271,895 research outputs found
Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
On most sponsored search platforms, advertisers bid on some keywords for
their advertisements (ads). Given a search request, ad retrieval module
rewrites the query into bidding keywords, and uses these keywords as keys to
select Top N ads through inverted indexes. In this way, an ad will not be
retrieved even if queries are related when the advertiser does not bid on
corresponding keywords. Moreover, most ad retrieval approaches regard rewriting
and ad-selecting as two separated tasks, and focus on boosting relevance
between search queries and ads. Recently, in e-commerce sponsored search more
and more personalized information has been introduced, such as user profiles,
long-time and real-time clicks. Personalized information makes ad retrieval
able to employ more elements (e.g. real-time clicks) as search signals and
retrieval keys, however it makes ad retrieval more difficult to measure ads
retrieved through different signals. To address these problems, we propose a
novel ad retrieval framework beyond keywords and relevance in e-commerce
sponsored search. Firstly, we employ historical ad click data to initialize a
hierarchical network representing signals, keys and ads, in which personalized
information is introduced. Then we train a model on top of the hierarchical
network by learning the weights of edges. Finally we select the best edges
according to the model, boosting RPM/CTR. Experimental results on our
e-commerce platform demonstrate that our ad retrieval framework achieves good
performance
MIHash: Online Hashing with Mutual Information
Learning-based hashing methods are widely used for nearest neighbor
retrieval, and recently, online hashing methods have demonstrated good
performance-complexity trade-offs by learning hash functions from streaming
data. In this paper, we first address a key challenge for online hashing: the
binary codes for indexed data must be recomputed to keep pace with updates to
the hash functions. We propose an efficient quality measure for hash functions,
based on an information-theoretic quantity, mutual information, and use it
successfully as a criterion to eliminate unnecessary hash table updates. Next,
we also show how to optimize the mutual information objective using stochastic
gradient descent. We thus develop a novel hashing method, MIHash, that can be
used in both online and batch settings. Experiments on image retrieval
benchmarks (including a 2.5M image dataset) confirm the effectiveness of our
formulation, both in reducing hash table recomputations and in learning
high-quality hash functions.Comment: International Conference on Computer Vision (ICCV), 201
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Exploring User Interface Improvements for Software Developers who are Blind
Software developers who are blind and interact with the computer non-visually face unique challenges with information retrieval. We explore the use of speech and Braille combined with software to provide an improved interface to aid with challenges associated with information retrieval. We motivate our design on common tasks performed by students in a software development course using a Microprocessor without Interlocked Pipeline Stages (MIPS) architecture simulation tool. We test our interface via a single-subject longitudinal study, and we measure and show improvement in both the user’s performance and the user experience
Building economic models and measures of search
Economics provides an intuitive and natural way to formally represent the costs and benefits of interacting with applications, interfaces and devices. By using economic models it is possible to reason about interaction, make predictions about how changes to the system will affect behavior, and measure the performance of people's interactions with the system. In this tutorial, we first provide an overview of relevant economic theories, before showing how they can be applied to formulate different ranking principles to provide the optimal ranking to users. This is followed by a session showing how economics can be used to model how people interact with search systems, and how to use these models to generate hypotheses about user behavior. The third session focuses on how economics has been used to underpin the measurement of information retrieval systems and applications using the C/W/L framework (which reports the expected utility, expected total utility, expected total cost, and so on) - and how different models of user interaction lead to different metrics. We then show how information foraging theory can be used to measure the performance of an information retrieval system - connecting the theory of how people search with how we measure it. The final session of the day will be spent building economic models and measures of search. Here sample problems will be provided to challenge participants, or participants can bring their own
A Balanced Memory-Based Collaborative Filtering Similarity Measure.
Collaborative filtering recommender systems contribute to alleviating the problem of information overload that exists on the Internet as a result of the mass use of Web 2.0 applications. The use of an adequate similarity measure becomes a determining factor in the quality of the prediction and recommendation results of the recommender system, as well as in its performance. In this paper, we present a memory-based collaborative filtering similarity measure that provides extremely high-quality and balanced results; these results are complemented with a low processing time (high performance), similar to the one required to execute traditional similarity metrics. The experiments have been carried out on the MovieLens and Netflix databases, using a representative set of information retrieval quality measures
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