10,505 research outputs found
Recommended from our members
The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach
To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception
A user preference perception model using data mining on a Web-based Environment
In a competitive environment, how to provide the information and products to meet the requirements of customers and improve the customer satisfaction will be the key criteria to measure a company’s competitiveness. Customer Relationship Management (CRM) becomes an important issue in any business market gradually. Using information technology, businesses can achieve their requirements for one to one marketing more efficiently with lower cost, labor and time.
In this paper, we proposed a user preference perception model by using data mining technology on a web-based environment. First, the users’ web browse records are aggregated. Second, fuzzy set theory and most sequential pattern mining algorithm are used to infer the users’ preference changes in a period. After the test had processed, we use the on-line questionnaire to investigate the customer satisfaction degree from all participators. The results show that the degree of satisfaction was up to 72% for receiving the new information of participants whose preferences had been changed. It indicates that the proposed system can effectively perceive the change of preference for users on a web environment
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
Construction of Product Appearance Kansei Evaluation Model Based on Online Reviews and FAHP: A Case Study of Household Portable Air Conditioners
Meeting the personalized needs of users is the key to achieving the sustainable success of a product. It depends not only on the product’s functionality but also on satisfying users’ emotional needs for the product’s appearance. Therefore, researchers have been conducting research focusing on Kansei engineering theory to determine users’ emotional needs effectively. The initial process involves accurately extracting and filtering emotional data and Kansei words from consumers. Thus, we propose an evaluation model to efficiently obtain, screen, and sort these Kansei words based on Kansei engineering, using household portable air conditioners as research subjects. By integrating techniques for online user comment mining methods, users’ Kansei terms related to the product’s appearance can be gathered efficiently. These terms are then combined with image samples and filtered to determine a final set of 16 Kansei word pairs. Subsequently, the fuzzy analytic hierarchy process (FAHP) is utilized to prioritize these terms, and the fuzzy comprehensive evaluation (FCE) method is used to validate the results and determine the applicability of the evaluation model. The results showed that Kansei words could be quickly and objectively acquired using existing text mining techniques on online reviews. Moreover, the weights of different Kansei terms of the product’s appearance in the consumer’s perception are accurately produced through the FAHP. This evaluation model marks a significant advancement in accurately obtaining users’ emotional data in Kansei engineering. It offers valuable guidance for designing products that meet users’ personalized needs, enhancing design efficiency and reducing resource wastage at the early stages of designing, and improving the sustainability development of Kansei engineering
Automated user modeling for personalized digital libraries
Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to
improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in
an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
The data mining along with emerging computing techniques have astonishingly
influenced the healthcare industry. Researchers have used different Data Mining
and Internet of Things (IoT) for enrooting a programmed solution for diabetes
and heart patients. However, still, more advanced and united solution is needed
that can offer a therapeutic opinion to individual diabetic and cardio
patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced
healthcare system for proficient diabetes and cardiovascular diseases have been
proposed. The hybridization of data mining and IoT with other emerging
computing techniques is supposed to give an effective and economical solution
to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining,
Internet of Things, chatbots, contextual entity search (CES), bio-sensors,
semantic analysis and granular computing (GC). The bio-sensors of the proposed
system assist in getting the current and precise status of the concerned
patients so that in case of an emergency, the needful medical assistance can be
provided. The novelty lies in the hybrid framework and the adequate support of
chatbots, granular computing, context entity search and semantic analysis. The
practical implementation of this system is very challenging and costly.
However, it appears to be more operative and economical solution for diabetes
and cardio patients.Comment: 11 PAGE
- …