Ontology-based hybrid recommender system

In today’s information-overloaded era, the exponential growth of information we are required to absorb is huge, and we are hard to get the information that we needed. Hence, Recommender System (RS) has been introduced to tackle this problem. RS captures the user preferences and behaviour and then provides a relevant recommendation to the user. This helps users to reach the information that they might not be able to search for by themselves. With this, RS has been widely developed in many companies such as Netflix and Amazon to boost their sales and revenue. Current RS that are using only content-based or collaborative filtering are hard to adapt to the continuous changes of user preferences and have some issues such as cold-start and data sparsity. On the other hand, ontologies define rules to structure data, including interrelations between entities in the database. As such, it offers greater semantic relations within a particular domain. As compared to relational databases, the ontology approach is more flexible, scalable and faster. With the previous success of ontology in RS, we propose to design an RS, in which the semantic information about the domain is constructed as an ontology to represent both the user profile and the recommendable items. The drawback of the current RS is the result may be limited by sparse data and the information is not sufficient for the model to generate a good recommendation. Essentially, good data will improve the accuracy and personalization of an RS model. Hence, this motivates us to enrich the data prior to passing it to an RS model to achieve higher accuracy. In this research, we proposed an ontology-based hybrid RS. We aimed to improve the accuracy of the matrix factorization model. We enhanced the matrix factorization model accuracy by utilizing ontology to enrich the information of the user-item matrix by item-based (IB) and user-based (UB) collaborative filtering techniques. In our approach, the IB and UB have been modified to adapt to the ontology approach. Instead of using just the rating pattern as the similarity calculation in IB and UB, we proposed to use it together with the semantic similarity of item features and user features. The enriched data will then be forwarded to the matrix factorization model to produce the recommendation. The proposed method is evaluated with real-world data to verify the accuracy of our proposed method compared to the existing method. The evaluation performed on real-world datasets demonstrated that our proposed approach outperformed the baseline model and existing models in every dataset. With the ontology constructed with hierarchy structure, our proposed approach Ontology-based Dataset Enriching Recommendation Method (Onto-DERM+) has archived Root Mean Square Error (RMSE) value of 0.9357 in MovieLens 100K dataset, 0.9312 in MovieLens 1M dataset, 1.6321 in Book-Crossing dataset and 0.9653 in Yahoo! Movie dataset

A hybrid recommender system based on data enrichment on the ontology modelling

A recommender system captures the user preferences and behaviour to provide a relevant recommendation to the user. In a hybrid model-based recommender system, it requires a pre-trained data model to generate recommendations for a user. Ontology helps to represent the semantic information and relationships to model the expressivity and linkage among the data. Methods: We enhanced the matrix factorization model accuracy by utilizing ontology to enrich the information of the user-item matrix by integrating the item-based and user-based collaborative filtering techniques. In particular, the combination of enriched data, which consists of semantic similarity together with rating pattern, will help to reduce the cold start problem in the model-based recommender system. When the new user or item first coming into the system, we have the user demographic or item profile that linked to our ontology. Thus, semantic similarity can be calculated during the item-based and user-based collaborating filtering process. The item-based and user-based filtering process are used to predict the unknown rating of the original matrix. Results: Experimental evaluations have been carried out on the MovieLens 100k dataset to demonstrate the accuracy rate of our proposed approach as compared to the baseline method using (i) Singular Value Decomposition (SVD) and (ii) combination of item-based collaborative filtering technique with SVD. Experimental results demonstrated that our proposed method has reduced the data sparsity from 0.9542% to 0.8435%. In addition, it also indicated that our proposed method has achieved better accuracy with Root Mean Square Error (RMSE) of 0.9298, as compared to the baseline method (RMSE: 0.9642) and the existing method (RMSE: 0.9492). Conclusions: Our proposed method enhanced the dataset information by integrating user-based and item-based collaborative filtering techniques. The experiment results shows that our system has reduced the data sparsity and has better accuracy as compared to baseline method and existing method. Keyword

A Hybrid Ontology-based Recommender System Utilizing Data Enrichment and SVD Approaches

. A recommender system is a method of filtering data that provides a personalized recommendation list to a user where the user is interested. The semantic relationship from the ontology modelling does help to boost the accuracy of the recommender system based on recent research. In this paper, we propose a hybrid method to predict the unknown rating in the user-item matrix by using the semantic information of the ontology. The rating prediction utilizes the combination of user-based and item-based techniques. The predicted ratings boost the information of the input data of the model used in the recommender system as input data quality plays an important role in constructing the model. Experimental results demonstrated that the proposed approach achieves greater accuracy as compared to the baseline and existing methods

WQMS: A Water Quality Monitoring System using IoT

Water quality management is an important aspect in sustaining any modern civilization. Throughout human history, having access to clean water has always been a requirement for survival and that will not change anytime soon, therefore monitoring water quality and responding to potential pollution outbreak in a water supply can be equally crucial. Over the last few decades computing devices have come down in size, cost and power consumption while increasing computational performance. Having a monitoring system that is always connected to the Internet, such as an IoT based system, could provide potential users with the ability to monitor water quality anywhere in real time. Such system has the potential to cut down response time by having a real time alert system as compared to manual sample collection with analysis and as the nature of IoT being low cost and small footprint, implementing such system requires minimal investment. In this paper, we propose a potentially cost effective solution to manage water quality using IoT and predicts the future quality of water based on the historical dat

Mapping of extensible markup language-to-ontology representation for effective data integration

Extensible markup language (XML) is well-known as the standard for data exchange over the internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. We also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language

Content-based Recommender System with Descriptive Analytics

A recommendation system (RS) is an information filtering system that provides users with information, which one may be interested in. Ontology modelling has been widely used to conceptualize items and their semantic relationship together. Hence, in this paper, we propose an intelligent CB RS that allows users to not only access the product recommendations, but also the dashboard systems, which contain descriptive analytics, modeled using ontology. The dashboard allows users to have insight into past data. It consists of five main features: (i) Highlight Dashboard, (ii) Customer Dashboard, (iii) Advanced Search, (iv) Pivot Table and Pivot Chart, and (v) Report. Experimental evaluations show that the CB RS can return the accurate recommended product in a real propriety dataset

Improving the Prediction Resolution Time for Customer Support Ticket System

Processing customer queries on time will able to engage customer satisfaction, and thus improve the customer retention of a company. Increasing the labour to process these queries is certainly not an ideal solution. Advancing technology such as artificial intelligence and machine learning has led to the goal of automating this process, by predicting the time needed to resolve certain issues based on past similar cases. In this paper, we present the architecture for the Customer Support Ticket System to improve the accuracy of the predicted resolution time. In this research, we first perform the one hot encoding on the categorical variables, followed by feature selection. Next, a combination of classification and regression models is being utilised in our prediction pipeline. Experimental evaluations demonstrated that the Random Forest (RF) regression model has the best performance as compared to Neural Network and ADA boost. In addition, by adding the extremity feature as the attention, a significant performance boost for RF is observed