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When Relevance is not Enough:Promoting Diversity and Freshness inPersonalized Question Recommendation IDAN SZPEKTOR,YOELLE MAAREK,DAN PELLEG
YAHOO!RESEARCH
ABSTRACT
a good question recommendation system
1. designed around answerers, rather than exclusively for askers
2. Scale to many questions and users and be fast enough
3. Relevant to his or her interests
4. diversity
INTRODUCTION
Common way: only to the best possible answerers (“experts”)
All potential answerers
INTRODUCTION
relevance: to what degree the question matches the user’s tastes
diversity and freshness needs
Three requirements:
1. questions need to be recommended for all types of users
2. questions have to be diverse
3. recommendations need to be fresh and be served fasta) serve questions as recommendations immediately
b) instantly adapting to users’ changes in taste
RELATED WORK
limitations
real-time ranking
the needs of new users with very little historical data are not addressed well.
only on relevance
FrameworkQuestion profile:
1. LDA model
2. Lexical model
3. Category model
User profile:
Question recommendationMatching question and user profiles
Proactive diversification
Recommendation merging
QUESTION PROFILE
Split it according to the 26 top categories in Yahoo! Answers
Two Advantage:1. represent disjoint users’ interests.
2. word sense disambiguation
1. question textual content(title and body)
2. category
QUESTION PROFILE
Build profile, which is represented by three vectors:
1. a Latent Dirichlet Allocation (LDA) topic vector
2. a lexical vector
3. a category vector
LDA Model
1. Initial training: a random sample of up to 2 million resolved questions
2. Incremental learning: a random sample of up to half a million questions per top category
3. Inference: at least10% of the probability mass
Lexical Model a unigram bag-of-words representation of a question
tf·idf score / L1 normalized
a probability distribution
Category Model a probability of 1 to the category in which the question was posted
USER PROFILE
the questions answered in the past
the user representation is generated by aggregating signals over these questions
user profile: a probability tree
1. Aggregating the profiles of the questions the user answered
2. Update
the first and third tree levels: a decaying factor on past questions
the second level:1. Measure the similarity between the feature distribution of each model in the
question and the corresponding feature distribution in the user profile
2. Normalized to a probability distribution
QUESTION RECOMMENDATION
Matching Question and User Profiles
A list of open questions ranked by a relevance score, which is calculated for the pair {questionprofile , user profile}
For question profiles:
1. Turn the three vectors forming the question profile into a single vector, multiply the probability of each feature by 1/3 before storing it in the index
2. Index every question vector and build an inverted index
QUESTION RECOMMENDATION
For user profile:
associate with each user feature a score that consists of the product of each probability score on the tree path that led to this feature
Ranking:
Similarity: a simple dot-product
QUESTION RECOMMENDATION
Proactive Diversification
thematic sampling:
1. For each user vector u , we generate N query vectors u 1 ;u 2 ;…;u N
2. N ranked lists
3. Blending them together results in a final diverse list
Two types of thematic constraints:
specific top category: randomly select top categories as constraints by sampling without repetition based on their distribution in the root node of the user’s probability tree
spefic LDA topic: randomly sample LDA topics without repetition from the user profile by traversing the probability tree
QUESTION RECOMMENDATION
Recommendation Merging
blending algorithm
1. Each list being associated with a probability score
2. Sampling an intermediate list, based on the assigned probabilities
3. Removing one recommendation from the sampled list to be added at the end of the finallist.
4. Repeat
QUESTION RECOMMENDATION
Non-Thematic LDA Topics
QUESTION RECOMMENDATION
Non-Thematic LDA Topics
116 topics, 23 top categories34% non-thematic topics
A logistic regression classifier
EXPERIMENTS
Offline Experiment
8 different top categories
Active users: at least 21 questions as of January 2011
New users: at least two questions as of January 2011
EXPERIMENTS
Online Experiment
A/B test
Control bucket , CTL ( n = 25093)
Relevance bucket , R ( n = 5359)
Freshness bucket , F ( n = 46228) : 50% recent ; 20% thematic sampling
Diversity bucket , D ( n = 42041) : 20% recent ; 50% thematic sampling
CONCLUSIONS
Relevance, but also by freshness and diversity
Several relevance models
“question retrieval engine“
Diversity: thematic sampling
内容上:different factors/models/levels
写作上:层次清楚,递进