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robati anaraki M, riahi N. An evolutionary approach for automating the selection of optimum Algorithm in Collaborative Filtering Recommender Systems. JSDP 2023; 20 (1) : 4
URL: http://jsdp.rcisp.ac.ir/article-1-1206-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1206-en.html
Abstract: (1501 Views)
Recommender system can be defined as a software that suggests the most appropriate and closest item to the user's taste. They work as a counselor, behaving in such a way to guide people in the discovery of products of interest.
Nowadays A great number of recommendation methods are used to implement a recommender system, a group of these algorithms are called collaborative filtering. These methods use the similarity between users or the similarity between items according to their user rating patterns for generating recommendations. Collaborative filtering algorithms can recommend the user, interesting items which are not similar to items she has rated before. These recommendations are generated according to the preferences of users with similar taste to the target user. Different similarity functions and metrics have been used to create the model or compute the similarity in collaborative filtering methods. The best method which generates the most relevant items is not always the same and it may change according to the available data of users and items, because each approach has particularities and depends on the context to be applied. Thus, it becomes a hard task for system designers to manually select an appropriate method among the techniques.
This article proposes an approach based on genetic algorithm for rank aggregation of memory based collaborative filtering methods and chooses the most relevant recommendations generated by different similarity techniques to create a Top-N recommender system. In order to implement these techniques, in addition to computing the similarity between users, inferred trust is also computed to increase the amount of available information about relations between user interests. The final method proposes a combination of collaborative filtering techniques for each data set, which in addition to considering time limits, has an acceptable precision for making recommendations.
The proposed method has been compared against memory based collaborative filtering methods and similar methods. Experiments were performed using 1M MovieLens and 100k MovieLens and HetRec2011 data sets. The results show that the methodology proposed in this paper performs better and has a higher precision in generating recommendations for users than any of similar algorithms.
Nowadays A great number of recommendation methods are used to implement a recommender system, a group of these algorithms are called collaborative filtering. These methods use the similarity between users or the similarity between items according to their user rating patterns for generating recommendations. Collaborative filtering algorithms can recommend the user, interesting items which are not similar to items she has rated before. These recommendations are generated according to the preferences of users with similar taste to the target user. Different similarity functions and metrics have been used to create the model or compute the similarity in collaborative filtering methods. The best method which generates the most relevant items is not always the same and it may change according to the available data of users and items, because each approach has particularities and depends on the context to be applied. Thus, it becomes a hard task for system designers to manually select an appropriate method among the techniques.
This article proposes an approach based on genetic algorithm for rank aggregation of memory based collaborative filtering methods and chooses the most relevant recommendations generated by different similarity techniques to create a Top-N recommender system. In order to implement these techniques, in addition to computing the similarity between users, inferred trust is also computed to increase the amount of available information about relations between user interests. The final method proposes a combination of collaborative filtering techniques for each data set, which in addition to considering time limits, has an acceptable precision for making recommendations.
The proposed method has been compared against memory based collaborative filtering methods and similar methods. Experiments were performed using 1M MovieLens and 100k MovieLens and HetRec2011 data sets. The results show that the methodology proposed in this paper performs better and has a higher precision in generating recommendations for users than any of similar algorithms.
Article number: 4
Keywords: recommender systems, collaborative filtering, genetic algorithm, similarity metrics, rank aggregation, inferred trust.
Type of Study: Research |
Subject:
Paper
Received: 2021/01/30 | Accepted: 2023/02/22 | Published: 2023/08/13 | ePublished: 2023/08/13
Received: 2021/01/30 | Accepted: 2023/02/22 | Published: 2023/08/13 | ePublished: 2023/08/13
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