1,534 research outputs found
Recommender System Based on Expert and Item Category
The objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique 2) the experts from the frequency of rating technique and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creatingThe objective of this study was to introduce the recommender system based on expert and item category to match the right items to users. In this study, the expert identification was divided into 3 techniques which were 1) the experts from social network technique, 2) the experts from the frequency of rating technique, and 3) the experts from other user’s preferences. To filter the expert users by using the frequency of rating technique and the experts from other user’s preferences technique, data about item category is used. For evaluation in this study, the researcher used Epinion for the performance testing to find out errors and accuracies in the prediction process. The results of this study showed that all the presented techniques had mean absolute error score at 0.15 and 85 percentages of accuracy, especially the expert identification combining with item category, it can reduce 60 percentages of the duration of recommendation creating
Credibility-based social network recommendation: Follow the leader
In Web-based social networks (WBSN), social trust relationships between users indicate the similarity of their needs and opinions. Trust can be used to make recommendations on the web because trust information enables the clustering of users based on their credibility which is an aggregation of expertise and trustworthiness. In this paper, we propose a new approach to making recommendations based on leaders' credibility in the "Follow the Leader" model as Top-N recommenders by incorporating social network information into user-based collaborative filtering. To demonstrate the feasibility and effectiveness of "Follow the Leader" as a new approach to making recommendations, first we develop a new analytical tool, Social Network Analysis Studio (SNAS), that captures real data and used it to verify the proposed model using the Epinions dataset. The empirical results demonstrate that our approach is a significantly innovative approach to making effective collaborative filtering based recommendations especially for cold start users. © 2010 Al-Sharawneh & Williams
Exploiting Cognitive Structure for Adaptive Learning
Adaptive learning, also known as adaptive teaching, relies on learning path
recommendation, which sequentially recommends personalized learning items
(e.g., lectures, exercises) to satisfy the unique needs of each learner.
Although it is well known that modeling the cognitive structure including
knowledge level of learners and knowledge structure (e.g., the prerequisite
relations) of learning items is important for learning path recommendation,
existing methods for adaptive learning often separately focus on either
knowledge levels of learners or knowledge structure of learning items. To fully
exploit the multifaceted cognitive structure for learning path recommendation,
we propose a Cognitive Structure Enhanced framework for Adaptive Learning,
named CSEAL. By viewing path recommendation as a Markov Decision Process and
applying an actor-critic algorithm, CSEAL can sequentially identify the right
learning items to different learners. Specifically, we first utilize a
recurrent neural network to trace the evolving knowledge levels of learners at
each learning step. Then, we design a navigation algorithm on the knowledge
structure to ensure the logicality of learning paths, which reduces the search
space in the decision process. Finally, the actor-critic algorithm is used to
determine what to learn next and whose parameters are dynamically updated along
the learning path. Extensive experiments on real-world data demonstrate the
effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Using Data Mining for Facilitating User Contributions in the Social Semantic Web
This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and for recommendation of semantic relations. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on those systems
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