Background: 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.
<span lang="EN-US">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. <br /> 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.</span>
Recommender system has been developed as a useful tool especially when we reached the era of big data and in the meanwhile the internet has been overwhelming with lots of choices. There is a need for people to filter the information to search for their needs and wants efficiently. E-commerce website such as Amazon and Netflix have been using recommender system to build and boost their sales through the personalization recommendation. With the success in the e-commerce area, researchers are keen on finding a method to boost traditional offline retailer sales thru the recommender system. Therefore, in this paper, we introduced the existing recommender system and discuss the method of filtering of each method. Then, we provide the overview of the recent paper in retailer and e-commerce domain to provide the insight and trends such as the filtering techniques and evaluation metric used. Several possible research direction has been discussed based on the current trends and problems. CCS Concepts• Information systems➝Information retrieval➝Retrieval tasks and goals➝Recommender systems.
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