Emanuel Lacic
Dominik Kowald
Denis Parra
Martin Kahr
Christoph Trattner
ABSTRACT
Recent research has unveiled the importance of online social networks for improving the quality of recommenders in several domains, what has encouraged the research community to investigate ways to better exploit the social information for recommendations. However, there is a lack of work that offers details of frameworks that allow an easy integration of social data with traditional recommendation algorithms in order to yield a straight-forward and scalable implementation of new and existing systems. Furthermore, it is rare to find details of performance evaluations of recommender systems such as hardware and software specifications or benchmarking results of server loading tests. In this paper we intend to bridge this gap by presenting the details of a social recommender engine for online marketplaces built upon the well-known search engine Apache Solr. We describe our architecture and also share implementation details to facilitate the re-use of our approach by people implementing recommender systems. In addition, we evaluate our framework from two perspectives: (a) recommendation algorithms and data sources, and (b) system performance under server stress tests. Using a dataset from the SecondLife virtual world that has both trading and social interactions, we contribute to research in social recommenders by showing how certain social features allow to improve recommendations in online marketplaces. On the platform implementation side, our evaluation results can serve as a baseline to people searching for performance references in terms of scalability, model training and testing trade-offs, real-time server performance and the impact of model updates in a production system.
- M. Balabanović and Y. Shoham. Fab: content-based, collaborative recommendation. Commun. ACM, 40(3):66--72, Mar. 1997. Google Scholar
Digital Library
- A. Bellogın and J. Parapar. Using graph partitioning techniques for neighbour selection in user-based collaborative filtering. In Proceedings of the sixth ACM conference on Recommender systems, pages 213--216. ACM, 2012. Google ScholarDigital Library
- A. Bellogin, J. Wang, and P. Castells. Bridging memory-based collaborative filtering and text retrieval. Information Retrieval, 16(6):697--724, 2013. Google ScholarDigital Library
- S. Bostandjiev, J. O'Donovan, and T. Höllerer. Tasteweights: a visual interactive hybrid recommender system. In Proceedings of the sixth ACM conference on Recommender systems, pages 35--42. ACM, 2012. Google ScholarDigital Library
- R. Burke. Hybrid recommender systems: Survey and experiments. User modeling and user-adapted interaction, 12(4):331--370, 2002. Google ScholarDigital Library
- E. Diaz-Aviles, L. Drumond, L. Schmidt-Thieme, and W. Nejdl. Real-time top-n recommendation in social streams. In Proceedings of the sixth ACM conference on Recommender systems, pages 59--66. ACM, 2012. Google ScholarDigital Library
- S. Doerfel and R. J\"aschke. An analysis of tag-recommender evaluation procedures. In Proceedings of the 7th ACM conference on Recommender systems, pages 343--346. ACM, 2013. Google ScholarDigital Library
- Z. Gantner, S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Mymedialite: A free recommender system library. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys '11, pages 305--308, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1):5--53, 2004. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009. Google ScholarDigital Library
- S. M. McNee, J. Riedl, and J. A. Konstan. Being accurate is not enough: how accuracy metrics have hurt recommender systems. In CHI '06 Extended Abstracts on Human Factors in Computing Systems, CHI EA '06, pages 1097--1101, New York, NY, USA, 2006. ACM. Google ScholarDigital Library
- D. Parra, P. Brusilovsky, and C. Trattner. User controllability in an hybrid talk recommender system. In Proceedings of the ACM 2014 International Conference on Intelligent User Interfaces, IUI '14, pages 305--308, New York, NY, USA, 2014. ACM.Google Scholar
- D. Parra and S. Sahebi. Recommender systems : Sources of knowledge and evaluation metrics. In Advanced Techniques in Web Intelligence-2: Web User Browsing Behaviour and Preference Analysis, pages 149--175. Springer-Verlag, 2013.Google ScholarCross Ref
- D. Parra-Santander and P. Brusilovsky. Improving collaborative filtering in social tagging systems for the recommendation of scientific articles. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on, volume 1, pages 136--142. IEEE, 2010. Google ScholarDigital Library
- M. J. Pazzani and D. Billsus. Content-based recommendation systems. In The adaptive web, pages 325--341. Springer, 2007. Google ScholarCross Ref
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pages 175--186. ACM, 1994. Google ScholarDigital Library
- R. Ronen, N. Koenigstein, E. Ziklik, and N. Nice. Selecting content-based features for collaborative filtering recommenders. In Proceedings of the 7th ACM conference on Recommender systems, pages 407--410. ACM, 2013. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285--295. ACM, 2001. Google ScholarDigital Library
- M. Sarwat, J. Avery, and M. F. Mokbel. Recdb in action: recommendation made easy in relational databases. Proceedings of the VLDB Endowment, 6(12):1242--1245, 2013. Google ScholarDigital Library
- J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen. Collaborative filtering recommender systems. In The adaptive web, pages 291--324. Springer, 2007. Google ScholarCross Ref
- B. Smyth and P. McClave. Similarity vs. diversity. In D. Aha and I. Watson, editors, Case-Based Reasoning Research and Development, volume 2080 of Lecture Notes in Computer Science, pages 347--361. Springer Berlin Heidelberg, 2001. Google ScholarDigital Library
- J. Suchal and P. Návrat. Full Text Search Engine as Scalable k-Nearest Neighbor Recommendation System. In M. Bramer, editor, Artificial Intelligence in Theory and Practice III, pages 165--173. Springer Berlin Heidelberg, 2010.Google ScholarCross Ref
- S. G. Walunj and K. Sadafale. An online recommendation system for e-commerce based on apache mahout framework. In Proceedings of the 2013 annual conference on Computers and people research, pages 153--158. ACM, 2013. Google ScholarDigital Library
- O. Yilmazel, B. Yurekli, B. Yilmazel, and A. Arslan. Relational Databases versus Information Retrieval Systems : A Case Study. IADIS International Conference Applied Computing 2009, pages 1--4, 2009.Google Scholar
- Y. Zhang and M. Pennacchiotti. Predicting purchase behaviors from social media. In Proceedings of the 22Nd International Conference on World Wide Web, WWW '13, pages 1521--1532, 2013. Google ScholarDigital Library
- Z.-D. Zhao and M.-s. Shang. User-based collaborative-filtering recommendation algorithms on hadoop. In Knowledge Discovery and Data Mining, 2010. WKDD'10. Third International Conference on, pages 478--481. IEEE, 2010. Google ScholarDigital Library
Index Terms
- Towards a scalable social recommender engine for online marketplaces: the case of apache solr
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