A Generic Framework for Collaborative Filtering Based on Social Collective Recommendation

Homann, Leschek; Maleszka, Bernadetta; Martins, Denis Mayr Lima; Vossen, Gottfried

doi:10.1007/978-3-319-98443-8_22

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…The social collective framework proposed in Homann et al 4 was designed to exploit relations and patterns in a user community in order to provide useful recommendations to users that are new to a recommendation platform. To achieve this goal we apply a community detection algorithm on social network data to identify groups of users (i.e., communities) that appropriately capture di®erent user preferences.…”

Section: Social Collective Signals For Recommender Systemsmentioning

confidence: 99%

“…LOAD CSV FROM`file://users.csv' AS row FIELDTERMINATOR`j' CREATE (p: Personfid:toInteger(row[0]), age:toInteger(row [1]), gender:row [2], occupation:row [3], zipCode:toInteger(row [4] Here, for each line in the CSV¯le, a node of type Person with an identi¯er, age, gender, occupation, and zip code is created in the graph database. In Sec.…”

Section: Acquisition and Preprocessingmentioning

confidence: 99%

“…For instance, the tie strength between users can be considered to produce more accurate recommendations, particularly in cases where a spare rating dataset hinders the e®ectiveness of CF. 3 In a previous work, 4 we have introduced a conceptual framework that describes how social collective signals can be incorporated in traditional Recommender Systems to alleviate the cold-start problem. Next, we present an illustrative example to clarify the concept of social-based recommendations.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Traditional Recommender Systems via Social Communities

Homann

Martins

Vossen

et al. 2019

Vietnam J. Comp. Sci.

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Collaborative Filtering (CF) has become the most popular approach for developing Recommender Systems in diverse business applications. Unfortunately, problems such as the cold-start problem (i.e., new users or items enter the system and for those no previous preference information is available) and the gray sheep problem (i.e., cases in which a user profile does not match any other profile in the user community) are widely recognized for hindering recommendation effectiveness of traditional CF methods. To alleviate such problems, substantial research has focused on enhancing CF with social information about users (e.g., social relationships and communities). However, despite the crescent interest in social-based approaches, researches and practitioners face the challenge of developing their own Recommender System architecture for appropriately combining social and collaborative filtering methods to improve recommendation results. In this paper, we address this issue by introducing a flexible architecture to support researchers and practitioners in the task of designing real-world Recommender Systems that exploit social network data. We focus on detailing our proposed architecture modules and their interplay, potential algorithms for extracting and combining relevant social information, and candidate technologies for handling diverse and massive data volumes. Additionally, we provide an empirical analysis demonstrating the effectiveness of the proposed architecture on alleviating the cold-start problem over a concrete experimental case.

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Section: Social Collective Signals For Recommender Systemsmentioning

confidence: 99%

Section: Acquisition and Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing Traditional Recommender Systems via Social Communities

Homann

Martins

Vossen

et al. 2019

Vietnam J. Comp. Sci.

Self Cite

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“…Collaborative filtering collects preference information from the users, and uses it to automatically predict the user interest . Collaborative filtering is applied in several recommendation systems because it does not require user profile information and can provide suggestions regardless of the content . OSN services that use this type of content recommendation scheme include Last.fm, Flickr, Instagram, and YouTube .…”

Section: Introductionmentioning

confidence: 99%

Personalized content recommendation scheme based on trust in online social networks

Bok

Geonsik

Lim

et al. 2019

Concurrency and Computation

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Summary As various social network services have developed, users are creating and sharing a large amount of content. Concurrently, there have been many studies on recommendation schemes for providing users with content that matches their preferences. In this paper, we propose a trust‐based personalized content recommendation scheme using collaborative filtering in online social network services. The user trust is calculated by analyzing social activities, content usages, and social relationships. In addition, the content trust is calculated by analyzing user expertise and reputations. Collaborative filtering is performed on users who are filtered through the user trust, and recommendation priorities are determined according to the content trust. The proposed scheme can improve the performance of collaborative filtering by eliminating untrustworthy users using user trust. It also improves the accuracy of recommendations since it provides recommendations based on content trust. Therefore, the proposed scheme can improve the performance of recommendation services using collaborative filtering in online social network services that share multimedia content. Performance evaluation is performed in terms of MAE and RMSE, which assesses errors in recommended results to demonstrate the superiority of the proposed scheme. Performance evaluations have shown that errors in the proposed scheme are reduced compared to the existing schemes, improving the accuracy of recommendations.

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A Generic Framework for Collaborative Recommendation in a Scientific Network

Maleszka

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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A Generic Framework for Collaborative Filtering Based on Social Collective Recommendation

Cited by 4 publications

References 15 publications

Enhancing Traditional Recommender Systems via Social Communities

Enhancing Traditional Recommender Systems via Social Communities

Personalized content recommendation scheme based on trust in online social networks

A Generic Framework for Collaborative Recommendation in a Scientific Network

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