2,863 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
To whom and why should I connect? Co-author Recommendation based on Powerful and Similar Peers
Sie, R. L. L., Drachsler, H., Bitter-Rijpkema, M., & Sloep, P. B. (2012). To whom and why should I connect? Co-author Recommendation based on Powerful and Similar Peers. International Journal of Technology Enhanced Learning (IJTEL), 4(1), 121-137. doi:10.1504/IJTEL.2012.048314The present article offers preliminary outcomes of a user study that investigated the acceptance of a recommender system that suggests future co- authors for scientific article writing. The recommendation approach is twofold: network information (betweenness centrality) and author (keyword) similarity are used to compute the utility of peers in a network of co-authors. Two sets of recommendations were provided to the participants: Set one focused on all candidate authors, including co-authors of a target user to strengthen current bonds and strive for acceptance of a certain research topic. Set two focused on solely new co-authors of a target user to foster creativity, excluding current co- authors. A small-scale evaluation suggests that the utility-based recommendation approach is promising, but to maximize outcome, we need to 1) compensate for researchers’ interests that change over time, and 2) account for multi-person co-authored papers
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
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
A Hybrid Recommender Strategy on an Expanded Content Manager in Formal Learning
The main topic of this paper is to find ways to improve learning in a formal Higher Education Area. In this environment, the teacher publishes or suggests contents that support learners in a given course, as supplement of classroom training. Generally, these materials are pre-stored and not changeable. These contents are typically published in learning management systems (the Moodle platform emerges as one of the main choices) or in sites created and maintained on the web by teachers themselves. These scenarios typically include a specific group of students (class) and a given period of time (semester or school year). Contents reutilization often needs replication and its update requires new edition and new submission by teachers. Normally, these systems do not allow learners to add new materials, or to edit existing ones.
The paper presents our motivations, and some related concepts and works. We describe the concepts of sequencing and navigation in adaptive learning systems, followed by a short presentation of some of these systems. We then discuss the effects of social interaction on the learners’ choices. Finally, we refer some more related recommender systems and their applicability in supporting learning.
One central idea from our proposal is that we believe that students with the same goals and with similar formal study time can benefit from contents' assessments made by learners that already have completed the same courses and have studied the same contents. We present a model for personalized recommendation of learning activities to learners in a formal learning context that considers two systems. In the extended content management system, learners can add new materials, select materials from teachers and from other learners, evaluate and define the time spent studying them. Based on learner profiles and a hybrid recommendation strategy, combining conditional and collaborative filtering, our second system will predict learning activities scores and offers adaptive and suitable sequencing learning contents to learners. We propose that similarities between learners can be based on their evaluation interests and their recent learning history. The recommender support subsystem aims to assist learners at each step suggesting one suitable ordered list of LOs, by decreasing order of relevance.
The proposed model has been implemented in the Moodle Learning Management System (LMS), and we present the system’s architecture and design.
We will evaluate it in a real higher education formal course and we intend to present experimental results in the near future
Using Semantic Recommenders for Personalized Recommendations
With the ever increasing information overload on the internet, recommender systems have long become a necessity. The popularity of e-commerce sites is increasing by the day and an abundance of shopping sites are presenting users with an increasing number of choices. It has become a challenging task to meet expectations of customers to better understand their needs and provide them with information and suggestions of their interest. With the e-commerce field being fiercely competitive, businesses have started to feel the need of personalization which helps them in building customer loyalty [17]. Personalized recommendations can prove to be the most important aspect of the evolution of the recommender systems. Personalized recommendation services provide opportunities to promote new products, increase sales, click-through and conversion rates [18]. The use of semantic web technologies in recommender systems can effectively enhance the quality of recommendation. Semantic web has provided structured knowledge representation tools such as taxonomies, ontologies, powerful languages such as Resource Description Framework (RDF), Web Ontology Language (OWL), etc. which can be used to represent rich, complex knowledge about things and their relationships and query languages such as SPARQL, reasoning engines that can infer logical consequences from a set of assertions. Semantics enable machines to process natural languages in a manner close to human cognition and mimic human reasoning to a certain extent [12]. This can greatly help to generate personalized predictions in the recommender framework [6]
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