论文信息 - SocRecM: a scalable social recommender engine for online marketplaces

SocRecM: a scalable social recommender engine for online marketplaces

This paper presents work-in-progress on SocRecM, a novel social recommendation framework for online marketplaces. We demonstrate that SocRecM is easy to integrate with existing Web technologies through a RESTful, scalable and easy-to-extend service-based architecture. Moreover, we reveal the extent to which various social features and recommendation approaches are useful in an online social marketplace environment.

[1] Mehrbakhsh Nilashi,et al. Collaborative filtering recommender systems , 2013 .

[2] Peter Brusilovsky,et al. User modeling and user adapted interaction , 2001 .

[3] Michael J. Pazzani,et al. Content-Based Recommendation Systems , 2007, The Adaptive Web.

[4] Jaana Kekäläinen,et al. Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[5] Jonathan L. Herlocker,et al. Evaluating collaborative filtering recommender systems , 2004, TOIS.

[6] Robin D. Burke,et al. Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[7] Yongzheng Zhang,et al. Predicting purchase behaviors from social media , 2013, WWW.

[8] Dominik Kowald,et al. Towards a scalable social recommender engine for online marketplaces: the case of apache solr , 2014, WWW.

[9] Barry Smyth,et al. Similarity vs. Diversity , 2001, ICCBR.

[10] Christoph Trattner,et al. Acquaintance or partner? Predicting partnership in online and location-based social networks , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).