NDLI: Compact Snippet Caching for Flash-based Search Engines

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Salton Award Lecture: People, Interacting with Information

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Dynamic Query Modeling for Related Content Finding

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Multiple Social Network Learning and Its Application in Volunteerism Tendency Prediction

Relevance Scores for Triples from Type-Like Relations

Bayesian Ranker Comparison Based on Historical User Interactions

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Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings

Retrieval of Relevant Opinion Sentences for New Products

Learning to Extract Local Events from the Web

Optimised Scheduling of Online Experiments

Inferring Searcher Attention by Jointly Modeling User Interactions and Content Salience

Leveraging Procedural Knowledge for Task-oriented Search

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Representative & Informative Query Selection for Learning to Rank using Submodular Functions

Learning to Reweight Terms with Distributed Representations

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Image-Based Recommendations on Styles and Substitutes

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Splitting Water: Precision and Anti-Precision to Reduce Pool Bias

Learning by Example: Training Users with High-quality Query Suggestions

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Influence of Vertical Result in Web Search Examination

HSpam14: A Collection of 14 Million Tweets for Hashtag-Oriented Spam Research

Fielded Sequential Dependence Model for Ad-Hoc Entity Retrieval in the Web of Data

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Effective Latent Models for Binary Feedback in Recommender Systems

Listwise Collaborative Filtering

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Personalizing Search on Shared Devices

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A Probabilistic Model for Information Retrieval Based on Maximum Value Distribution

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StarSum: A Simple Star Graph for Multi-document Summarization

VenueMusic: A Venue-Aware Music Recommender System

Cross-Platform Question Routing for Better Question Answering

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Incremental Sampling of Query Logs

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Semi-supervised Hashing with Semantic Confidence for Large Scale Visual Search

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adaQAC: Adaptive Query Auto-Completion via Implicit Negative Feedback

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Personalized Recommendation via Parameter-Free Contextual Bandits

BROOF: Exploiting Out-of-Bag Errors, Boosting and Random Forests for Effective Automated Classification

Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search

Parametric and Non-parametric User-aware Sentiment Topic Models

GeoSoCa: Exploiting Geographical, Social and Categorical Correlations for Point-of-Interest Recommendations

Sequential Testing for Early Stopping of Online Experiments

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The Benefits of Magnitude Estimation Relevance Assessments for Information Retrieval Evaluation

Non-Compositional Term Dependence for Information Retrieval

User Variability and IR System Evaluation

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An Introduction to Click Models for Web Search: SIGIR 2015 Tutorial

SIGIR 2015 Workshop on Reproducibility, Inexplicability, and Generalizability of Results (RIGOR)

Search Engine Evaluation based on Search Engine Switching Prediction

CricketLinking: Linking Event Mentions from Cricket Match Reports to Ball Entities in Commentaries

Controversy Detection and Stance Analysis

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IR Evaluation: Modeling User Behavior for Measuring Effectiveness

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Time-Aware Authorship Attribution for Short Text Streams

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A Priori Relevance Based On Quality and Diversity Of Social Signals

ERICA: Expert Guidance in Validating Crowd Answers

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Location in Search

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Online News Tracking for Ad-Hoc Queries

Retrieval from Noisy E-Discovery Corpus in the Absence of Training Data

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Opinion Spammer Detection in Web Forum

Multi-Faceted Recall of Continuous Active Learning for Technology-Assisted Review

Time Pressure and System Delays in Information Search

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A Head-Weighted Gap-Sensitive Correlation Coefficient

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Modularity-Based Query Clustering for Identifying Users Sharing a Common Condition

Understanding Temporal Query Intent

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Evaluating Retrieval Models through Histogram Analysis

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Regularised Cross-Modal Hashing

Adapted B-CUBED Metrics to Unbalanced Datasets

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A Test Collection for Spoken Gujarati Queries

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On the Cost of Phrase-Based Ranking

Location-Aware Model for News Events in Social Media

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Tailoring Music Recommendations to Users by Considering Diversity, Mainstreaminess, and Novelty

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Probabilistic Multileave for Online Retrieval Evaluation

Twitter Sentiment Analysis with Deep Convolutional Neural Networks

Anchoring and Adjustment in Relevance Estimation

Cognitive Activity during Web Search

Personalized Semantic Ranking for Collaborative Recommendation

Active Learning for Entity Filtering in Microblog Streams

Relevance-aware Filtering of Tuples Sorted by an Attribute Value via Direct Optimization of Search Quality Metrics

Multi-source Information Fusion for Personalized Restaurant Recommendation

Joint Matrix Factorization and Manifold-Ranking for Topic-Focused Multi-Document Summarization

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Early Detection of Topical Expertise in Community Question Answering

LBMCH: Learning Bridging Mapping for Cross-modal Hashing

Gibberish, Assistant, or Master?: Using Tweets Linking to News for Extractive Single-Document Summarization

Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization

Adaptive User Engagement Evaluation via Multi-task Learning

Compact Snippet Caching for Flash-based Search Engines

When Personalization Meets Conformity: Collective Similarity based Multi-Domain Recommendation

Sub-document Timestamping of Web Documents

Compact Snippet Caching for Flash-based Search Engines

Content Provider	ACM Digital Library
Author	Sun, Pengyu Stones, Rebecca Jane Wang, Gang Liu, Xiaoguang Zhang, Rui Tong, Jiancong
Abstract	In response to a user query, search engines return the top-k relevant results, each of which contains a small piece of text, called a snippet, extracted from the corresponding document. Obtaining a snippet is time consuming as it requires both document retrieval (disk access) and string matching (CPU computation), so caching of snippets is used to reduce latency. With the trend of using flash-based solid state drives (SSDs) instead of hard disk drives for search engine storage, the bottleneck of snippet generation shifts from I/O to computation. We propose a simple, but effective method for exploiting this trend, which we call fragment caching: instead of caching the whole snippet, we only cache snippet metadata which describe how to retrieve the snippet from the document. While this approach increases I/O time, the cost is insignificant on SSDs. The major benefit of fragment caching is the ability to cache the same snippets (without loss of quality) while only using a fraction of the memory the traditional method requires. In our experiments, we find around 10 times less memory is required to achieve comparable snippet generation times for dynamic memory, and we consistently achieve a vastly greater hit ratio for static caching.
Starting Page	1015
Ending Page	1018
Page Count	4
File Format	PDF
ISBN	9781450336215
DOI	10.1145/2766462.2767764
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-08-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Solid state drive Snippet Cache
Content Type	Text
Resource Type	Article

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