24,196 research outputs found
Intelligent Product Brokering for E-Commerce: An Incremental Approach to Unaccounted Attribute Detection
This research concentrates on designing generic product-brokering agent to understand user preference towards a product category and recommends a list of products to the user according to the preference captured by the agent. The proposed solution is able to detect both quantifiable and non-quantifiable attributes through a user feedback system. Unlike previous approaches, this research allows the detection of unaccounted attributes that are not within the ontology of the system. No tedious change of the algorithm, database, or ontology is required when a new product attribute is introduced. This approach only requires the attribute to be within the description field of the product. The system analyzes the general product descriptions field and creates a list of candidate attributes affecting the user’s preference. A genetic algorithm verifies these candidate attributes and excess attributes are identified and filtered off. A prototype has been created and our results show positive results in the detection of unaccounted attributes affecting a user
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
Content-Based Book Recommending Using Learning for Text Categorization
Recommender systems improve access to relevant products and information by
making personalized suggestions based on previous examples of a user's likes
and dislikes. Most existing recommender systems use social filtering methods
that base recommendations on other users' preferences. By contrast,
content-based methods use information about an item itself to make suggestions.
This approach has the advantage of being able to recommended previously unrated
items to users with unique interests and to provide explanations for its
recommendations. We describe a content-based book recommending system that
utilizes information extraction and a machine-learning algorithm for text
categorization. Initial experimental results demonstrate that this approach can
produce accurate recommendations.Comment: 8 pages, 3 figures, Submission to Fourth ACM Conference on Digital
Librarie
Collaborative trails in e-learning environments
This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future
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