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.

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 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 Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System

Rekayasa ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 234-239
Author(s):  
Noor Ifada ◽  
Nur Fitriani Dwi Putri ◽  
Mochammad Kautsar Sophan
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Method Development ◽  
Recommendation System ◽  
Rating Data ◽  
The Real ◽  
Rating Prediction ◽  
Filtering Approach ◽  
Better Than

A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy.  Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most  in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.

scholarly journals Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability

2020 ◽  
Author(s):  
Deng Pan ◽  
Xiangrui Li ◽  
Xin Li ◽  
Dongxiao Zhu
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Ground Truth ◽  
Research Community ◽  
Feature Mapping ◽  
Trade Off ◽  
Rating Prediction ◽  
Strong Performance ◽  
And Performance ◽  
Explainable Recommendation

Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory accuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluate the explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https://github.com/pd90506/AMCF.

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

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.

P2PCF: A collaborative filtering based recommender system for peer to peer social networks

2021 ◽  
pp. 1-19
Author(s):  
Lyes Badis ◽  
Mourad Amad ◽  
Djamil Aïssani ◽  
Sofiane Abbar
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Peer To Peer ◽  
Public Concerns ◽  
Cold Start Problem ◽  
Filtering Approach

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage.

Sign in / Sign up

Export Citation Format

Share Document