A Similarity-Inclusive Link Prediction Based Recommender System Approach
Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Kaunas University of Technology
2019-12-01
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| Series: | Elektronika ir Elektrotechnika |
| Subjects: | |
| Online Access: | http://eejournal.ktu.lt/index.php/elt/article/view/24828 |
| _version_ | 1818877029397823488 |
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| author | Zuhal Kurt Kemal Ozkan Alper Bilge Omer Nezih Gerek |
| author_facet | Zuhal Kurt Kemal Ozkan Alper Bilge Omer Nezih Gerek |
| author_sort | Zuhal Kurt |
| collection | DOAJ |
| description | Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems. |
| first_indexed | 2024-12-19T13:51:47Z |
| format | Article |
| id | doaj.art-08da799be62c466ead82ef9cac7d299a |
| institution | Directory Open Access Journal |
| issn | 1392-1215 2029-5731 |
| language | English |
| last_indexed | 2024-12-19T13:51:47Z |
| publishDate | 2019-12-01 |
| publisher | Kaunas University of Technology |
| record_format | Article |
| series | Elektronika ir Elektrotechnika |
| spelling | doaj.art-08da799be62c466ead82ef9cac7d299a2022-12-21T20:18:42ZengKaunas University of TechnologyElektronika ir Elektrotechnika1392-12152029-57312019-12-01256626910.5755/j01.eie.25.6.2482824828A Similarity-Inclusive Link Prediction Based Recommender System ApproachZuhal KurtKemal OzkanAlper BilgeOmer Nezih GerekDespite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems.http://eejournal.ktu.lt/index.php/elt/article/view/24828bipartite graphlink predictionrecommender systemssimilarity |
| spellingShingle | Zuhal Kurt Kemal Ozkan Alper Bilge Omer Nezih Gerek A Similarity-Inclusive Link Prediction Based Recommender System Approach Elektronika ir Elektrotechnika bipartite graph link prediction recommender systems similarity |
| title | A Similarity-Inclusive Link Prediction Based Recommender System Approach |
| title_full | A Similarity-Inclusive Link Prediction Based Recommender System Approach |
| title_fullStr | A Similarity-Inclusive Link Prediction Based Recommender System Approach |
| title_full_unstemmed | A Similarity-Inclusive Link Prediction Based Recommender System Approach |
| title_short | A Similarity-Inclusive Link Prediction Based Recommender System Approach |
| title_sort | similarity inclusive link prediction based recommender system approach |
| topic | bipartite graph link prediction recommender systems similarity |
| url | http://eejournal.ktu.lt/index.php/elt/article/view/24828 |
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