicon: challenges • Enumerate values – for each field • Query formula?on – ambiguous terms – Out-of-dic?onary words 26
- 14. Lexicon: challenges (contd.) • Domain-specific knowledge – Reduces manual annota?on • Human effort s?ll needed • Search topics evolve with ?me 27 Lexicon: seman?c class • [Wang ‘09] • extrac?on automated – using HTML lists • precision less important • BeQer criteria is trade- off – Maximizing recall – Minimizing confusability 28
- 15. 29 Lexicon: set expansion • HTML lists – contain instances of a single concept • Phrases of classes – extracted from annotated queries • Used as seeds – in graph learning for more instances Tagging: derived labels • [Li 09] • [query, product ?tle] as click event from log • Associate query with product metadata in RDB – Through fuzzy match of ?tle for higher coverage • Map source to target schema – E.g., color -> aQribute 30
- 16. Tagging: Sequence label • [Cheung ‘12] • Sequence clustering – queries with similar intent • Recognize paQerns – With sequence of seman?c concepts/lexical items • Automa?cally annotate new queries – With intent summary 31 Query understanding: agenda • Classifica?on • Tagging • Segmenta.on • Facets 32
- 18. Segmenta?on: noun phrase • Context features – Neighboring tokens cri?cal for discrimina?on – E.g., “bank loan” “amor?za?on schedule”, NOT “loan amor?za?on” • Dependency features – More likely to modify a later token – E.g., female bus driver 35 36 Segmenta?on: external knowledge • Corpus – Frequent paQerns [Bergsma ‘07] • Wikipedia [Tan ‘08] [Hagen ‘11] – Well-formed concepts • Search log [Li ‘11]
- 19. Segmenta?on: language models • Vector space based on bag-of- words – sparsity makes es?ma?on with dependencies harder – E.g., “new york ?mes” • Query term order important – Not independent in relevance computa?on – E.g., “NY ?mes” “travel guide” 37 Segmenta?on: n-gram scoring • Weighted sum of frequencies • Apply word-length based normaliza?on – Exponen?al factors rather than length • So longer segment has beQer chance – E.g., Toronto blue jays 38
- 20. 39 Segmenta?on: modeling ambiguity • [Li ‘11] • Quan?fy uncertainty – Probabilis?cally through query/clicked document • Capture user preference – Likelihood of a structure through collec?ve behavior Segmenta?on: evalua?on framework • Decouple accuracy from how segmenta?on is used for IR [Roy ‘12] – Generate all possible quoted versions • Only human relevance judgements for query-URL pairs 40
- 21. 41 Segmenta?on: e-tail challenges Phrases permuted oren No matching Wikipedia ?tles Reformula?on for null queries Segmenta?on: user-intent score [Parikh ‘13] • Mul?ple training corpora – For each product meta category – Frequency taken from most relevant corpus • Unsupervised metric – from click paQerns on result set – Captures for segmented form, closeness between intent/result 42
- 22. Segmenta?on: improving precision 43 NOT Segmenta?on: similar queries 44 1. Term-based 2. Unit-based
- 23. Segmenta?on: en?ty seeking • [Joshi ‘14] • Assign a purpose to each segment – Base en?ty, rela?on type, target en?ty type, contextual words 45 Query understanding: agenda • Classifica?on • Tagging • Segmenta?on • Facets 46
- 24. 47 Facet: customer need • Summariza?on – from mul?ple perspec?ves • Set of items – which describe a query aspect • Similar to en?ty aQributes 48 Facet: customer need (contd.) • Helps restrict results – to relevant items – by clarifying intent • Displayed along with results – for beQer experience
- 26. 51 Facet: intent taxonomy • [Yin ‘10] • Find phrases from queries • Organize into tree – Indica?ng equivalent meaning on same node – Parent-child links represent intent rela?onship 52 Facet: Intent phrase • Represent generic search sense – Involving en??es of a certain class • Rela?onship – E.g., City -> tourism, government
- 27. 53 Facet: query dimension • [Dou ‘11] • Aggregate frequent lists – within top results • Extract from free text – HTML tags and repeat regions • Group into clusters – based on contained items Facet: QD Miner 54 1. By gender, brand, type, … 2. Descending: brand, type, price, … 3. Grouping for dimension 4. Item ranked by importance (e.g., black)
- 28. 55 Facet: subtopic mining • [Hu ‘12] • One per search – Mul?ple URLs clicked in a query represent same sense • Subtopic clarifica?on by keyword – Expansion for intent 56 Facet: clarifica?on by keyword • Short queries simply noun phrases – E.g., Harry Shum • Expansion for intent – Label of subtopic – E.g., Microsor, Jr.