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SIGIR '89 Proceedings of the 12th annual international ACM SIGIR conference on Research and development in information retrieval
The automatic identification of stop words
Journal of Information Science
Probabilistic latent semantic indexing
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A Method for the Correction of Garbled Words Based on the Levenshtein Metric
IEEE Transactions on Computers
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CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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Knowing what potential clients want, is the most important issue for companies. The current situation of social communication is generating a lot of information about users, such as favourite sites, food, politic tendencies, hopes or needs. This information has an incalculable value for marketing interests but, unfortunately, it is not trivial to process it. One way to obtain this information without disturbing the users is to store their searches, used words on the Internet and clicked items, to construct specific profiles. But, the problem with these techniques is that they do not retrieve current information about the targeted user because they gather information that may or may not be up to date. In light of this background, we propose a methodology to obtain up to date information about user's interests, likes and needs by analysing users conversations in social networks and instant messaging systems to generate personalised and interesting advertisement with better impact and higher success rates.