User-based Collaborative Filtering Technique Research Articles

Recommendation System on Travel Destination based on Geotagged Data

Tourism research has benefitted from the worldwide spread and development of social networking services. People nowadays are more likely to rely on internet resources to plan their vacations. Thus, travel recommendation systems are designed to sift through the mammoth amount of data and identify the ideal travel destinations for the users. Moreover, it is shown that the increasing availability and popularity of geotagged data significantly impacts the destination decision. However, most current research concentrates on reviews and textual information to develop the recommendation model. Therefore, the proposed travel recommendation model examines the collective behaviour and connections between users based on geotagged data to provide personalized suggestions for individuals. The model was developed using the user-based collaborative filtering technique. The matrix factorization model was selected as the collaborative filtering technique to compute user similarities due to its adaptability in dealing with sparse rating matrices. The recommendation model generates prediction values to recommend the most appropriate locations. Finally, the model performance of the proposed model was assessed against the popularity and random models using the test design established using Mean Average Precision (MAP), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The findings indicated that the proposed matrix factorization model has an average MAP of 0.83, with RMSE and MAE values being 1.36 and 1.24, respectively. The proposed model got significantly higher MAP values and the lowest RMSE and MAE values compared to the two baseline models. The comparison shows that the proposed model is effective in providing personalized suggestions to users based on their past visits.

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

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.

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

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 theexpressivity 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 onthe MovieLens 100k dataset to demonstrate the accuracy rate of our proposed approach as compared to the baseline methodusing (i) Singular Value Decomposition (SVD) and (ii) combination of item-based collaborative filtering technique with SVD. Experimental results demonstrated thatour 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.

Using Content Features to Enhance the Performance of User-Basedcollaborative Filtering

Content-based and collaborative filtering methods are the most successful solutions in recommender systems. Content-based method is based on item’s attributes. This method checks the features of user's favourite items and then proposes the items which have the most similar characteristics with those items. Collaborative filtering method is based on the determination of similar items or similar users, which are called item-based and user-based collaborative filtering, respectively.In this paper we propose a hybrid method that integrates collaborative filtering and content-based methods. The proposed method can be viewed as user-based Collaborative filtering technique. However to find users with similar taste with active user, we used content features of the item under investigation to put more emphasis on user’s rating for similar items. In other words two users are similar if their ratings are similar on items that have similar context. This is achieved by assigning a weight to each rating when calculating the similarity of two users.We used movielens data set to access the performance of the proposed method in comparison with basic user-based collaborative filtering and other popular methods.