A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation.
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| Title: | A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. |
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| Authors: | Marco Degemmis1, Pasquale Lops1, Giovanni Semeraro1 |
| Source: | User Modeling & User-Adapted Interaction. Jul2007, Vol. 17 Issue 3, p217-255. 39p. |
| Subjects: | User-centered system design, Filtering software, Machine learning, Algorithms, Online information services |
| Abstract: | Â Â Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a nave Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles. [ABSTRACT FROM AUTHOR] |
| Copyright of User Modeling & User-Adapted Interaction is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 25213654 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Marco+Degemmis%22">Marco Degemmis</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pasquale+Lops%22">Pasquale Lops</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Giovanni+Semeraro%22">Giovanni Semeraro</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22User+Modeling+%26+User-Adapted+Interaction%22">User Modeling & User-Adapted Interaction</searchLink>. Jul2007, Vol. 17 Issue 3, p217-255. 39p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22User-centered+system+design%22">User-centered system design</searchLink><br /><searchLink fieldCode="DE" term="%22Filtering+software%22">Filtering software</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Online+information+services%22">Online information services</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Â Â Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. Traditional collaborative approaches compute a similarity value between the current user and each other user by taking into account their rating style, that is the set of ratings given on the same items. Based on the ratings of the most similar users, commonly referred to as neighbors, collaborative algorithms compute recommendations for the current user. The problem with this approach is that the similarity value is only computable if users have common rated items. The main contribution of this work is a possible solution to overcome this limitation. We propose a new content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. In more detail, user profiles are clustered to discover current user neighbors. Content-based user profiles play a key role in the proposed hybrid recommender. Traditional keyword-based approaches to user profiling are unable to capture the semantics of user interests. A distinctive feature of our work is the integration of linguistic knowledge in the process of learning semantic user profiles representing user interests in a more effective way, compared to classical keyword-based profiles, due to a sense-based indexing. Semantic profiles are obtained by integrating machine learning algorithms for text categorization, namely a nave Bayes approach and a relevance feedback method, with a word sense disambiguation strategy based exclusively on the lexical knowledge stored in the WordNet lexical database. Experiments carried out on a content-based extension of the EachMovie dataset show an improvement of the accuracy of sense-based profiles with respect to keyword-based ones, when coping with the task of classifying movies as interesting (or not) for the current user. An experimental session has been also performed in order to evaluate the proposed hybrid recommender system. The results highlight the improvement in the predictive accuracy of collaborative recommendations obtained by selecting like-minded users according to user profiles. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of User Modeling & User-Adapted Interaction is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11257-006-9023-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 39 StartPage: 217 Subjects: – SubjectFull: User-centered system design Type: general – SubjectFull: Filtering software Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Online information services Type: general Titles: – TitleFull: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Marco Degemmis – PersonEntity: Name: NameFull: Pasquale Lops – PersonEntity: Name: NameFull: Giovanni Semeraro IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 07 Text: Jul2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 09241868 Numbering: – Type: volume Value: 17 – Type: issue Value: 3 Titles: – TitleFull: User Modeling & User-Adapted Interaction Type: main |
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