1. Diversifying Search Results Rakesh AgrawalSreenivas GollapudiSearch LabsMicrosoft Research rakesha@microsoft.comsreenig@microsoft.com Alan HalversonSamuel IeongSearch LabsMicrosoft Research alanhal@microsoft.comsaieong@microsoft.com WSDM ’09

2. Outline Introduction Problem Formulation A Greedy Algorithm for DIVERSIFY(K) Performance Metrics Evaluation Conclusions

3. Introduction Minimize the risk of dissatisfaction of the average user Assume that there exist – a taxonomy – a model user intents Consider both the relevance of the documents and diversity of the search result Tradeoff relevance and diversity

4. Problem Formulation The number of results to show for each category according to the percentage of users interested in that category may perform poorly Example : Flash – Technology : 0.6

5. Problem Formulation Non-order Our algorithm is also designed to generate an ordering of results rather than just a set of results

6. Problem Formulation DIVERSIFY(k) is NP-hard Optimal for DIVERSIFY (k-1) need not be a subset of documents optimal for DIVERSIFY (k) Example : p(c 1 |q)=p(c 2 |q)=0.5 DIVERSIFY(1):d 1,d 2,d 3 DIVERSIFY(2):d 2,d 3,d 1

7. A Greedy Algorithm for DIVERSIFY(K)

8. Performance Metrics NDCG,MRR,MAP do not take into account the value of diversification Intent Aware Measure example: p(c 2 |q)>>p(c 1 |q) d 1 is Excellent for c 1 (but unrelated to c 2 ) d 2 is Good for c 2 (but unrelated to c 1 ) Classical IR metrics:d 1,d 2 Intent aware measures:d 2,d 1

9. Intent Aware Measure

10. Evaluation Evaluate our approach against three commercial search engine Conduct three sets of experiments Differ in how the distributions of intents and how the relevance of the documents are obtained

11. Experiment 1 The distributions of intents for both queries and documents via standard classifiers The relevance of documents from a proprietary repository of human judgements that we have been granted access to Dataset : 10,000 random queries with top 50 documents Many documents are assigned human judgments in the top 10 for each query

12. Experiment 1 sample about 900 queries – at least two categories – a significant fraction of associated documents have human judgments

13. Experiment 1

14. Experiment 2 Obtain the distributions of intents for queries and the document relevance using the Amazon Mechanical Turk platform Sample 200 queries from the dataset – at least three categories Submit these queries along with the three most likely categories as estimated by the classier and the top five results produced by IA-Select to the Turks

15. Experiment 2

16. Experiment 3 IA-Select : p(c|q) from Amazon Mechanical Turk platform Metrics : p(c|q) and relevance documents are the same as used in Experiment 1

17. Conclusions Provide a greedy algorithm with good approximation gurantees To evaluate the effectiveness of our approach, we proposed generalizations of well-studied metrics to take into account of the intentions of the users Our approach outperforms results produced by commercial search engines over all of the metrics