Two Models Based on Social Relations and SVD++ Method for Recommendation System
| Autor: | Hacer Karacan, Ali M. Ahmed Al Sabaawi, Yusuf Erkan Yenice |
|---|---|
| Přispěvatelé: | Mühendislik Fakültesi |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Similarity (geometry)
Exploit Computer Networks and Communications Computer science RSS 02 engineering and technology social relations TK5101-6720 Recommender system computer.software_genre Cold start recommendation system cold-start data sparsity Singular value decomposition 0202 electrical engineering electronic engineering information engineering svd++ 020206 networking & telecommunications computer.file_format Computer Science Applications Accepted and experimental value Telecommunication Resource allocation 020201 artificial intelligence & image processing Data mining SVD computer |
| Zdroj: | International Journal of Interactive Mobile Technologies, Vol 15, Iss 01, Pp 70-87 (2021) International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 01 (2021); pp. 70-87 |
| ISSN: | 1865-7923 |
| Popis: | *Al Sabaawi, Ali M. Ahmed (Aksaray, Yazar ) *Yenice, Yusuf Erkan (Aksaray, Yazar ) Recently, Recommender Systems (RSs) have attracted many researchers whose goal is to improve the performance of the prediction accuracy of recommendation systems by alleviating RSs drawbacks. The most common limitations are sparsity and the cold-start user problems. This article proposes two models to mitigate the effects of these limitations. The proposed models exploit five sources of information: rating information, which involves two sources, namely explicit and implicit, which can be extracted via users’ ratings, and two types of social relations: explicit and implicit relations, the last source is confidence values that are included in the first model only. The whole sources are combined into the Singular Value Decomposition plus (SVD++) method. First, to extract implicit relations, each non-friend pair of users, the Multi-Steps Resource Allocation (MSRA) method is adopted to compute the probability of being friends. If the probability has accepted value which exceeds a threshold, an implicit relationship will be created. Second, the similarity of explicit and implicit social relationships for each pair of users is computed. Regarding the first model, a confidence value between each pair of users is computed by dividing the number of common items by the total number of items which have also rated by the first user of this pair. The confidence values are combined with the similarity values to produce the weight factor. Furthermore, the weight factor, explicit, and implicit feedback information are integrated into the SVD++ method to compute the missing prediction values. Additionally, three standard datasets are utilized in this study, namely Last. Fm, Ciao, and FilmTrust, to evaluate our models. The experimental results have revealed that the proposed models outperformed state-of-the-art approaches in terms of accuracy. |
| Databáze: | OpenAIRE |
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