313 research outputs found
The Role of the Mangement Sciences in Research on Personalization
We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,
On Recommendation of Learning Objects using Felder-Silverman Learning Style Model
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation
Gradient-based Optimization for Bayesian Preference Elicitation
Effective techniques for eliciting user preferences have taken on added
importance as recommender systems (RSs) become increasingly interactive and
conversational. A common and conceptually appealing Bayesian criterion for
selecting queries is expected value of information (EVOI). Unfortunately, it is
computationally prohibitive to construct queries with maximum EVOI in RSs with
large item spaces. We tackle this issue by introducing a continuous formulation
of EVOI as a differentiable network that can be optimized using gradient
methods available in modern machine learning (ML) computational frameworks
(e.g., TensorFlow, PyTorch). We exploit this to develop a novel, scalable Monte
Carlo method for EVOI optimization, which is more scalable for large item
spaces than methods requiring explicit enumeration of items. While we emphasize
the use of this approach for pairwise (or k-wise) comparisons of items, we also
demonstrate how our method can be adapted to queries involving subsets of item
attributes or "partial items," which are often more cognitively manageable for
users. Experiments show that our gradient-based EVOI technique achieves
state-of-the-art performance across several domains while scaling to large item
spaces.Comment: To appear in the Thirty-Fourth AAAI Conference on Artificial
Intelligence (AAAI-20
Coarse preferences: representation, elicitation, and decision making
In this thesis we present a theory for learning and inference of user preferences with a
novel hierarchical representation that captures preferential indifference. Such models
of âCoarse Preferencesâ represent the space of solutions with a uni-dimensional, discrete
latent space of âcategoriesâ. This results in a partitioning of the space of solutions
into preferential equivalence classes. This hierarchical model significantly reduces the
computational burden of learning and inference, with improvements both in computation
time and convergence behaviour with respect to number of samples. We argue that
this Coarse Preferences model facilitates the efficient solution of previously computationally
prohibitive recommendation procedures. The new problem of âcoordination
through set recommendationâ is one such procedure where we formulate an optimisation
problem by leveraging the factored nature of our representation. Furthermore, we
show how an on-line learning algorithm can be used for the efficient solution of this
problem. Other benefits of our proposed model include increased quality of recommendations
in Recommender Systems applications, in domains where usersâ behaviour
is consistent with such a hierarchical preference structure. We evaluate the usefulness
of our proposed model and algorithms through experiments with two recommendation
domains - a clothing retailerâs online interface, and a popular movie database. Our experimental
results demonstrate computational gains over state of the art methods that
use an additive decomposition of preferences in on-line active learning for recommendation
Improving Data Quality, Model Functionalities and Optimizing User Interfaces in Decision Support Systems
This dissertation contributes to the research on three core elements of decision support systems for managers and consumers: data management, model management and user interface. With respect to data management this dissertation proposes an approach for reducing unobserved product heterogeneity in online transaction data sets. The example of an online auction data set is used to investigate the approachâs ability to improve data quality. In the area of model management this dissertation contributes an approach to elicit consumer product preferences for exponential (beside linear) utility functions aiming at predicting consumersâ utilities and willingness-to-pay for individual products. The question which utility function (linear or exponential) is better suited for predicting product utilities and the willingness to pay is evaluated using a laboratory experiment. Further, in the area of user interfaces this dissertation deals with information visualization. Focusing on coordinate systems, a laboratory experiment is used to investigate which visualization format (two or three dimensional) is better suited for supporting simple vs. complex decision making scenarios and which criteria matter when choosing a visualization format for a particular level of decision making complexity
A Low-Effort Recommendation System with High Accuracy - A New Approach with Ranked Pareto-Fronts
In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high userâs effort for using a utility-based recommendation system using a choice-based conjoint analysiswith hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Paretoefficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumerâs effort is kept low and comparable to that of simple systems that require little consumer input
Estimating Optimal Recommendation Set Sizes for Individual Consumers
Online consumers must burrow through vast piles of product information to find the best match to their preferences. This has boosted the popularity of recommendation agents promising to decrease consumers\u27 search costs. Most recent work has focused on refining methods to find the best products for a consumer. The question of how many of these products the consumer actually wants to see, however, is largely unanswered. This paper develops a novel procedure based on signal detection theory to estimate the number of recommendable products. We compare it to a utility exchange approach that has not been empirically examined so far. The signal detection approach showed very good predictive validity in two laboratory experiments, clearly outperforming the utility exchange approach. Our theoretical findings, supported by the experimental evidence, indicate conceptual inconsistencies in the utility exchange approach. Our research offers significant implications for both theory and practice of modeling consumer choice behavior
Walking the Middle Path: How Medium Trade-Off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents
Recommender Agents (RAs) facilitate consumersâ online purchase decisions for complex, multi-attribute products. As not all combinations of attribute levels can be obtained, users are forced into trade-offs. The exposure of trade-offs in a RA has been found to affect consumersâ perceptions. However, little is known about how different preference elicitation methods in RAs affect consumers by varying degrees of trade-off exposure. We propose a research model that investigates how different levels of trade-off exposure cognitively and affectively influence consumersâ satisfaction with RAs. We operationalize these levels in three different RA types and test our hypotheses in a laboratory experiment with 116 participants. Our results indicate that with increasing tradeoff exposure, perceived enjoyment and perceived control follow an inverted U-shaped relationship. Hence, RAs using preference elicitation methods with medium trade-off exposure yield highest consumer satisfaction. This contributes to the understanding of trade-offs in RAs and provides valuable implications toe-commerce practitioners
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