Reliable Book Recommender System: An Evaluation and Comparison of Collaborative Filtering Algorithms

Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people: those who have had similar opinions in the past will continue sharing the same tastes in the future. Collaborative filtering, the dominant algorithm underlying recommender systems, uses a model of its users, contained within profiles, in order to guide what interactions should be allowed, and how these interactions translate first into predicted ratings, and then into recommendations. In this chapter, the authors introduce the various approaches that have been adopted when designing collaborative filtering algorithms, and how they differ from one another in the way they make use of the available user information. They then explore how these systems are evaluated, and highlight a number of problems that prevent recommendations from being suitably computed, before looking at the how current trends in recommender system research are projecting towards future developments.

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Improving patient rehabilitation performance in exercise games using collaborative filtering approach

PeerJ Computer Science ◽

10.7717/peerj-cs.599 ◽

2021 ◽

Vol 7 ◽

pp. e599

Author(s):

Waidah Ismail ◽

Ismail Ahmed Al-Qasem Al-Hadi ◽

Crina Grosan ◽

Rimuljo Hendradi

Keyword(s):

Virtual Reality ◽

Collaborative Filtering ◽

Recommender System ◽

Experimental Results ◽

Software Platform ◽

Filtering Algorithms ◽

Rehabilitation Outcome ◽

Nearest Neighbours ◽

History Of ◽

Filtering Approach

Background Virtual reality is utilised in exergames to help patients with disabilities improve on the movement of their limbs. Exergame settings, such as the game difficulty, play important roles in the rehabilitation outcome. Similarly, suboptimal exergames’ settings may adversely affect the accuracy of the results obtained. As such, the improvement in patients’ movement performances falls below the desired expectations. In this paper, a recommender system is incorporated to suggest the most preferred movement setting for each patient, based on the movement history of the patient. Method The proposed recommender system (ResComS) suggests the most suitable setting necessary to optimally improve patients’ rehabilitation performances. In the course of developing the recommender system, three methods are proposed and compared: ReComS (K-nearest neighbours and collaborative filtering algorithms), ReComS+ (k-means, K-nearest neighbours, and collaborative filtering algorithms) and ReComS++ (bacterial foraging optimisation, k-means, K-nearest neighbours, and collaborative filtering algorithms). The experimental datasets are collected using the Medical Interactive Recovery Assistant (MIRA) software platform. Result Experimental results, validated by the patients’ exergame performances, reveal that the ReComS++ approach predicts the best exergame settings for patients with 85.76% accuracy.

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Building Error-Reflected Models for Collaborative Filtering Recommender System

The KIPS Transactions PartD ◽

10.3745/kipstd.2009.16-d.3.451 ◽

2009 ◽

Vol 16D (3) ◽

pp. 451-462

Author(s):

Heung-Nam Kim ◽

Geun-Sik Jo

Keyword(s):

Collaborative Filtering ◽

Recommender System

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Identifying Attack Models for Securing Cluster-Based Recommendation System

Recent Patents on Engineering ◽

10.2174/1872212114666200403091053 ◽

2020 ◽

Vol 14 ◽

Author(s):

Amreen Ahmad ◽

Tanvir Ahmad ◽

Ishita Tripathi

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

Recommendation System ◽

Relevant Information ◽

System Function ◽

Recommendation Algorithm ◽

Underlying Network ◽

Entire Dataset ◽

Attack Models ◽

Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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