NDLI: Data design for personalization: current challenges and emerging opportunities

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Data that matter: opportunities in crisis informatics research

Improving the efficiency of multi-site web search engines

Relative confidence sampling for efficient on-line ranker evaluation

Estimating ad group performance in sponsored search

Discovering common motifs in cursor movement data for improving web search

Taxonomy discovery for personalized recommendation

Improving pairwise learning for item recommendation from implicit feedback

Detecting cohesive and 2-mode communities indirected and undirected networks

Prediction in a microblog hybrid network using bonacich potential

Chinese-English mixed text normalization

Spatial compactness meets topical consistency: jointly modeling links and content for community detection

Inferring the impacts of social media on crowdfunding

Integration of large scale knowledge bases using probabilistic graphical models

Behavioral data mining and network analysis in massive online games

Log-based personalization: the 4th web search click data (WSCD) workshop

A self-adapting latency/power tradeoff model for replicated search engines

Adapting deep RankNet for personalized search

Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising

Modeling dwell time to predict click-level satisfaction

Who likes it more?: mining worth-recommending items from long tails by modeling relative preference

Personalized entity recommendation: a heterogeneous information network approach

FENNEL: streaming graph partitioning for massive scale graphs

Learning social network embeddings for predicting information diffusion

Sentiment analysis on evolving social streams: how self-report imbalances can help

Scalable topic-specific influence analysis on microblogs

Understanding and promoting micro-finance activities in Kiva.org

Strategy in action: analyzing online search behavior bymining search strategies

Exploration and mining of web repositories

Web-scale classification: web classification in the big data era

Heterogeneous graph-based intent learning with queries, web pages and Wikipedia concepts

Search engine click spam detection based on bipartite graph propagation

An efficient framework for online advertising effectiveness measurement and comparison

User modeling in search logs via a nonparametric bayesian approach

On building entity recommender systems using user click log and freebase knowledge

Social collaborative retrieval

A few good predictions: selective node labeling in a social network

Modeling opinion dynamics in social networks

Entity linking at the tail: sparse signals, unknown entities, and phrase models

WebChild: harvesting and organizing commonsense knowledge from the web

Who watches (and shares) what on youtube? and when?: using twitter to understand youtube viewership

On discovering non-obvious recommendations: using unexpectedness and neighborhood selection methods in collaborative filtering systems

Big graph mining for the web and social media: algorithms, anomaly detection, and applications

1st workshop on diffusion networks and cascade analytics

Exploiting user disagreement for web search evaluation: an experimental approach

Sampling dilemma: towards effective data sampling for click prediction in sponsored search

LASER: a scalable response prediction platform for online advertising

The last click: why users give up information network navigation

Transferring heterogeneous links across location-based social networks

Active learning for networked data based on non-progressive diffusion model

Fast approximation of betweenness centrality through sampling

Latent dirichlet allocation based diversified retrieval for e-commerce search

Using linked data to mine RDF from wikipedia's tables

Detecting non-gaussian geographical topics in tagged photo collections

Exploratory search with semantic transformations using collaborative knowledge bases

Diversity and novelty in web search, recommender systems and data streams

Workshop on large-scale and distributed systems for information retrieval (LSDS-IR 2014)

Improving search relevance for short queries in community question answering

Exploiting contextual factors for click modeling in sponsored search

Lessons from the journey: a query log analysis of within-session learning

Customized tour recommendations in urban areas

Learning latent representations of nodes for classifying in heterogeneous social networks

Effective co-betweenness centrality computation

Supervised N-gram topic model

Knowledge-based graph document modeling

Ranking in heterogeneous social media

Search by multiple examples

Multilingual probabilistic topic modeling and its applications in web mining and search

Data design for personalization: current challenges and emerging opportunities

Struggling or exploring?: disambiguating long search sessions

Predicting response in mobile advertising with hierarchical importance-aware factorization machine

Scalable K-Means by ranked retrieval

Going beyond Corr-LDA for detecting specific comments on news & blogs

Trust, but verify: predicting contribution quality for knowledge base construction and curation

Visualizing brand associations from web community photos

Entity linking and retrieval for semantic search

Democracy is good for ranking: towards multi-view rank learning and adaptation in web search

Partner tiering in display advertising

Nonparametric bayesian upstream supervised multi-modal topic models

Modelling growth of urban crowd-sourced information

Is a picture really worth a thousand words?: - on the role of images in e-commerce

Data design for personalization: current challenges and emerging opportunities

Content Provider	ACM Digital Library
Author	Das Sarma, Atish Churchill, Elizabeth F.
Abstract	There are several definitions of personalization but one that relates specifically to internet technologies is the following: Personalization technology enables the dynamic insertion, customization or suggestion of content in any format that is relevant to the individual user, based on the user's implicit behavior and preferences, and explicitly given details. Personalization is central to most Internet experiences. Personalization is a data-driven process, whether the data are explicitly gathered (e.g., by asking people to fill out forms) or implicitly (e.g. through analysis of behavioral data). It is clear that designing for effective personalization poses interesting engineering and computer science challenges. However, personalization is also a user experience issue. We believe that encouraging dialogue and collaboration between data mining experts, content providers, and user-focused researchers will offer gains in the area of personalization for search and for other domains. This is increasingly the case as devices enable more forms of data to be gathered, are always on/connected and are always with users. This workshop brings researchers interested in the area of personalization to share their research, explore possibilities for collaboration, and work on defining an agenda for Data Design for Personalization.
Starting Page	693
Ending Page	694
Page Count	2
File Format	PDF
ISBN	9781450323512
DOI	10.1145/2556195.2556211
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Personalization Search Data design
Content Type	Text
Resource Type	Article

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