Mining interests for user profiling in electronic conversations

The increasing amount of Web-based tasks is currently requiring personalization strategies to improve the user experience. However, building user profiles is a hard task, since users do not usually give explicit information about their interests. Therefore, interests must be mined implicitly from electronic sources, such as chat and discussion forums. In this work, we present a novel method for topic detection from online informal conversations. Our approach combines: (i) Wikipedia, an extensive source of knowledge, (ii) a concept association strategy, and (iii) a variety of text-mining techniques, such as POS tagging and named entities recognition. We performed a comparative evaluation procedure for searching the optimal combination of techniques, achieving encouraging results.

► We present a technique for inferring the interests of a user when working online.
► The unsupervised technique uses a set of NLP tasks, and Wikipedia as a dictionary.
► The empirical evaluation suggests a suitable combination of NLP techniques for the task.
► We improved the precision by adjusting the dictionary to a specific knowledge area.