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Journal ArticleDOI

Knowledge based Recommendation System in Semantic Web - A Survey

15 Mar 2019-International Journal of Computer Applications (Foundation of Computer Science)-Vol. 182, Iss: 43, pp 20-25
TL;DR: Different techniques used to generate knowledge-based recommendations are explored highlighting the advantages of knowledge based recommendation system over other recommendation techniques.
Abstract: Knowledge based recommendation systems use knowledge about users and products to make recommendations. Knowledge-based recommendations are not dependent on the rating, nor do they have to gather information about a particular user to give recommendations. Knowledge acquisition is the most important task for constructing knowledge-based recommendation system. Acquired knowledge must be represented in some structured machine-readable form, e.g., as ontology to support reasoning about what products meets the user’s requirements. In Semantic Web, knowledge is represented in the form of ontology. Representation of knowledge in structured form of ontology in Semantic Web makes the application of knowledge based recommendations system on Semantic Web very easy, as there is no need to construct knowledge base from scratch. Performance of knowledge based recommendations systems can be enhanced by exploiting ontology reasoning characteristics. This paper explores different techniques used to generate knowledge-based recommendations highlighting the advantages of knowledge based recommendation system over other recommendation techniques.

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Citations
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Journal ArticleDOI
TL;DR: In this article, the authors explore traditional and more recent developments of filtering methods for a recommender system, identify and analyze proposals related to knowledge graph-based recommender systems, and present the most relevant contributions using an application domain.
Abstract: In recent years, the use of recommender systems has become popular on the web. To improve recommendation performance, usage, and scalability, the research has evolved by producing several generations of recommender systems. There is much literature about it, although most proposals focus on traditional methods’ theories and applications. Recently, knowledge graph-based recommendations have attracted attention in academia and the industry because they can alleviate information sparsity and performance problems. We found only two studies that analyze the recommendation system’s role over graphs, but they focus on specific recommendation methods. This survey attempts to cover a broader analysis from a set of selected papers. In summary, the contributions of this paper are as follows: (1) we explore traditional and more recent developments of filtering methods for a recommender system, (2) we identify and analyze proposals related to knowledge graph-based recommender systems, (3) we present the most relevant contributions using an application domain, and (4) we outline future directions of research in the domain of recommender systems. As the main survey result, we found that the use of knowledge graphs for recommendations is an efficient way to leverage and connect a user’s and an item’s knowledge, thus providing more precise results for users.

34 citations

Journal Article
TL;DR: The experiment shows that semantic recommendation system model could produce more related results for particular user than classic methods, and can discovery more new interest which is implicated in the interest concepts.
Abstract: In order to solve the inability of personalized recommendation system on the semantic information processing,the semantic recommendation system model was built,which can descript the semantic sence in user's interest information.A kind of recommendation algorithm was also proposed to calculate the relationship between users and resource.Description logic was used to apply the model in a special domain,two rules was introduced to achieve the ability of interest transmission between different level of semantic concepts both in user profiles and resource profiles.The experiment shows that semantic recommendation system model could produce more related results for particular user than classic methods,it can discovery more new interest which is implicated in the interest concepts.

2 citations

Journal ArticleDOI
TL;DR: In this article , the authors proposed a novel semantic-enhanced neural collaborative filtering (NCF) model for movie rating prediction and movie recommendation based on ontology-like modeling and deep learning.
Abstract: Recommendation systems or recommender systems (RSs) are very popular in entertainment websites. With the combination of neural networks and collaborative filtering, Neural Collaborative Filtering (NCF) recommendation methods have shown their outperformance in making item suggestions. However, the lack of semantic relationships between objects makes the NCF unable to capture the complex user-item interactions. Moreover, traditional NCF is unable to capture the dynamic user preference over time. To address these issues, in this paper, we propose novel semantic-enhanced NCF models which are applied to movie rating prediction and movie recommendation. Therefore, MovieLens and IMDB datasets are taken into account as case studies. The proposed models are the integration of ontology-like modeling and deep learning for recommendation tasks into two parts:(1) building the semantic knowledge base for movies and (2) building the user behavior analytic model that has semantic knowledge inference on the knowledge base combined with the sequential preference learned from user sessions, input into the NCF module for making predictions or recommendations. Several experiments have been conducted to show their better recommendation performance than the traditional NCF model. • Building the semantic knowledge base for enhancing deep learning models. • Building the user behavior analytic model that has semantic knowledge inference on the knowledge base combined with the sequential preference learned from sequential data, input into the Neural Collaborative Filtering module for making predictions and recommendations.

2 citations

References
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Journal ArticleDOI
TL;DR: From basic techniques to the state-of-the-art, this paper attempts to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.
Abstract: As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

3,406 citations

Journal ArticleDOI
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.
Abstract: Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

2,639 citations

Book ChapterDOI
01 Jan 2011
TL;DR: The role of User Generated Content is described as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.
Abstract: Recommender systems have the effect of guiding users in a personal- ized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recom- mender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content- based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describ- ing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

1,582 citations

Book ChapterDOI
01 Jan 2005
TL;DR: The vision of a Semantic Web has recently drawn considerable attention, both from academia and industry, and description logics are often named as one of the tools that can support this vision and thus help to make this vision reality.
Abstract: The vision of a Semantic Web has recently drawn considerable attention, both from academia and industry. Description logics are often named as one of the tools that can support the Semantic Web and thus help to make this vision reality.

484 citations

Journal ArticleDOI
TL;DR: In this article, transformations and normal forms in the context of defeasible logic have been investigated, a simple but efficient formalism for non-monotonic reasoning based on rules and priorities.
Abstract: The importance of transformations and normal forms in logic programming, and generally in computer science, is well documented. This paper investigates transformations and normal forms in the context of Defeasible Logic, a simple but efficient formalism for nonmonotonic reasoning based on rules and priorities. The transformations described in this paper have two main benefits: on one hand they can be used as a theoretical tool that leads to a deeper understanding of the formalism, and on the other hand they have been used in the development of an efficient implementation of defeasible logic.

370 citations