scholarly journals Improving patient rehabilitation performance in exercise games using collaborative filtering approach

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.

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

scholarly journals Session-Based Recommender System for Sustainable Digital Marketing

2019 ◽  
Vol 11 (12) ◽  
pp. 3336 ◽  
Author(s):  
Hyunwoo Hwangbo ◽  
Yangsok Kim
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
High Performance ◽  
Marketing Strategies ◽  
Feature Weighting ◽  
Experimental Results ◽  
Digital Marketing ◽  
Chi Square ◽  
Fashion Products

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

Computing Recommendations with Collaborative Filtering

2011 ◽  
pp. 23-41
Author(s):  
Neal Lathia
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
System Research ◽  
Filtering Algorithms ◽  
Future Developments ◽  
The Past ◽  
Current Trends ◽  
The Future ◽  
The Way

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.

scholarly journals Application of Using Simulated Annealing to Combine Clustering with Collaborative Filtering for Item Recommendation

2013 ◽  
Vol 347-350 ◽  
pp. 2747-2751 ◽  
Author(s):  
Zhi Ming Feng ◽  
Yi Dan Su
Keyword(s):  
Simulated Annealing ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Experimental Results ◽  
Good Effect ◽  
Large Item ◽  
Performance Reduction ◽  
Similar Item ◽  
And Performance

tem-item collaborative filtering was widely used in item recommender system because of good recommend effects. However when facing a large amount of items, there would be performance reduction, because of building a very large item comparison dataset in order to find the similar item. K-means cluster had a very good effect in classification and a good performance even though the dataset being processed is very large. But the cold start was a problem to k-means and we must do some extra work to use it in item recommendation. By using the simulated annealing theory to combine the two methods to fixed the problems of the two methods mentioned above and take use of their advantages for better recommendation effect and performance. The experimental results show that, using simulated annealing to combine the clustering and collaborative filtering in item recommendation system can get more stable recommendation results of better quality.

scholarly journals Improving Children’s Experience on a Mobile EdTech Platform through a Recommender System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Almudena Ruiz-Iniesta ◽  
Luis Melgar ◽  
Alejandro Baldominos ◽  
David Quintana
Keyword(s):  
Collaborative Filtering ◽  
Mobile Devices ◽  
Recommender System ◽  
Experimental Results ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Smile and Learn is an EdTech digital publisher that offers a smart library of close to 100 educational stories and gaming apps for mobile devices aimed at children aged 2 to 10 and their families. Given the complexity of navigating the content, a recommender system was developed. The system consists of two major components: one that generates content recommendations and another that provides explanations and recommendations relevant to parents and educators. The former was implemented as a hybrid recommender system that combines three kinds of recommendations. Among these, we introduce a collaborative filtering adapted to overcome specific limitations associated with younger users. The approach described in this work was tested on real users of the platform. The experimental results suggest that this recommendation model is suitable to suggest apps to children and increase their engagement in terms of usage time and number of games played.

A Comprehensive Collaborative Filtering Approach using Autoencoder in Recommender System

2019 ◽  
Author(s):  
Mahamudul Hasan ◽  
Md. Tasdikul Hasan ◽  
Md. Selim Reza ◽  
Md. Nirab Akonda ◽  
M. Saddam Hossain Khan ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Filtering Approach

scholarly journals Tourism Site Recommender System Using Item-Based Collaborative Filtering Approach

2020 ◽  
Vol 2 (2) ◽  
pp. 119-126
Author(s):  
RA Nugroho ◽  
◽  
AM Polina ◽  
YD Mahendra ◽  
◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Time Consumption ◽  
Rating Prediction ◽  
Filtering Approach

Many people like traveling. But, often they are difficult to find a tourism site that they like much. Too many information about tourism is the problem. To overcome this problem, we need to filter the information. Recommender System could filter the information. By considering the advantages, the system used item-based collaborative filtering approach to give recommendation. Some tourism site around Yogyakarta province were used in this research. The system is able to give recommendation to users. The accuracy of the rating prediction is 0,6293 and the average time consumption is 1693,33 millisecond.

Open Source Projects Recommendation on GitHub

2018 ◽  
pp. 86-101
Author(s):  
Mohamed Guendouz ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Collaborative Filtering ◽  
Open Source ◽  
Recommender System ◽  
Real World ◽  
Evaluation Metrics ◽  
Recall Accuracy ◽  
Search Results ◽  
Filtering Approach

This chapter discusses the design and the implementation of a recommender system for open source projects on GitHub using the collaborative-filtering approach. Having such a system can be helpful for many developers, especially those who search for a particular project based on their interests. It can also reduce searching time and make search results more relevant. The system presented in this chapter was evaluated on a real-world dataset and using various evaluation metrics. Results obtained from these experiments are very promising. The authors found that their recommender system can reach better precision and recall accuracy.

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