Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Contextual mobile local search based on social network vitality information

Abstract: A system, apparatus, and method are directed to managing contextual based mobile searches. A context oriented user interface interprets inputs from a mobile user based on vitality information. In one embodiment, the input may be interpreted as a request to perform a context-based search over a network using at least some of the vitality information. Vitality information may include a location of the mobile device, a time of day, an event, information from the mobile user's calendar, past behavior of the mobile user, weather, social networking data, aggregate behaviors, or even information about proximity of a social contact. By employing vitality information to perform a mobile search, better search results and a richer user experience may be provided that includes a sense of community, a sense of presence (e.g., a sense of 'here-ness.'). In one embodiment, the mobile user may provide comments to others regarding the search results.

Method and apparatus for identifying related searches in a database search system

Abstract: A method of generating a search result list also provides related searches for use by a searcher. Search listings which generate a match with a search request submitted by the searcher are identified in a pay-for-performance database which includes a plurality of search listings. Related search listings contained in a related search database generated from the pay-for-performance database are identified as relevant to the search request. A search result list is returned to the searcher including the identified search listings and one or more of the identified search listings.

Universally utility-maximizing privacy mechanisms

Abstract: A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Publishing fully accurate information maximizes utility while minimizing privacy, while publishing random noise accomplishes the opposite. Privacy can be rigorously quantified using the framework of differential privacy, which requires that a mechanism's output distribution is nearly the same whether or not a given database row is included or excluded. The goal of this paper is strong and general utility guarantees, subject to differential privacy. We pursue mechanisms that guarantee near-optimal utility to every potential user, independent of its side information (modeled as a prior distribution over query results) and preferences (modeled via a loss function). Our main result is: for each fixed count query and differential privacy level, there is a geometric mechanism M* -- a discrete variant of the simple and well-studied Laplace mechanism -- that is simultaneously expected loss-minimizing for every possible user, subject to the differential privacy constraint. This is an extremely strong utility guarantee: every potential user u, no matter what its side information and preferences, derives as much utility from M* as from interacting with a differentially private mechanism Mu that is optimally tailored to u. More precisely, for every user u there is an optimal mechanism Mu for it that factors into a user-independent part (the geometric mechanism M*) followed by user-specific post-processing that can be delegated to the user itself. The first part of our proof of this result characterizes the optimal differentially private mechanism for a fixed but arbitrary user in terms of a certain basic feasible solution to a linear program with constraints that encode differential privacy. The second part shows that all of the relevant vertices of this polytope (ranging over all possible users) are derivable from the geometric mechanism via suitable remappings of its range.

Systems and methods for generating concept units from search queries

Abstract: Systems and method for enhancing search functionality provided to a user. In certain aspects, a query processing engine automatically decomposes queries into constituent units that are related to concepts in which a user may be interested. The query processing engine decomposes queries into one or more constituent units per query using statistical methods. In certain aspects, no real world knowledge is used in determining units. In other aspects, aspects of world and content knowledge are introduced to enhance and optimize performance, for example, manually using a team of one or more information engineers.

Collaborative filtering and the missing at random assumption

Abstract: Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR). In this paper we present the results of a user study in which we collect a random sample of ratings from current users of an online radio service. An analysis of the rating data collected in the study shows that the sample of random ratings has markedly different properties than ratings of user-selected songs. When asked to report on their own rating behaviour, a large number of users indicate they believe their opinion of a song does affect whether they choose to rate that song, a violation of the MAR condition. Finally, we present experimental results showing that incorporating an explicit model of the missing data mechanism can lead to significant improvements in prediction performance on the random sample of ratings.