Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user's judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the first based on statistical language models and the second based on the binary independence probabilistic model. The paper shows the resemblance of the approaches to relevance feedback of these models, introduces new approaches to relevance feedback for both models, and evaluates the new relevance feedback algorithms on the TREC collection. The paper shows that there are no significant differences between simple and sophisticated approaches to relevance feedback.
|Number of pages||6|
|Publication status||Published - Jun 2001|
|Event||Joint DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries - Dublin|
Duration: 18 Jun 2001 → 20 Jun 2001
|Workshop||Joint DELOS-NSF Workshop on Personalisation and Recommender Systems in Digital Libraries|
|Period||18/06/01 → 20/06/01|
|Other||18-20 June 2001|