Penguins in sweaters, or serendipitous entity search on user-generated content

@article{Bordino2013PenguinsIS,
  title={Penguins in sweaters, or serendipitous entity search on user-generated content},
  author={Ilaria Bordino and Yelena Mejova and Mounia Lalmas},
  journal={Proceedings of the 22nd ACM international conference on Information \& Knowledge Management},
  year={2013}
}
In many cases, when browsing the Web users are searching for specific information or answers to concrete questions. Sometimes, though, users find unexpected, yet interesting and useful results, and are encouraged to explore further. What makes a result serendipitous? We propose to answer this question by exploring the potential of entities extracted from two sources of user-generated content -- Wikipedia, a user-curated online encyclopedia, and Yahoo! Answers, a more unconstrained question… 

Figures and Tables from this paper

"Driving curiosity in search with large-scale entity networks" by Ilaria Bordino, Mounia Lalmas, Yelena Mejova, and Olivier Van Laere with Martin Vesely as coordinator
TLDR
An entity search system that explores the potential of entities extracted from two of the most popular sources of user-generated content -- Wikipedia, a user-curated online encyclopedia, and Yahoo Answers, a more unconstrained question & answering forum -- in promoting serendipitous search.
DEESSE: entity-Driven Exploratory and sErendipitous Search SystEm
We present DEESSE [1], a tool that enables an exploratory and serendipitous exploration - at entity level, of the content of two different social media: Wikipedia, a user-curated online encyclopedia,
Beyond entities: promoting explorative search with bundles
TLDR
This work proposes to enhance an explorative search system that represents a large sample of Yahoo Answers as an entity network, with a result structuring that goes beyond ranked lists, using composite entity retrieval, which requires a bundling of the results.
Learning to Recommend Related Entities With Serendipity for Web Search Users
TLDR
A learning to recommend framework that consists of two components: related entity finding and candidate entity ranking is proposed that significantly outperforms several strong baseline methods and can significantly improve user engagement against multiple baseline methods.
Leveraging Knowledge Bases for Contextual Entity Exploration
TLDR
A system called Lewis is presented for retrieving contextually relevant entity results leveraging a knowledge graph, and a large scale crowdsourcing experiment is performed, which shows that Lewis can outperform the state-of-the-art contextual entity recommendation systems by more than 20% in terms of the MAP score.
Enriching News Articles with Related Search Queries
TLDR
This work presents a three-phase retrieval framework for query recommendation that incorporates various article-dependent and article-independent relevance signals and reveals interesting characteristics of the type of queries users tend to click and the nature of their interaction with the resultant search engine results page.
Content Explorer: Recommending Novel Entities for a Document Writer
TLDR
An evaluation metric is proposed and an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set is performed to demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models.
In Situ Insights
TLDR
A selection-centric context language model and a selection-focused context semantic model are proposed to capture user interest and measure the quality of a reference concept across three aspects: selection clarity, context coherence, and concept relevance, and a machine learning approach is put forward to decide if a selection is noisy, and filter out low-quality candidate references.
Probabilistic Prototype Model for Serendipitous Property Mining
TLDR
This paper proposes to leverage probabilistic approach to build a prototype that can overcome noise in the knowledge base and shows higher relevance than the traditional relevance-pursuing baseline using TF-IDF.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 44 REFERENCES
Serendipitous Browsing: Stumbling through Wikipedia
TLDR
It is hypothesize that a greater understanding of what makes certain Wikipedia articles more attractive to the serendipitously browsing user than others, will enable us to develop adaptations that expose a greater amount of Wikipedia articles to the leisure seeking user.
From machu_picchu to "rafting the urubamba river": anticipating information needs via the entity-query graph
TLDR
A novel method is introduced that is based on the content of the page visited, rather than on past browsing patterns as in previous literature, that produces relevant and interesting recommendations, and outperforms an alternative method based on reverse IR.
Learning document aboutness from implicit user feedback and document structure
TLDR
This work uses implicit user feedback available in search engine click logs to characterize the user-perceived notion of term relevance, and presents a machine learning approach that learns to score and rank words and phrases in a document according to their relevance to the document.
On the evolution of the yahoo! answers QA community
TLDR
This poster investigates the temporal evolution of a popular QA community - Yahoo! Answers, with respect to its effectiveness in answering three basic types of questions: factoid, opinion and complex questions, and shows that Yahoo!swers keeps growing rapidly, while its overall quality as an information source for factoid question-answering degrades.
EntityRank: Searching Entities Directly and Holistically
TLDR
This work focuses on the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking.
A large-scale sentiment analysis for Yahoo! answers
TLDR
This work uses a sentiment extraction tool to investigate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sentiments in the context of a large online question answering site.
TweetMotif: Exploratory Search and Topic Summarization for Twitter
TLDR
This work presents TweetMotif, an exploratory search application for Twitter that groups messages by frequent significant terms — a result set’s subtopics — which facilitate navigation and drilldown through a faceted search interface.
Liquid query: multi-domain exploratory search on the web
TLDR
The Liquid Query paradigm is proposed, to support users in finding responses to multi-domain queries through exploratory information seeking across structured information sources, wrapped by means of a uniform notion of search service.
Collective annotation of Wikipedia entities in web text
TLDR
This work gives formulations for the trade-off between local spot-to-entity compatibility and measures of global coherence between entities, and investigates practical solutions based on local hill-climbing, rounding integer linear programs, and pre-clustering entities followed by local optimization within clusters.
Social search and discovery using a unified approach
TLDR
This research describes a social search engine positioned within a large enterprise, applied over social data gathered from several Web 2.0 applications, based on multifaceted search, which provides an efficient update mechanism for relations between objects, as well as efficient search over the heterogeneous data.
...
1
2
3
4
5
...