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A Structured Approach to Query Recommendation With Social Annotation Data
童薇
2010/12/3
Outline
Motivation Challenges Approach Experimental Results Conclusions
2010/12/3
Outline
Motivation Challenges Approach Experimental Results Conclusions
2010/12/3
Motivation
Query Recommendation Help users search Improve the usability of search engines
2010/12/3
Recommend what?
Existing Work Search interests: stick to user’s search intent
Anything Missing? Exploratory Interests: some vague or delitescent interests Unaware of until users are faced with one May be provoked within a search session
2010/12/3
smartphones
apple productsnexus one
mobilemeipod touch
equivalent or highly related queries
apple iphone
Is the existence of exploratory interest common and significant?
Identified from search user behavior analysis Make use of one-week log search data
Verified by Statistical Tests(Log-likehood Ratio Test) Analyze the causality between initial queries and
consequ-ent queries Results In 80.9% of cases: Clicks on search results indeed affect
the formulation of the next queries In 43.1% of cases: Users would issue different next
queries if they clicked on different results
2010/12/3
Two different heading directions of Query Recommendation Emphasize search interests:
Help users easily refine their queries and find what they
need more quickly Enhance the “search-click-leave” behavior
Focus on exploratory interests: Attract more user clicks and make search and browse
more closely integrated Increase the staying time and advertisement revenue
Recommend queries to satisfy both search and exploratory interests of users simultaneously
2010/12/3
equivalent or highly related queries
apple iphonemobilemeipod touch
nexus one
Outline
Motivation Challenges Our Approach Experimental Results Conclusions
2010/12/3
Challenges To leverage what kind of data resource?
Search logs: Interactions between search users and search engines
Social annotation data: Keywords according to the content of the pages
“wisdom of crowds”
2010/12/3
Challenges To leverage what kind of data resource? How to present such recommendations to users?
Refine queries
Stimulate exploratory interests
2010/12/3
A Structured Approach to Query Recommendation
With Social Annotation Data
Outline
Motivation Challenges Approach Experimental Results Conclusions
2010/12/3
Approach
Query Relation Graph A one-mode graph with the nodes representing all
the unique queries and the edges capturing relationships between queries
Structured Query Recommendation Ranking using Expected Hitting Time Clustering with Modularity Labeling each cluster with social tags
2010/12/3
Query RelationGraph
Query Formulation Model
2010/12/3
Query RelationGraph
Query Formulation Model
2010/12/3
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Query RelationGraph
Query Formulation Model Construction of Query Relation Graph
2010/12/3
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Ranking with Hitting Time
Apply a Markov random walk on the graph Employ hitting time as a measure to rank queries The expected number of steps before node j is visited
starting from node i The hiting time T is the first time that the random walk is at
node j from the start node i:
The mean hitting time h(j|i) is the expectation of T under the condition
2010/12/3
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Ranking with Hitting Time
Apply a Markov random walk on the graph Employ hitting time as a measure to rank queries The expected number of steps before node j is visited
starting from node i Satisfies the following linear system
2010/12/3
Clustering with Modularity
Group the top k recommendations into clusters It is natural to apply a graph clustering approach Modularity function
Note: In a network in which edges fall between vertices without regard for the communities they belong to ,we would have
2010/12/3
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Clustering with Modularity
Group the top k recommendations into clusters It is natural to apply a graph clustering approach Modularity function
Employ the fast unfolding algorithm to perform clustering
2010/12/3
Clustering with Modularity
Group the top k recommendations into clusters It is natural to apply a graph clustering approach Modularity function
Employ the fast unfolding algorithm to perform clustering
Label each cluster explicitly with social tagsThe expected tag distribution given a query:
The expected tag distribution under a cluster:
2010/12/3
Outline
Motivation Challenges Approach Experimental Results Conclusions
2010/12/3
Experimental Results
Data set Query Logs: Spring 2006 Data Asset (Microsoft Research)
15 million records (from US users) sampled over one month in May, 2006 2.7 million unique queries and 4.2 million unique URLs
Social Annotation Data: Delicious data Over 167 million taggings sampled during October and November, 2008 0.83 million unique users, 57.8 unique URLs and 5.9 million unique tags
Query Relation Graph: 538, 547 query nodes
Baseline Methods BiHit: Hitting Time approach based on query logs (Mei et al.
CIKM ’08) TriList: list-based approach to query recommendation
considering both search and exploratory interests TriStrucutre: Our approach2010/12/3
Examplesof RecommendationResults Query = espn
BiHit
espn magazine
espn go
espn news
espn sports
esonsports
baseball news espn
espn mlb
sports news
espn radio
espn 103.3
espn cell phone
espn baseball
sports
mobile espn
espn hockey
TriList
espn radio
espn news
yahoo sports
nba news
cbs sportsline
espn nba
sports
espn mlb
espn sports
sporting news
scout
sportsline
sports illustrated
bill simmons
fox sports
TriStructure
[sports espn news]
espn radio
espn news
espn nba
espn mlb
espn sports
bill simmons
[sports news scores]
yahoo sports
nba news
cbs sportsline
sports
sporting news
scout
sportsline
sports illustrated
fox sports2010/12/3
Examplesof RecommendationResults Query = 24
BiHit
24 season 5
24 series
24 on fox
24 fox
fox 24
24 tv show
tv show 24
24 hour
fox television network
fox broadcasting
fox tv
fox sports net
fox sport
ktvi 2
fox five news
TriList
fox 24
kiefer sutherland
tv guide
24 tv show
24 fox
jack bauer
grey’s anatomy
24 on fox
desperate housewives
prison break
24 spoilers
abc
tv listings
fox
one tree hill
TriStructure
[tv 24 entertainment]
fox 24
kiefer sutherland
24 tv show
24 fox
jack bauer
24 on fox
24 spoilers
[tv televisions entertainment]
tv guide
abc
tv listings
fox
[tv television series]
grey’s anatomy
desperate housewives
prison break
one tree hill2010/12/3
Manual Evaluation
Comparison based on users’click behavior A label tool to simulate the real search scenario Label how likelihood the user would like to click (6-point scale) Randomly sampled 300 queries, 9 human judges
2010/12/3
Distributions of Labeled Score over Recommendations
Experimental Results (cont.)
Overall Performance non-zero label score click➡
Clicked Recommendation Number (CRN)
Clicked Recommendation Score (CRS)
Total Recommendation Score (TRS)Click Performance Comparison
2010/12/3
How Structure Helps How the structured approach affects users’ click willingness Click Entropy
Experimental Results (cont.)
The Average Click Entropy over Queries under the TriList and TriStructure Methods.2010/12/3
How Structure Helps How the structured approach affects users’ click patterns Label Score Correlation
Experimental Results (cont.)
Correlation between the Average Label Scores on Same Recommendations for Queries.2010/12/3
Outline
Motivation Challenges Approach Experimental Results Conclusions
2010/12/3
Conclusions
Recommend queries in a structured way for better
satisfying both search and exploratory interests of users Introduce the social annotation data as an important
resource for recommendation Better satisfy users interests and significantly enhance
user’s click behavior on recommendations Future work
Trade-off between diversity and concentration Tag propagation
2010/12/3
Thanks!
2010/12/3