Interactive Intent Modeling from Multiple Feedback Domains Pedram Daee1, Joel Pyykkö2, Dorota Głowacka2, and Samuel Kaski1
In 21st International Conference on Intelligent User Interfaces, ACM (2016). DOI: http://dx.doi.org/10.1145/2856767.2856803.
Helsinki Institute for Information Technology HIIT | 1 Aalto University, Department of Computer Science | 2 University of Helsinki, Department of Computer Science
[email protected]
I. Exploratory Search
II. SciNet
•In exploratory search, the user starts with an uncertain information need and provides relevance feedback to the system’s suggestions to direct the search. The search system can learn the user intent based on this feedback and employs it to recommend novel results. •The main challenge is that the user can only provide limited amount of feedback to the system, at the same time machine learning methods require considerable number of samples to learn effectively. •Research questions: 1. What is the objective goal of exploratory search? Proposed definition: keep the user interested in the retrieved items 2. How can the system learn by limited feedback? Proposed idea: take full advantage of user feedback both on keywords and on documents SciNet exploratory search system [1]. SciNet only considers feedback on the keywords (the radar interface) for learning the hidden user intent. Our modeling considers both feedback on keywords and on documents.
III. Problem Definition
IV. Keywords and Documents
•User intent: a set of relevance distributions on documents and keywords with expected relevances X and .
• Document-keyword probability matrix
V. Probabilistic User Model
•Goal: find the most relevant document • Assumption 1: Expected relevance of keys and docs are related through M: •This is a black box optimization problem, where the objective function is unknown and expensive to sample • •
Goal: find the maximum with minimum number of sampling, where each sample is a user feedback Multi-armed bandits [2] and Bayesian optimization [3] literature
• Exploration and Exploitation dilemma: To solve this maximization the system needs to explore the document space to estimate the expected relevance of documents based on user feedback. At the same time it should exploit the estimates to show relevant documents as early as possible.
VI. User Study
• Assumption 2: Expected relevance of keys are connected to their feature vector through an unknown weight vector • Therefore: • By this feature definition, the feedback for documents and keywords can be transfered to the same space.
Coupled Multi-armed Bandits Algorithm: At each time step t • Draw • For document bandit: select • For keyword bandit: select • Update the posterior based on user feedback and observed feature vectors
VII. Simulation Study
The simulator considers a small set of documents, and/or a set of keywords, as targets. In each iteration: •the algorithm presents a small set of documents and a small set of keywords to the user. •The simulated user may provide relevance feedback to keywords and documents
•We implemented the proposed method in an existing exploratory search system SciNet [1]. The baseline and the proposed method had the same user interface, but different learning algorithms.
Figure: Histogram for 100 independent runs of the methods. The sum of expected relevances of selected documents after 10 iterations is significantly higher when both types of feedback (doc+key) are considered.
•Participants: 10 university students and researchers. •The proposed system gave the users a more satisfying image of the topic they were exploring. Furthermore, the usability of the total system has also improved.
VIII. Summary and Future Work References: [1] Glowacka, et al., 2013, IUI, Directing exploratory search: Reinforcement learning from user interactions with keywords. [2] Agrawal, et al., 2013, ICML, Thompson Sampling for Contextual Bandits with Linear Payoffs. [3] Hoffman, et al., 2014, AISTATS, On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning. [4] Barral, et al., 2015, IUI, Exploring Peripheral Physiology as a Predictor of Perceived Relevance in Information Retrieval.
Helsinki Institute for Information Technology HIIT is a joint research institute of Aalto University and the University of Helsinki for basic and applied research on information technology.
• The coupled multi-armed bandits algorithm is an exploratory search method that employs the user feedback on both the retrieved items and their features. • Simulation results and preliminary user study indicate that considering these two sources of feedback can improve the performance and quality of the exploratory search.
• Our algorithm provides the opportunity to exploit several types of relevance feedback that are available for documents, in addition to relevance feedback available for individual keywords. One example of this is the implicit relevance feedback from physiological signals such as electrodermal activity and facial electromyography [4].
This work has been partly supported by the Academy of Finland (Finnish Centre of Excellence in Computational Inference Research COIN), Re:Know funded by TEKES, and MindSee (FP7–ICT; Grant Agreement #611570).
