Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns
Recommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most challenging probl...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/16/4/182 |
| _version_ | 1797606705088954368 |
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| author | Antiopi Panteli Basilis Boutsinas |
| author_facet | Antiopi Panteli Basilis Boutsinas |
| author_sort | Antiopi Panteli |
| collection | DOAJ |
| description | Recommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most challenging problems in collaborative filtering is handling users whose previous item purchase behavior is unknown, (e.g., new users) or products for which user interactions are not available, (e.g., new products). In this work, we address the cold-start problem in recommender systems based on frequent patterns which are highly frequent in one set of users, but less frequent or infrequent in other sets of users. Such discriminant frequent patterns can distinguish one target set of users from all other sets. The proposed methodology, first forms different clusters of old users and then discovers discriminant frequent patterns for each different such cluster of users and finally exploits the latter to hallucinate the purchase behavior of new users. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology. |
| first_indexed | 2024-03-11T05:18:51Z |
| format | Article |
| id | doaj.art-588fa1a88f8f45d4b0d024c4042a30fd |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-11T05:18:51Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-588fa1a88f8f45d4b0d024c4042a30fd2023-11-17T17:58:54ZengMDPI AGAlgorithms1999-48932023-03-0116418210.3390/a16040182Addressing the Cold-Start Problem in Recommender Systems Based on Frequent PatternsAntiopi Panteli0Basilis Boutsinas1Management Information Systems & Business Intelligence Laboratory, Department of Business Administration, University of Patras, GR 26504 Patras, GreeceManagement Information Systems & Business Intelligence Laboratory, Department of Business Administration, University of Patras, GR 26504 Patras, GreeceRecommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most challenging problems in collaborative filtering is handling users whose previous item purchase behavior is unknown, (e.g., new users) or products for which user interactions are not available, (e.g., new products). In this work, we address the cold-start problem in recommender systems based on frequent patterns which are highly frequent in one set of users, but less frequent or infrequent in other sets of users. Such discriminant frequent patterns can distinguish one target set of users from all other sets. The proposed methodology, first forms different clusters of old users and then discovers discriminant frequent patterns for each different such cluster of users and finally exploits the latter to hallucinate the purchase behavior of new users. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology.https://www.mdpi.com/1999-4893/16/4/182cold-start problemsparsityrecommender systemsdiscriminant frequent patterns |
| spellingShingle | Antiopi Panteli Basilis Boutsinas Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns Algorithms cold-start problem sparsity recommender systems discriminant frequent patterns |
| title | Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns |
| title_full | Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns |
| title_fullStr | Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns |
| title_full_unstemmed | Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns |
| title_short | Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns |
| title_sort | addressing the cold start problem in recommender systems based on frequent patterns |
| topic | cold-start problem sparsity recommender systems discriminant frequent patterns |
| url | https://www.mdpi.com/1999-4893/16/4/182 |
| work_keys_str_mv | AT antiopipanteli addressingthecoldstartprobleminrecommendersystemsbasedonfrequentpatterns AT basilisboutsinas addressingthecoldstartprobleminrecommendersystemsbasedonfrequentpatterns |