Multimedia and hypertext: the Internet and beyond
Multimedia and hypertext: the Internet and beyond
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cross-lingual relevance models
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Journal of Machine Learning Research
A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A Generative Theory of Relevance
A Generative Theory of Relevance
A Comparative Study of Utilizing Topic Models for Information Retrieval
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Cross-language linking of news stories on the web using interlingual topic modelling
Proceedings of the 2nd ACM workshop on Social web search and mining
Empirical study of topic modeling in Twitter
Proceedings of the First Workshop on Social Media Analytics
Recommender systems by means of information retrieval
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Text retrieval methods for item ranking in collaborative filtering
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Pinteresting: towards a better understanding of user interests
Proceedings of the 2012 workshop on Data-driven user behavioral modelling and mining from social media
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Improving LDA topic models for microblogs via tweet pooling and automatic labeling
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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User-generated content offers opportunities to learn about people's interests and hobbies. We can leverage this information to help users find interesting shops and businesses find interested users. However this content is highly noisy and unstructured as posted on social media sites and blogs. In this work we evaluate different textual representations and retrieval models that aim to make sense of social media data for retail applications. Our task is to link the text of pins (from Pinterest.com) to online shops (formed by clustering Amazon.com's products). Our results show that document representations that combine latent concepts with single words yield the best performance.