Since it can effectively address the problem of sparsity and cold start of collaborative filtering, knowledge graph (KG) is widely studied and employed as side information in the field of recommender systems. However, most of existing KG-based recommendation methods mainly focus on how to effectively encode the knowledge associations in KG, without highlighting the crucial collaborative signals which are latent in user-item interactions. As such, the learned embeddings underutilize the two kinds of pivotal information and are insufficient to effectively represent the latent semantics of users and items in vector space. In this paper, we propose a novel method named Collaborative Knowledge-aware Attentive Network (CKAN) which explicitly encodes the collaborative signals by collaboration propagation and proposes a natural way of combining collaborative signals with knowledge associations together. Specifically, CKAN employs a heterogeneous propagation strategy to explicitly encode both kinds of information, and applies a knowledge-aware attention mechanism to discriminate the contribution of different knowledge-based neighbors. Compared with other KG-based methods, CKAN provides a brand-new idea of combining collaborative information with knowledge information together. We apply the proposed model on four real-world datasets, and the empirical results demonstrate that CKAN significantly outperforms several compelling state-of-the-art baselines.

CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems

Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases

Parkinson’s disease is one of the notable neurodegenerative disorders caused by insufficient production of dopamine which damages the motor skills and voice. Advancement of the Internet of Things (IoT) has fuelled the development of healthcare systems. In this article, we propose an intelligent system for detecting Parkinson’s disease to provide proper medication by analysing voice samples. Instead of relying on limited storage capacity and computational resources of IoT, the recent healthcare systems take advantages of the cloud server. On the other hand, the utilization of cloud computing incurs the issues of data privacy and additional communication costs to the healthcare systems. To address this issue, we propose to utilize Fog computing as a midway layer between end devices and the cloud server. The proposed system employs the combinatorial Fuzzy K-nearest Neighbor and Case-based Reasoning classifier for the better classification of the Parkinson patients from healthy individuals. On the detection of abnormality, the proposed healthcare system is designed to generate an immediate alert to the patient. The proposed system is experimentally evaluated on the UCI-Parkinson dataset, and the results reveal the improved performance of our system over baseline approaches.

Intelligent cyber-physical system for an efficient detection of Parkinson disease using fog computing

The way that users provide feedback on items regarding their satisfaction varies among systems: in some systems, only explicit ratings can be entered; in other systems textual reviews are accepted; and in some systems, both feedback types are accommodated. Recommender systems can readily exploit explicit ratings in the rating prediction and recommendation formulation process, however textual reviews -which in the context of many social networks are in abundance and significantly outnumber numeric ratings- need to be converted to numeric ratings. While numerous approaches exist that calculate a user's rating based on the respective textual review, all such approaches may introduce errors, in the sense that the process of rating calculation based on textual reviews involves an uncertainty level, due to the characteristics of the human language, and therefore the calculated ratings may not accurately reflect the actual ratings that the corresponding user would enter. In this work (1) we examine the features of textual reviews, which affect the reliability of the review-to-rating conversion procedure, (2) we compute a confidence level for each rating, which reflects the uncertainty level for each conversion process, (3) we exploit this metric both in the users’ similarity computation and in the prediction formulation phases in recommender systems, by presenting a novel rating prediction algorithm and (4) we validate the accuracy of the presented algorithm in terms of (i) rating prediction accuracy, using widely-used recommender systems datasets and (ii) recommendations generated for social network user satisfaction and precision, where textual reviews are abundant.

What makes a review a reliable rating in recommender systems

In recent times, the modern developments of internet technologies and social networks have attracted global researchers to explore the recommender systems for generating personalized location-based services. Recommender Systems (RSs) as proven decision support tools have gained immense popularity to solve information overloading problem among various real-time applications of e-commerce, travel and tourism, movies and e-learning. RSs emerge as a popular and reliable information filtering approach that is capable of suggesting relevant items, movies, and locations to the active target user based on dynamic preferences and interests. Beyond the development of many feature-rich recommendation algorithms, the need for a better full-fledged RS to produce precise and highly relevant recommendations based on ratings and preferences provided by the target user is very high. With the specific focus to the travel domain, the global research community has been involved in the development of a complete travel recommender system that is immune to the sparsity and cold start problems. In this paper, we present a new Hybrid Location-based Travel Recommender System (HLTRS) through exploiting ensemble based co-training method with swarm intelligence algorithms to enhance the personalized travel recommendations. The proposed HLTRS is experimentally validated on the real-world large-scale dataset, and we have made an extensive user study to determine the ability of developed RS to produce user satisfiable recommendations in real-time scenarios. The obtained results and analyses demonstrate the improved performance of the proposed Hybrid Location-based Travel Recommender System over existing baselines of recommender systems research.

Hybrid Location-based Recommender System for Mobility and Travel Planning

With the massive growth of the internet, a new paradigm of recommender systems (RS's) is introduced in various real time applications. In the research for better RS's, especially in the travel domain, the evolution of location-based social networks have helped RS's to understand the changing interests of users. In this article, the authors present a new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs). The recommended personalized list of travel locations will be predicted by generating a heat map of already visited POIs and the highly relevant POIs will be selected for recommendation as destinations. To enhance the recommendation quality, this article exploits the temporal features for increased user visits. A personalized travel plan is recommended to the user based on the user selected POIs and the proposed travel RS is experimentally evaluated with the real-time large-scale dataset. The obtained results of the developed RS are found to be proficient by means of improved diversity and accuracy of generated recommendations.

A. Sivapathi

Papers

Effective Knowledge Based Recommender System for Tailored Multiple Point of Interest Recommendation