H.R. Koosha; Z. Ghorbani; R. Nikfetrat
Abstract
In the last decade, online shopping has played a vital role in customers' approach to purchasing different products, providing convenience to shop and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. So, recommendation systems are ...
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In the last decade, online shopping has played a vital role in customers' approach to purchasing different products, providing convenience to shop and many benefits for the economy. E-commerce is widely used for digital media products such as movies, images, and software. So, recommendation systems are of great importance, especially in today's hectic world, which search for content that would be interesting to an individual. In this research, a new two-steps recommender system is proposed based on demographic data and user ratings on the public MovieLens datasets. In the first step, clustering on the training dataset is performed based on demographic data, grouping customers in homogeneous clusters. The clustering includes a hybrid Firefly Algorithm (FA) and K-means approach. Due to the FA's ability to avoid trapping into local optima, which resolves K-means' main pitfall, the combination of these two techniques leads to much better performance. In the next step, for each cluster, two recommender systems are proposed based on K-Nearest Neighbor (KNN) and Naïve Bayesian Classification. The results are evaluated based on many internal and external measures like the Davies-Bouldin index, precision, accuracy, recall, and F-measure. The results showed the effectiveness of the K-means/FA/KNN compared with other extant models.
B. Hassanpour; N. Abdolvand; S. Rajaee Harandi
Abstract
The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges ...
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The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy systems capabilities in determining the borders of user interests, it seems reasonable to combine it with social networks information and the factor of time. Hence, this study, for the first time, tries to assess the efficiency of the recommender systems by combining fuzzy logic, longitudinal data and social networks information such as tags, friendship, and membership in groups. And the impact of the proposed algorithm for improving the accuracy of recommender systems was studied by specifying the neighborhood and the border between the users’ preferences over time. The results revealed that using longitudinal data in social networks information in memory-based recommender systems improves the accuracy of these systems.
H.3. Artificial Intelligence
S. Adeli; P. Moradi
Abstract
Since, most of the organizations present their services electronically, the number of functionally-equivalent web services is increasing as well as the number of users that employ those web services. Consequently, plenty of information is generated by the users and the web services that lead to the users ...
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Since, most of the organizations present their services electronically, the number of functionally-equivalent web services is increasing as well as the number of users that employ those web services. Consequently, plenty of information is generated by the users and the web services that lead to the users be in trouble in finding their appropriate web services. Therefore, it is required to provide a recommendation method for predicting the quality of web services (QoS) and recommending web services. Most of the existing collaborative filtering approaches don’t operate efficiently in recommending web services due to ignoring some effective factors such as dependency among users/web services, the popularity of users/web services, and the location of web services/users. In this paper, a web service recommendation method called Popular-Dependent Collaborative Filtering (PDCF) is proposed. The proposed method handles QoS differences experienced by the users as well as the dependency among users on a specific web service using the user/web service dependency factor. Additionally, the user/web service popularity factor is considered in the PDCF that significantly enhances its effectiveness. We also proposed a location-aware method called LPDCF which considers the location of web services into the recommendation process of the PDCF. A set of experiments is conducted to evaluate the performance of the PDCF and investigating the impression of the matrix factorization model on the efficiency of the PDCF with two real-world datasets. The results indicate that the PDCF outperforms other competing methods in most cases.
