Knowledge-Based Recommendation Systems

Bouraga, Sarah; Jureta, Ivan; Faulkner, Stéphane; Herssens, Caroline

doi:10.4018/ijiit.2014040101

Cited by 62 publications

(34 citation statements)

References 31 publications

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“…The first category deals with general introduction to Recommender Systems research. This includes the works of Bouraga, Jureta, Faulkner, & Herssens, 2014;Park, Choi, Kim, & Kim, 2011). The second category of the surveys provide methods; Context-aware systems (Baldauf, Dustdar, & Rosenberg, 2007), approaches and limitations (M. Sharma, 2013), Collaborative Filtering based on social networks (Yang, Guo, Liu, & Steck, 2013); Basic approaches in Recommender Systems (Felfernig et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Abdi¹,

Okeyo²,

Mwangi³

2018

CIS

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Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.

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Section: Related Workmentioning

confidence: 99%

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Abdi¹,

Okeyo²,

Mwangi³

2018

CIS

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“…First, Detailed survey of RS is done [3] [29]. The second step is to survey about the various types of RS such as Knowledge based Recommender system [55] [59], constraint based recommendation [22] [47] and context aware recommender systems [4]. The next step is to list of applications of RS as mentioned in [10] [24].…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Review of Recommender Systems in Education

Vaidhehi¹,

Suchithra²

2018

IJET

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Recommender system (RS)s are widely used in different walks of life. This research work is to explore the usage of RS in the field of education. This review is performed in five dimensions which includes, Purpose of RS in Education, various techniques to build RS, input parameters used in design of RS, type of students involved in design of RS and Modelling strategies for RS to represent the data. The outcome of the research work is to facilitate the efficient design of the recommender system in education which will help the students by generating the appropriate recommendations.

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“…Vosecky, Leung, & Ng (2012) also used the implicit microblogs information combined with content-based features in order to filter and rank posts according to their quality. Another approach consists of using domain knowledge to provide recommendations (Bouraga et al, 2014), This approach presents the advantage of avoiding the "cold-start" problem (i.e., not having enough historical data to infer recommendations). Finally, efficient community detection in social networks can be used to compute clusters in the users set and perform recommendations (Yin, Li, & Niu, 2014).…”

Section: Ranking and Recommendationmentioning

confidence: 99%

Filtering Structures for Microblogging Content

Dahimene

Mouza

2015

International Journal of Intelligent Information Technologies

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In the last years, microblogging systems have encountered a large success. After 7 years of existence Twitter claims more than 271 million active accounts leading to 500 million tweets per day. Microblogging systems rely on the all-or-nothing paradigm: a user receives all the posts from an account s/he follows. A consequence for a user is the risk of flooding, i.e., the number of posts received from all the accounts s/he follows implies a time-consuming scan of her/his feed to find relevant updates that match his interests. To avoid user flooding and to significantly diminish the number of posts to be delivered by the system, the authors propose to introduce filtering on top of microblogging systems. Driven by the shape of the data, the authors designed different filtering structures and compared them analytically as well as experimentally on a Twitter dataset which consists of more than 2.1 million users, 15.7 million tweets and 148.5 million publisher-follower relationships.

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Knowledge-Based Recommendation Systems

Cited by 62 publications

References 31 publications

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

A Systematic Review of Recommender Systems in Education

Filtering Structures for Microblogging Content

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