Context Models For Web Search Personalization

We present our solution to the Yandex Personalized Web Search Challenge. The aim of this challenge was to use the historical search logs to personalize top-N document rankings for a set of test users. We used over 100 features extracted from user- and query-depended contexts to train neural net and tree-based learning-to-rank and regression models. Our final submission, which was a blend of several different models, achieved an NDCG@10 of 0.80476 and placed 4'th amongst the 194 teams winning 3'rd prize

The role of memory and restricted context in repeated visual search

Previous studies have shown that the efficiency of visual search does not improve when participants search through the same unchanging display for hundreds of trials (repeated search), even though the participants have a clear memory of the search display. In this article, we ask two important questions. First, why do participants not use memory to help search the repeated display? Second, can context be introduced so that participants are able to guide their attention to the relevant repeated items? Experiments 1–4 show that participants choose not to use a memory strategy because, under these conditions, repeated memory search is actually less efficient than repeated visual search, even though the latter task is in itself relatively inefficient. However, when the visual search task is given context, so that only a subset of the items are ever pertinent, participants can learn to restrict their attention to the relevant stimuli (Experiments 5 and 6)

Context Information Based Initial Cell Search for Millimeter Wave 5G Cellular Networks

Millimeter wave (mmWave) communication is envisioned as a cornerstone to fulfill the data rate requirements for fifth generation (5G) cellular networks. In mmWave communication, beamforming is considered as a key technology to combat the high path-loss, and unlike in conventional microwave communication, beamforming may be necessary even during initial access/cell search. Among the proposed beamforming schemes for initial cell search, analog beamforming is a power efficient approach but suffers from its inherent search delay during initial access. In this work, we argue that analog beamforming can still be a viable choice when context information about mmWave base stations (BS) is available at the mobile station (MS). We then study how the performance of analog beamforming degrades in case of angular errors in the available context information. Finally, we present an analog beamforming receiver architecture that uses multiple arrays of Phase Shifters and a single RF chain to combat the effect of angular errors, showing that it can achieve the same performance as hybrid beamforming

CONTEXT-BASED AUTOSUGGEST ON GRAPH DATA

Autosuggest is an important feature in any search applications. Currently, most applications only suggest a single term based on how frequent that term appears in the indexed documents or how often it is searched upon. These approaches might not provide the most relevant suggestions because users often enter a series of related query terms to answer a question they have in mind. In this project, we implemented the Smart Solr Suggester plugin using a context-based approach that takes into account the relationships among search keywords. In particular, we used the keywords that the user has chosen so far in the search text box as the context to autosuggest their next incomplete keyword. This context-based approach uses the relationships between entities in the graph data that the user is searching on and therefore would provide more meaningful suggestions

Integration of Exploration and Search: A Case Study of the M3 Model

International audienceEffective support for multimedia analytics applications requires exploration and search to be integrated seamlessly into a single interaction model. Media metadata can be seen as defining a multidimensional media space, casting multimedia analytics tasks as exploration, manipulation and augmentation of that space. We present an initial case study of integrating exploration and search within this multidimensional media space. We extend the M3 model, initially proposed as a pure exploration tool, and show that it can be elegantly extended to allow searching within an exploration context and exploring within a search context. We then evaluate the suitability of relational database management systems, as representatives of today’s data management technologies, for implementing the extended M3 model. Based on our results, we finally propose some research directions for scalability of multimedia analytics

Limits on Four-Top Production from the ATLAS Same-sign Top-quark Search

We repurpose the recent ATLAS search for same-sign top quarks in data with 1.0 fb$^{-1}$ in the context of a search for production of four top quarks. Using the null results of that search, we place limits on the four-top-quark production cross section of about 1 pb. These limits are larger than the expected Standard Model rate for four-top-quark production, but are already strong enough to place interesting constraints on models which enhance that rate. We interpret these results in the context of models in which the right-handed top quark is composite and find limits on the compositeness scale of about 700 GeV.Comment: 4 pages, 4 figure