A Recommender System With IBA Similarity Measure
Само за регистроване кориснике
2020
Конференцијски прилог (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Recommender systems help users to reduce the amount of time they spend to find the items they are interested in. One of the most successful approaches is collaborative filtering. The main feature of a recommender system is its ability to predict user’s interests by analyzing the behavior of this particular user and/or the behavior of other similar users to generate personalized recommendations. Identification of neighbor users who have had similar taste to the target user in the past is a crucial process for successful application of collaborative filtering. In this paper, we proposed a collaborative filtering method that uses interpolative Boolean algebra for calculation of similarity between users. In order to analyze the effectiveness of the proposed approach we used three common datasets: MovieLens 100K, MovieLens 1M, and CiaoDVD. We compared a collaborative filtering based on IBA similarity measure with two standard similarity measures: Pearson correlation and cosine-based coeffic...ient. Even though statistical measures are traditionally used in recommender systems, proposed logic-based approach showed promising results on the tested datasets. A recommender system with IBA similarity measure outperformed the others in most cases.
Кључне речи:
User-based collaborative filtering / Similarity modeling / Recommender systems / Interpolative boolean algebra / IBA similarity measure / Collaborative filteringИзвор:
Springer Proceedings in Business and Economics, 2020, 275-290Издавач:
- Springer Science and Business Media B.V.
Институција/група
Fakultet organizacionih naukaTY - CONF AU - Vranić, N. AU - Milošević, Pavle AU - Poledica, Ana AU - Petrović, Bratislav PY - 2020 UR - https://rfos.fon.bg.ac.rs/handle/123456789/2112 AB - Recommender systems help users to reduce the amount of time they spend to find the items they are interested in. One of the most successful approaches is collaborative filtering. The main feature of a recommender system is its ability to predict user’s interests by analyzing the behavior of this particular user and/or the behavior of other similar users to generate personalized recommendations. Identification of neighbor users who have had similar taste to the target user in the past is a crucial process for successful application of collaborative filtering. In this paper, we proposed a collaborative filtering method that uses interpolative Boolean algebra for calculation of similarity between users. In order to analyze the effectiveness of the proposed approach we used three common datasets: MovieLens 100K, MovieLens 1M, and CiaoDVD. We compared a collaborative filtering based on IBA similarity measure with two standard similarity measures: Pearson correlation and cosine-based coefficient. Even though statistical measures are traditionally used in recommender systems, proposed logic-based approach showed promising results on the tested datasets. A recommender system with IBA similarity measure outperformed the others in most cases. PB - Springer Science and Business Media B.V. C3 - Springer Proceedings in Business and Economics T1 - A Recommender System With IBA Similarity Measure EP - 290 SP - 275 DO - 10.1007/978-3-030-21990-1_17 UR - conv_3711 ER -
@conference{ author = "Vranić, N. and Milošević, Pavle and Poledica, Ana and Petrović, Bratislav", year = "2020", abstract = "Recommender systems help users to reduce the amount of time they spend to find the items they are interested in. One of the most successful approaches is collaborative filtering. The main feature of a recommender system is its ability to predict user’s interests by analyzing the behavior of this particular user and/or the behavior of other similar users to generate personalized recommendations. Identification of neighbor users who have had similar taste to the target user in the past is a crucial process for successful application of collaborative filtering. In this paper, we proposed a collaborative filtering method that uses interpolative Boolean algebra for calculation of similarity between users. In order to analyze the effectiveness of the proposed approach we used three common datasets: MovieLens 100K, MovieLens 1M, and CiaoDVD. We compared a collaborative filtering based on IBA similarity measure with two standard similarity measures: Pearson correlation and cosine-based coefficient. Even though statistical measures are traditionally used in recommender systems, proposed logic-based approach showed promising results on the tested datasets. A recommender system with IBA similarity measure outperformed the others in most cases.", publisher = "Springer Science and Business Media B.V.", journal = "Springer Proceedings in Business and Economics", title = "A Recommender System With IBA Similarity Measure", pages = "290-275", doi = "10.1007/978-3-030-21990-1_17", url = "conv_3711" }
Vranić, N., Milošević, P., Poledica, A.,& Petrović, B.. (2020). A Recommender System With IBA Similarity Measure. in Springer Proceedings in Business and Economics Springer Science and Business Media B.V.., 275-290. https://doi.org/10.1007/978-3-030-21990-1_17 conv_3711
Vranić N, Milošević P, Poledica A, Petrović B. A Recommender System With IBA Similarity Measure. in Springer Proceedings in Business and Economics. 2020;:275-290. doi:10.1007/978-3-030-21990-1_17 conv_3711 .
Vranić, N., Milošević, Pavle, Poledica, Ana, Petrović, Bratislav, "A Recommender System With IBA Similarity Measure" in Springer Proceedings in Business and Economics (2020):275-290, https://doi.org/10.1007/978-3-030-21990-1_17 ., conv_3711 .