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Unified relevance models for information retrieval
- Institution: University College London, UK
- Supervisor:
Jun Wang
- To apply, please visit: http://www.ucl.ac.uk/...
- Note: nationality restrictions apply*
- Abstract: In information retrieval, relevance is a very
important concept and has been heavily studied. There are two
different views on how to assign a probability of relevance of a
document to a user need, namely document-oriented and query-oriented
views. The classic probability retrieval model of information
retrieval takes the query-oriented view while the language modelling
approach to information retrieval builds upon the document-oriented
view. However, neither view represents the problem of information
retrieval completely. This proposed PhD research programme aims at
breaking the partial views existing in current retrieval models, and
formally develop a new retrieval paradigm that explores more of the
dependency in the data and unifies evidence from different
information sources to improve retrieval performance. To achieve our
goal, we will 1) explore the dependency between users who have
similar information needs, 2) combine evidence from different
information sources, and 3) apply Bayesian probabilistic approaches
to model the unified relevance.
Understanding resource-constrained information
retrieval
- Institution: University College London, UK
- Supervisor:
Ingemar Cox
- To apply, please visit:
http://www.ucl.ac.uk/...
- Note: nationality restrictions apply*
- Abstract: Search engines need to index a huge amount of data
(billions of web pages) and must deal with very high query loads
(100’s of thousands of requests per hour). This places serious
strains on the underlying computer systems. This proposal seeks to
develop a theoretical framework in which to understand the tradeoffs
between performance and cost, where performance is measured with
respect to the quality of the retrieved results and cost is a
function of hardware costs and economic costs associated with
degraded performance. The theoretical developments will be supported
by empirical studies to both verify the theoretical models and to
guide the developments of improved models.
* Some Scholarships are offered as
Dorothy Hodgkin Postgraduate
Awards and only students from the following countries are eligible:
Afghanistan, Albania, Algeria, Angola, Anguilla, Antigua and Barbuda,
Armenia, Argentina, Azerbaijan, Bahrain, Bangladesh, Barbados, Belize,
Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil,
Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Central African
Republic, Chad, Chile, China, Colombia, Comoros, Congo, Cook Islands,
Costa Rica, Côte d'Ivoire, Croatia, Cuba, Democratic Republic of Congo,
Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador,
Equatorial Guinea, Eritrea, Ethiopia, Fiji, Gabon, Gambia, Georgia,
Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti,
Honduras, Hong Kong, India, Indonesia, Iran, Iraq, Jamaica, Jordan,
Kazakhstan, Kenya, Kiribati, Korea, Laos, Lebanon, Lesotho, Liberia, FYR
Macedonia, Madagascar, Malawi, Malaysia, Maldives, Mali, Marshall
Islands, Mauritania, Mauritius, Mayotte, Mexico, Micronesia, Moldova,
Mongolia, Montenegro, Montserrat, Morocco, Mozambique, Myanmar, Namibia,
Nauru, Nepal, Nicaragua, Niger, Nigeria, Niue, Oman, Pakistan, Palau
Islands, Palestinian Administered Areas, Panama, Papua New Guinea,
Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and
Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone,
Solomon Islands, Somalia, South Africa, Sri Lanka, St Helena, St Kitts
and Nevis, St Lucia, St Vincent & Grenadines, Sudan, Suriname,
Swaziland, Syria, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo,
Tokelau, Trinidad and Tobago, Tonga, Tunisia, Turkey, Turkmenistan,
Tuvalu, Turks and Caicos Islands, Uganda, Uruguay, Uzbekistan, Vanuatu,
Venezuela, Vietnam, Wallis and Futuna, Yemen, Zambia, Zimbabwe.
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