Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations
<p>Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to...
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2022
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| _version_ | 1864513567907119104 |
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| author | Shameem A. Puthiya Parambath (14150997) |
| author2 | Sanjay Chawla (4254202) |
| author2_role | author |
| author_facet | Shameem A. Puthiya Parambath (14150997) Sanjay Chawla (4254202) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shameem A. Puthiya Parambath (14150997) Sanjay Chawla (4254202) |
| dc.date.none.fl_str_mv | 2022-11-22T21:13:05Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10618-020-00708-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Simple_and_effective_neural-free_soft-cluster_embeddings_for_item_cold-start_recommendations/21597222 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Applied computing Data management and data science Distributed computing and systems software Recommender systems Item recommendation Item cold-start problem Soft-cluster embeddings |
| dc.title.none.fl_str_mv | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metricCold Items Precision(CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.</p><h2>Other Information</h2> <p> Published in: Data Mining and Knowledge Discovery<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10618-020-00708-6" target="_blank">http://dx.doi.org/10.1007/s10618-020-00708-6</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0a2a9dfb525cf3c5df032d53c42eb29d |
| identifier_str_mv | 10.1007/s10618-020-00708-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21597222 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendationsShameem A. Puthiya Parambath (14150997)Sanjay Chawla (4254202)Information and computing sciencesApplied computingData management and data scienceDistributed computing and systems softwareRecommender systemsItem recommendationItem cold-start problemSoft-cluster embeddings<p>Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metricCold Items Precision(CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.</p><h2>Other Information</h2> <p> Published in: Data Mining and Knowledge Discovery<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s10618-020-00708-6" target="_blank">http://dx.doi.org/10.1007/s10618-020-00708-6</a></p>2022-11-22T21:13:05ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10618-020-00708-6https://figshare.com/articles/journal_contribution/Simple_and_effective_neural-free_soft-cluster_embeddings_for_item_cold-start_recommendations/21597222CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215972222022-11-22T21:13:05Z |
| spellingShingle | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations Shameem A. Puthiya Parambath (14150997) Information and computing sciences Applied computing Data management and data science Distributed computing and systems software Recommender systems Item recommendation Item cold-start problem Soft-cluster embeddings |
| status_str | publishedVersion |
| title | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations |
| title_full | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations |
| title_fullStr | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations |
| title_full_unstemmed | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations |
| title_short | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations |
| title_sort | Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations |
| topic | Information and computing sciences Applied computing Data management and data science Distributed computing and systems software Recommender systems Item recommendation Item cold-start problem Soft-cluster embeddings |