9617419
Hybrid critiquing-based recommender systems
We propose a novel critiquing-based recommender interface, the hybrid critiquing interface that integrates the user self-motivated critiquing facility to compensate for the limitations of system-proposed critiques. The results from our user study show that the integration of such self-motivated critiquing support enables users to achieve a higher level of decision accuracy while consuming less cognitive effort. In addition, users expressed higher subjective opinions of the hybrid critiquing interface than the interface simply providing system-proposed critiques, and they would more likely return to it for future use.
2603707
Evaluating Critiquing-based Recommender Agents
We describe a user study evaluating two critiquing-based recommender agents based on three criteria: decision accuracy. decision effort, and user confidence. Results show that user-motivated critiques were more frequently applied and the example critiquing system employing only this type of critiques achieved the best results. In particular, the example critiquing agent significantly improves users' decision accuracy with less cognitive effort consumed than the dynamic critiquing recommender with system-proposed critiques. Additionally, the former is more likely to inspire users' confidence of their choice and promote their intention to purchase and return to the agent for future use.
6274371
Trust building with explanation interfaces
Based on our recent work on the development of a trust model for recommender agents and a qualitative survey, we explore the potential of building users' trust with explanation interfaces. We present the major results from the survey, which provided a roadmap identifying the most promising areas for investigating design issues for trust-inducing interfaces. We then describe a set of general principles derived from an in-depth examination of various design dimensions for constructing explanation interfaces, which most contribute to trust formation. We present results of a significant-scale user study, which indicate that the organization-based explanation is highly effective in building users' trust in the recommendation interface, with the benefit of increasing users' intention to return to the agent and save cognitive effort.
46218447
Parallelizing Progressive Computation for Skyline Queries in Multi-disk Environment
Given a set of d-dimensional points, skyline query returns the points that are not dominated by any other point on all dimensions. In this paper, we study an interesting scenario of skyline retrieval, where multi-dimensional points are distributed among multiple disks. Efficient algorithms for parallelizing progressive skyline computation are developed, using the parallel R-trees. The core of our scheme is to visit more entries from some disks simultaneously and enable effective pruning strategies with dominance checking to prune away the non-qualifying entries. Extensive experiments with synthetic data confirm that our proposed algorithms are both efficient and scalable.
18118919
EnjoyPhoto: a vertical image search engine for enjoying high-quality photos
In this paper, we propose building a vertical image search engine called EnjoyPhoto that leverages rich metadata from various photo forum web sites to meet users' requirements for enjoying high-quality photos, which is virtually impossible in traditional image search engines. To solve the ranking problem when aggregating multiple photo forums, we propose a novel rank fusion algorithm that uses duplicate photos to normalize rating scores. To further improve user experiences in enjoying photos, we design an in-place image browsing interface, and compare it with several other interfaces in a user study. With rich metadata and rating information, more attractive user interfaces are enabled, including slideshow authoring and photo recommendations. We conducted experiments and user studies on a 2.5-million image database to evaluate the proposed rank fusion algorithm, investigate the rationale behind building a vertical image search engine, and study user interfaces and preferences for the purpose of enjoying high-quality photos. The experimental results demonstrate the effectiveness of the proposed ranking algorithm. The results also show that the 2.5-million high-quality image database in EnjoyPhoto performs comparably with Google's 1- billion image database for queries related to location, nature, and daily life categories. Finally, our results show that the in-place browsing interface-called Force-Transfer view-is much more convenient for users than traditional interfaces.
24161285
Effects of Local-Lag Mechanism on Task Performance in a Desktop CVE System
Consistency maintenance is a kernel problem in Collaborative Virtual Environment (CVE) research. The approaches used in Networked Virtual Environments (e.g., DR algorithm) could not be used in CVEs, for they could not prevent short-term inconsistency. Therefore, local-lag mechanism has been proposed to eliminate short-term inconsistency in CVEs. Choosing a proper lag value is a key problem in local-lag mechanism. This paper studied the effects of lag value (0ms–900ms) on task performance in a desktop CVE system. Experimental results indicate that the effect of lag value on task performance is not linear. The effect could be separated into four segments by three dividing points: 150ms, 300ms and 600ms. Lag has no effect on task performance while ranging from 0ms to 150ms. From 150ms to 300ms, lag slightly affects task performance. Lag deteriorates task performance seriously while ranging from 300ms to 600ms. When lag is longer than 600ms, the task cannot be accomplished sometimes.
41643555
A Fuzzy Dead Reckoning Algorithm for Distributed Interactive Applications
A fuzzy Dead Reckoning (DR) algorithm for distributed interactive applications is proposed in this paper. Since fixed threshold cannot adequately handle the dynamic relationships between moving entities, some multi-level threshold DR algorithms were proposed in the past few years. In these algorithms the level of threshold is adaptively adjusted based on the distance between entities. The proposed fuzzy DR algorithm is based on multi-level threshold DR algorithm and takes all properties of entity (e.g. position, size and view angle etc.) into consideration when adjusting the level of threshold. This algorithm employs fuzzy correlation degree to measure the relationships between entities and determine the level of threshold for DR algorithm. Fuzzy consistent relation is used to distribute weight for each property. Simulation results indicate that fuzzy DR algorithm can achieve a considerable reduction in the number of state update messages while maintaining adequate accuracy in extrapolation.
21308144
EmoEars: An Emotion Recognition System for Mandarin Speech
In this paper, an emotion recognition system for mandarin speech is presented. Five basic human emotions including angry, fear, happy, neutral and sad are investigated. The recognizer is based on neural network with OCON and ACON architecture. Some novel feature selection methods are also added as optional tool to enhance the efficiency and classification accuracy. The system can train speaker dependent emotion speech model through online emotional utterance recording. Experiment results show that emotion can be recognized through neural network model, the best mean accuracy is 86.7%. In addition, the feature selection module is effective to reduce the compute load and increase the generalization ability of the recognizer.