Item Recommendations System

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An Item Recommendations System is an information filtering system that implements an item recommendation algorithm to solve an item recommendation task.



  • (Wikipedia, 2017) ⇒ Retrieved:2017-7-21.
    • A recommender system or a recommendation system (sometimes replacing "system" with a synonym such as platform or engine) is a subclass of information filtering system that seeks to predict the "rating" or "preference" that a user would give to an item.[1] Recommender systems have become increasingly popular in recent years, and are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general. There are also recommender systems for experts,[2] collaborators,[3] jokes, restaurants, garments, financial services,[4] life insurance, romantic partners (online dating), and Twitter pages.[5]
  1. Cite error: Invalid <ref> tag; no text was provided for refs named handbook
  2. H. Chen, A. G. Ororbia II, C. L. Giles ExpertSeer: a Keyphrase Based Expert Recommender for Digital Libraries, in arXiv preprint 2015
  3. H. Chen, L. Gou, X. Zhang, C. Giles Collabseer: a search engine for collaboration discovery, in ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2011
  4. Alexander Felfernig, Klaus Isak, Kalman Szabo, Peter Zachar, The VITA Financial Services Sales Support Environment, in AAAI/IAAI 2007, pp. 1692-1699, Vancouver, Canada, 2007.
  5. Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh Zadeh WTF:The who-to-follow system at Twitter, Proceedings of the 22nd International Conference on World Wide Web



    • QUOTE: covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit.




  • (Adomavicius & Tuzhilin, 2005), ⇒ Gediminas Adomavicius, and Alexander Tuzhilin. (2005). “Toward the Next Generation of Recommender Systems: A survey of the state-of-the-art and possible extensions.” In: IEEE Transactions on Knowledge and Data Engineering. doi:10.1109/TKDE.2005.99.
    • QUOTE: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.


  • (Herlocker et al., 2004) ⇒ Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John Riedl. (2004). “Evaluating Collaborative Filtering Recommender Systems.” In: ACM Transactions on Information Systems (TOIS) 22(1). doi:10.1145/963770.963772.
    • ABSTRACT: Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.
    • NOTES: It supports that recommender system provide users with a ranked list of the recommended items.
    • Cited by ~659


  • (Burke, 2002) ⇒ Robin D. Burke. (2002). “Hybrid Recommender Systems: Survey and Experiments.” In: User Modeling and User-Adapted Interaction, 12(4). doi:10.1023/A:1021240730564.
    • ABSTRACT: Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques. To improve performance, these methods have sometimes been combined in hybrid recommenders. This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants. Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering.
    • ... Any system that produces individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options.



  • (Resnick & Varian, 1997) ⇒ Paul Resnick, and Hal R. Varian. (1997). “Recommender Systems.” In: Communications of the ACM, 40(3). doi:10.1145/245108.245121.
    • ABSTRACT: IT IS OFTEN NECESSARY TO MAKE CHOICES WITHOUT SUFFICIENT personal experience of the alternatives. In everyday life, we rely on recommendations from other people either by word of mouth, recommendation letters, movie and book reviews printed in newspapers, or general surveys such as Zagat’s restaurant guides. …

      … people provide recommendations as inputs, which the system then aggregates and directs to appropriate recipients.