The collobarating filtering through neighbourhood based  interpolation weights, which are used to estimate unknown ratings from neighboring known ones. Nevertheless, the literature lacks a rigorous way to derive these weights. This work  showed how the interpolation weights can be computed as a global solution to an optimization problem that precisely reflects their role. Comparison with past kNN methods on the Netflix data, demonstrated a significant improvement of prediction accuracy without a meaningful increase in running time. A kNN method can be most effectively employed in an item-oriented manner, by analyzing relationships between items.

Philipp Keller

Frank Smadja

Peter Allen

The demonstration of an automatic tagging system that learns new tags for a 3D model by comparing it to a large set of tagged models and probabilistically propagating tags from neighbors. They shown that autotagging to improve shape retrieval in a digital library, there are several other domains where automatically annotating 3D models can be helpfulthe discriminative power of these tags is comparable to that of the underlying geometric similarity distance, and that searching for models based on our autotags can result in better precision and greater recall than searching on the original tags.

Mohammad Zubair

Kurt Maly

Krysta Svore

Susan T. Dumais

In this work,demonstrated that topical class information can be used to improve the relevance of retrieval resultsby generalizing along the class dimension to identify other relevant results. In order to do this, they introduced a natural denition of query class that stems from the results that are relevant for that query and can be estimated using click behavior. Approach is notable for its focus on directly improving ranking relevance rather than indirect measures like query classication.

Craig Macdonald

Iadh Ounis

Introduced a novel probabilistic framework for search result diversification. In particular, the xQuAD (eXplicit Query Aspect Diversification) framework explicitly models the aspects underlying an initial query, in the form of sub-queries. Instead of comparing documents to one another—which usually demands expensive computations—our approach achieves an effective diversification performance by directly estimating the relevance
of the retrieved documents to multiple sub-queries. Besides being efficient in practice, the principled formulation of xQuAD naturally models several dimensions of interest in a  diversification task, as components within the framework. These include the relevance of a document to an initial query and its multiple aspects, identified as sub-queries, as well as the relative importance of each sub-query and how novel a document satisfying each sub-query is.

 loglikelihood

http://acl.ldc.upenn.edu/J/J93/J93-1003.pdf

http://portal.acm.org/citation.cfm?id=972454

Presented a particle swarm optimization technique to improve term extraction precision. they choose five features to represent the term score: domain relevance, domain consensus, term cohesion, first occurrence and length of noun phrase. In the experiments, we use a translation of the meaning of the Quran (focus on verses of prayer) as an input document, both for training and testing phases. separate the documents between training documents and test documents. Particles swarm optimization is trained using the training documents to determine the appropriate weight of each feature to produce the best score for each term. We conduct tests with the test document using the weight of each feature which is generated from the training stage to calculate the final score for each term to be extracted.