1. Personalized Search Result Diversification via Structured Learning
Shangsong Liang, Zhaochun Ren, Maarten de Rijke
University of Amsterdam
Presented by Yu Hu

2. Tackling Ambiguous Query
Personalization approach:
Tailor the results to the specific interests of the user
Inaccurate user profile
When query is unrelated to the personalized information
Diversification Approach:
Maximize probability of showing an interpretation relevant to the user
Outliers

3. Diversification
Query: Queen ?
Diversified Results

4. Personalization
Query: Queen
Personalized ordering
User Profile

5. Overview of PSVMdiv
Given a user and a query, predict a diverse set of docs
Formulate a discriminant based on maximizing search result diversification
Perform training using the structured support vector machines framework
User interest LDA-style topic model
Infer a per-document per-user multinomial distribution over topics and determine whether a document can cater to a specific user
During Training use features extracted from three sources

6. The Learning Problem
Given a user and a set of documents, select a subset of documents that maximizes search result diversification for the user
y: candidate documents
x : a set of documents
u: documents user u is interested in
Loss Function:

7. The Learning Problem
Learn a hypothesis function to predict a y given x and u;
Labeled training data assumed to be available:
To find a function h such that the empirical risk
can be minimized;
Let a discriminant compute how well the predicting y fits x and u.
The hypothesis predicts the y that maximizes F:
Each (x, u, y) is described through a feature vector
The discriminant function is assumed to be linear in the feature space：

8. Standard SVMs and Additional Constraints
Optimization problem for standard SVMs
Additional constraints:
For diversity:
For consistency with user’s interest:

9. User Interest Topic Model
To capture per-user and per-document distributions over topics

10. Latent Dirichelet Allocation
α is the Dirichlet prior on the per-document topic distributions,
β is the Dirichlet prior on the per-topic word distribution,
θi is the topic distribution for document i,
ϕk is the word distribution for topic k,
Z is the topic for the j th word in document i, and
wij is the specific word.

11. Feature Space Three types:
Extracted directly from tokens’ statistical information in the documents
Compute similarity scores between a document x ϵ y and a set of documents u that a user is interested in. Cosine, Euclidean, KL divergence metrics are considered.
Those generated from proposed user- interest LDA-style topic model
Compute similarity scores between a document x ϵ y and a set of documents u based on a multinomial distribution over topics and the user’s multinomial distribution over topics generated by the User Interest Topic Model. Cosine, Euclidean, KL divergence metrics are considered.
Those utilized by unsupervised personalized diversification algorithms
The main probability used in state-of-art unsupervised personalized diversification methods are utilized here as features. Such as p(d|q), the probability of d relevant to q; p(c|d), the probability of d belonging to a category c, etc.

12. Dataset A publicly available personalized diversification dataset.
Contains private evaluation information from 35 users on 180 search queries
Ambiguous queries, length no more than two keywords
751 subtopics for the queries, with most of the queries having more than 2 subtopics
Over 3800 relevance judgments are available, for at least top 5 results for each query
Each relevance judgment includes 3 main assessments
4-grade scale assessment on how relevant the result is to the user’s interest—user relevance
4-grade scale assessment on how relevant the result is to the evaluated query—topic relevance
2-grade assessment whether a subtopic is related to the evaluated query

13. Baselines PSVMdiv compared to 11 baselines: Traditional: BM25
Plain diversity: IA-select, xQuAD
Plain personalization: PersBM25
Two step, first div, then pers: xQuADBM25
Pers-diversification: PIA-select, PIA-select BM25 , PxQuAD, PxQuAD BM25
Supervised diversification: SVMdiv, SVMrank

14. Results & Analysis -Supervised v. Unsupervised

15. Results & Analysis- Effect of UIT Model

16. Results & Analysis-Effects of Constraints

17. Query-Level Analysis

18. Conclusion Pro: User Interest Topic Model Con:
Evaluated on a single, small dataset

19. Thank you! Questions?