Explanations are necessary for building users’ understanding and trust in machine learning systems. However, users may abandon systems if these explanations demonstrate consistent errors and they cannot affect change in the systems’ behavior in response. When user feedback is supported, then the utility of explanations is to not only promote understanding, but also enable users to help the mach...

Copyright is held by the author/owner(s). IUI’13, March 19–22, 2012, Santa Monica, California, USA. This work was performed while interning at Adobe Research. Abstract Machine learning techniques used for single-channel sound source separation currently offer no mechanism for user-feedback to improve upon poor results and typically require isolated training data to perform separation. To overco...

Bayesian networks have been widely used for diagnostics. These models can be extended to POMDPs to select the best action. This allows modeling partial observability due to causes and the utility of executing various tests. We describe the problem of refining diagnostic POMDPs when user feedback is available. We propose utilizing user feedback to pose constraints on the model, i.e., the transit...

In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpo...

this internet search engine relevance may be enhanced by means of considering end user search goal. In addition to the individual search engine optimization experience is usually increased through inferring individual search goals. This paper proposes a novel approach to infer user search goals by analyzing search engine query logs known as feedback session. First framework is proposed to disco...

One interesting way of accessing collections of multimedia objects is by methods of visualization and clustering. Growing self-organizing maps provide such a solution, which adapts automatically to the underlying database. Unfortunately, the result of the clustering greatly depends on the definition of the describing features and the used similarity measure. In this paper, we present a general ...

Image retrieval approach by proposing a new image feature detector and descriptor, namely the micro-structure descriptor (MSD). We present a computational model of creative design based on collaborative interactive genetic algorithms. This Paper test our model on floor planning. This Paper guide the evolution of floorplan based on subjective and objective criteria. The subjective criteria consi...

This paper is focused on the information retrieval area. Implicit user feedback is today very important part of this research area. Increase of the amount of information also increases the time to find relevant information. Currently most Web search engines produce the same results independently of who the user is. The article includes system design, which implements an implicit users feedback ...

One aspect of the vision of dataspaces has been articulated as providing various benefits of classical data integration with reduced up-front costs. In this paper, we present techniques that aim to support schema mapping specification through interaction with end users in a pay-as-you-go fashion. In particular, we show how schema mappings, that are obtained automatically using existing matching...

A prototype system for the filtering and ranking of news items has been developed and a pilot test has been conducted. The user’s interests are modeled by a user interest hierarchy based on explicit user feedback with adaptive learning after each session. The system learned very quickly, reaching normalized recall values of over 0.9 within three sessions. When the user’s interests “drifted”, th...