Towards a user based recommendation strategy for digital ecosystems

Moscato, Vincenzo; Picariello, Antonio; Rinaldi, Antonio M.

doi:10.1016/j.knosys.2012.07.021

Cited by 25 publications

(9 citation statements)

References 21 publications

(23 reference statements)

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“…Social information the "credibility" of users [7], social relationships of users discovered by social networks [8] Social behaviors of users Users' browsing behaviors [9], users' point of interest [10] Opinions of users Comments given by users [11,12] Information of items Items' reputations, semantic contents [6] and items' attributes [5,13] Tag information Tags annotated by users and tags provided by systems [14] Beside the basic descriptions of users and items, tag information, which has been incorporated into hybrid CBF/CF algorithms by being used to calculate user-based and item-based similarity measures [14], is a kind of useful semantic information for recommendation systems.…”

Section: Categories Detailed Descriptionmentioning

confidence: 99%

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An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

et al. 2018

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Social tag information has been used by recommender systems to handle the problem of data sparsity. Recently, the relationships between users/items and tags are considered by most tag-induced recommendation methods. However, sparse tag information is challenging to most existing methods. In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance of recommender systems, even in the case of sparse tag information. The proposed method integrates coupled similarity between tags, which is calculated by the co-occurrences of tags in the same items, to extend each item's tags. Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization. MovieLens dataset and Book-Crossing dataset are adopted to evaluate the performance of the proposed algorithm. The results of experiments show that the proposed method can alleviate the impact of tag sparsity and improve the performance of recommender systems.

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Section: Categories Detailed Descriptionmentioning

confidence: 99%

“…For example, the contextual description was combined with usage patterns to predict behaviors of users and provide effective recommendation services [6]. The detailed categories of extra information integrated by hybrid recommendation methods are listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

et al. 2018

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“…. , }; (5) while ( ) do (6) = ; (7) for each ( ) do (8) = hybridAlgorithm( , , , ); (9) = the interestarray of ; (10) if ( ∩ = 0) then (11) the user quits to the RS; (12) else (13) the browsed item is recorded into bm( ); (14) = ; (15) end if (16) end for (17) end while algorithm and the 7 is recommended based on random one in the second round of recommendation. The recommended set of the second round is…”

Section: A Runningmentioning

confidence: 99%

“…These behaviors include browsing, rating, and sequence-independent manner. The browsing behavior [12,13] is that the user only specifies which items are browsed. The rating behavior [14,15] is that the user specifies the score to items.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Recommender System Based on User-Recommender Interaction

Zhang

Min

et al. 2015

Mathematical Problems in Engineering

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Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, they seldom consider user-recommender interactive scenarios in real-world environments. In this paper, we propose a hybrid recommender system based on userrecommender interaction and evaluate its performance with recall and diversity metrics. First, we define the user-recommender interaction. The recommender system accepts user request, recommends N items to the user, and records user choice. If some of these items favor the user, she will select one to browse and continue to use recommender system, until none of the recommended items favors her. Second, we propose a hybrid recommender system combining random and k-nearest neighbor algorithms. Third, we redefine the recall and diversity metrics based on the new scenario to evaluate the recommender system. Experiments results on the well-known MovieLens dataset show that the hybrid algorithm is more effective than nonhybrid ones.

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“…To Enhance User Experience using content recommendation facilities, based on user profile (favourite pages, subscribed channels, etc.) -All recommendation systems exploit user profiles to provide suggestions about contents related to particular topics, concepts or entities Amato et al, 2014;Moscato et al, 2013). For the Intrage Portal, content recommendation is implemented in "My Home" section, where user can view targeted recommendation boxes.…”

Section: To Facilitate the Content Editing Task Throughmentioning

confidence: 99%

An Application of Semantic Web Technologies to Enhance Content Management in Web Information Portals

Orabona¹,

Palmieri²,

Moscato

et al. 2015

Proceedings of 4th International Conference on Data Management Technologies and Applications

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As well known, Semantic Web technologies make available a set of facilities that allow data to be shared and reused across applications. The last generation of Content Management System (CMS) can leverage such technologies to improve the content management task incorporating semantic annotations of the produced resources. Here, we present the benefits deriving from the application of semantic technologies in a CMS environment. To this goal, we collect preliminary results about the effectiveness of the integration of a semantic annotation engine within the Intrage Web Portal for content management purposes. The obtained results show that the approach is quite promising and encourage the current research.

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Towards a user based recommendation strategy for digital ecosystems

Cited by 25 publications

References 21 publications

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems

A Hybrid Recommender System Based on User-Recommender Interaction

An Application of Semantic Web Technologies to Enhance Content Management in Web Information Portals

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