Sachin Gulabrao Walunj
ABSTRACT
Selecting a foundational platform is an important step in developing recommender systems for personal, research, or commercial purposes. This can be done in many different ways the platform may be developed from the ground up, an existing recommender engine may be contracted (OracleAS Personalization), code libraries can be adapted, or a platform may be selected and tailored to suit (LensKit, MymediaLite, Apache Mahout, etc.). In some cases, a combination of these approaches will be employed. For E-commerce projects, and particularly in the E-commerce website t, the ideal situation is to find an open-source platform with many active contributors that provides a rich and varied set of recommender system functions that meets all or most of the baseline development requirements. Short of finding this ideal solution, some minor customization to an already existing system may be the best approach to meet the specific development requirements. Various libraries have been released to support the development of recommender systems for some time, but it is only relatively recently that larger scale, open-source platforms have become readily available. In the context of such platforms, evaluation tools are important both to verify and validate baseline platform functionality, as well as to provide support for testing new techniques and approaches developed on top of the platform. Apache Mahout as an enabling platform for research and have faced both of these issues in employing it as part of work in collaborative filtering recommenders.
- Carlos E. Seminario,David C. Wilson. ?Case study evaluation of mahout as a recommender platform?. ACM 2012.Google Scholar
- Zhi-Dan Zhao, Ming-Sheng Shang. "User-based Collaborative-Filtering Recommendation Algorithm on Hadoop". third International conference on knowledge Discovery and data mining 2012.Google Scholar
- Greg Linden, Brent Smith, and Jeremy York, Amazon.com ?Recommendations Item-to-Item Collaborative Filtering? Published by the IEEE Computer Society 1089--7801/03/$17.00 2003.Google Scholar
- RuiMaximo Esteves, Chunming Rong. "Using Mahout for clustering Wikipedia's latest articles- A comparison between k-means" and fuzzy c-means third international conference on cloud computing Technology and science 'In the cloud 2011. Google ScholarDigital Library
- C. Desrosiers and G. Karypis. "A comprehensive survey of neighborhood-based recommendations methods". In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook. Springer, 2011.Google ScholarCross Ref
- M. D. Ekstrand, M. Ludwig, J. A. Konnstan, and J. T. Riedl. "Rethinking the recommender research ecosystem: Reproducibility, openness, and lenskit". In Proceedings of the 5th ACM Recommender Systems Conference (RecSys '11), October 2011. Google ScholarDigital Library
- M. Ge, C. Delgado-Battenfeld, and D. Jannach. "Beyond accuracy: Evaluating recommender systems by coverage and serendipity". In Proceedings of the 4th ACM Recommender Systems Conference (RecSys '10), September 2010. Google ScholarDigital Library
- https://cwiki.apache.org/MAHOUT/recommender-documentation.htmlGoogle Scholar
- SatnamAlag, "Collective Intelligence In Action", Manning Publications Co., first edition, 2009.Google Scholar
- Shine Ge, Xinyang Gen "An SVD-based Collaborative Filtering Approach toAlleviate Cold-Start Problems", In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2012).Google Scholar
- Min Xiao, BingjieYan, "Collaborative filtering recommendation algorithm based on shift of users" preferences IEEE 2010.Google Scholar
- SutheeraPuntheeranurak, ThanutChaiwitooanukool, "An Item-based Collaborative Filtering Method using Item-based Hybrid Similarity" IEEE 2011.Google Scholar
Index Terms
- An online recommendation system for e-commerce based on apache mahout framework
Recommendations
Personalized Recommendation Algorithm Using User Demography Information
WKDD '09: Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data MiningPersonalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography ...
Personalized rough-set-based recommendation by integrating multiple contents and collaborative information
In recent years, explosively-growing information makes the users confused in making decisions among various kinds of products such as music, movies, books, etc. As a result, it is a challenging issue to help the user identify what she/he prefers. To ...
Representation learning with collaborative autoencoder for personalized recommendation
AbstractIn the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful ...
Highlights- Two different autoencoders are used to capture characteristics for users and items.
- Manifold regularization is integrated into autoencoder for user’s features learning.
- The comprehensive experiments evaluate the effectiveness of ...
Comments