The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Strengthening integrality gaps for capacitated network design and covering problems
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Less is more: probabilistic models for retrieving fewer relevant documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving personalized web search using result diversification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning diverse rankings with multi-armed bandits
Proceedings of the 25th international conference on Machine learning
Novelty and diversity in information retrieval evaluation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
Proceedings of the forty-first annual ACM symposium on Theory of computing
Efficient Computation of Diverse Query Results
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A constant factor approximation algorithm for generalized min-sum set cover
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
A unified approach to ranking in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Max-Sum diversification, monotone submodular functions and dynamic updates
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Top-k diversity queries over bounded regions
ACM Transactions on Database Systems (TODS)
| Hi-index | 0.00 |
A fundamental issue in Web search is ranking search results based on user logs, since different users may have different preferences and intents with regards to a search query. Also, in many search query applications, users tend to look at only the top part of the ranked result list in order to find relevant documents. The setting we consider contains various types of users, each of which is interested in a subset of the search results. The goal is to rank the search results of a query providing highly ranked relevant results. Our performance measure is the discounted cumulative gain which offers a graded relevance scale of documents in a search engine result set, and measures the usefulness (gain) of a document based on its position in the result list. Based on this measure, we suggest a general approach to developing approximation algorithms for ranking search results that captures different aspects of users' intents. We also take into account that the relevance of one document cannot be treated independently of the relevance of other documents in a collection returned by a search engine. We first consider the scenario where users are interested in only a single search result (e.g., navigational queries). We then develop a polynomial time approximation scheme for this case. We further consider the general case where users have different requirements on the number of search results, and develop efficient approximation algorithms. Finally, we consider the problem of choosing the top k out of n search results and show that for this problem (1-1/e) is indeed the best approximation factor achievable, thus separating the approximability of the two versions of the problem.