We present here the results for a comparative study of some techniques, available in the literature, related to the relevance feedback mechanism in the case of a short-term learning. Only one method among those considered here is belonging to the data mining field which is the K-nearest neighbors algorithm (KNN) while the rest of the methods is related purely to the information retrieval field ...

We present a novel relevance feedback (RF) method that uses not only the surface information in texts, but also the latent information contained therein. In the proposed method, we infer the latent topic distribution in user feedback and in each document in the search results using latent Dirichlet allocation, and then we modify the search results so that documents with a similar topic distribu...

A search engine retrieves the documents based on the query submitted to it. However, incorporation of user modelling, by the inclusion of past information (like the previous queries submitted and the titles of the documents clicked) is expected to increase the accuracy of the search results. Especially, in the case of short term history, such history information is highly related with the curre...

This paper proposes an approach to personalization by relevance `ranking’ feedback in impression-based retrieval for a multimedia database. Impression-based retrieval is a kind of ambiguous retrieval, and it enables a database user to find not only a known data but also an unknown data to him/her. Conventional approaches using relevance feedback technique only return a binary information: `rele...

Complex similarity queries, i.e., multi-feature multi-object queries, are needed to express the information need of a user against a large multimedia repository. Even if a user initially issues a single-object query over one feature, a system with relevance feedback will automatically generate a complex similarity query. Relevance feedback is only useful if response times are interactive. There...

We present results of a study in which 50 searchers, of varying degrees of experience in information retrieval (IR), each performed searches on two TREC4 adhoc interactive track topics, using a simple interface to the INQUERY retrieval engine. The foci of our study were: the relationships between the users' models and experience of IR, and their performance in the TREC-4 adhoc task while using ...

Talebob ("Speech Bob") is an interactive language learning tool for pupils (10+ years) helping them practice their pronunciation of simple, highly frequent phrases in Danish. Talebob's feedback is based on acoustic measurements (for pitch and intensity), presented to the user as helpful instructions for improvement. Talebob is currently being tested in schools in Nuuk, Hafnarfjörður and Tórshav...

The relevance feedback based approach in image retrieval system has been an active research field in the past few years. This powerful technique has been proved successful in many application areas. Various ad hoc parameter estimation techniques have been proposed for relevance feedback. In addition, methods that perform relevance feedback on multi-level image model have been formulated. The me...

Retrieval relevance feedback is an iterative search technique to bridge the semantic gap between the high level user intention and low level data representation. This technique interactively determines a user's desired output or query concept by asking the user whether certain proposed 3D models are relevant or not. In the past, most research efforts in 3D model retrieval field have focused on ...

Pseudo-relevance feedback has been perceived as an effective solution for automatic query expansion. However, a recent study has shown that traditional pseudo-relevance feedback may bring into topic drift and hence be harmful to the retrieval performance. It is often crucial to identify those good feedback documents from which useful expansion terms can be added to the query. Compared with trad...