Item Recommender-based System

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

• Context:
  • It can typically process Item Data with item feature extraction techniques to build item recommendation models.
  • It can typically generate Item Recommendation Lists for item recommendation users based on their item recommendation preferences.
  • It can typically evaluate Item Recommendation Quality using item recommendation evaluation metrics such as item recommendation precision and item recommendation recall.
  • It can typically personalize Item Recommendation Results based on item recommendation user profiles and item recommendation user behavior.
  • It can typically improve Item Recommendation Accuracy through item recommendation feedback loops.
  • ...
  • It can often integrate Item Recommendation Algorithms with item recommendation business rules to meet item recommendation business objectives.
  • It can often handle Item Recommendation Cold Start Problems through item recommendation hybrid approaches.
  • It can often support Item Recommendation Explanation to increase item recommendation transparency and item recommendation user trust.
  • It can often adapt Item Recommendation Strategy based on item recommendation context such as item recommendation time, item recommendation location, and item recommendation device.
  • ...
  • It can range from being a Data-Driven Item Recommender-based System to being a Heuristic Item Recommender-based System, depending on its item recommender-based algorithm approach.
  • It can range from being a Personalized Item Recommender-based System to being a Non-Personalized Item Recommender-based System, depending on its item recommender-based personalization level.
  • It can range from being a Simple Item Recommender-based System to being a Complex Item Recommender-based System, depending on its item recommender-based implementation complexity.
  • It can range from being a Batch Item Recommender-based System to being a Real-Time Item Recommender-based System, depending on its item recommender-based processing frequency.
  • ...
  • It can be created by an Item Recommender-based System Creation Task.
  • It can be based on an Item Recommendation Platform.
  • It can be evaluated by an Item Recommender-based System Evaluation Task.
  • It can often be represented by an Item Recommender-based Architecture.
  • It can integrate with User Profile Systems to access item recommendation user data.
  • It can connect to Item Catalog Systems to obtain item recommendation product information.
  • It can provide Item Recommendation APIs for item recommendation third-party services.
  • ...
• Examples:
  • Item Recommender-based System Types, such as:
    • Product Recommender-based Systems, such as:
      • Amazon.com's Product Recommender-based System demonstrating item recommender-based collaborative filtering.
      • Walmart's Product Recommender-based System implementing item recommender-based content-based filtering.
    • Entertainment Content Recommender-based Systems, such as:
      • Video Recommender-based Systems, such as:
        • Movie Recommender-based Systems, such as:
          • Netflix's Movie Recommender-based System implementing item recommender-based matrix factorization and item recommender-based deep learning.
          • Hulu's Movie Recommender-based System using item recommender-based hybrid filtering.
      • Music Recommender-based Systems, such as:
        • Spotify's Song Recommender-based System combining item recommender-based collaborative filtering with item recommender-based audio analysis.
        • Pandora's Music Recommender-based System using item recommender-based content-based filtering with the item recommender-based Music Genome Project.
      • Video Game Recommender-based Systems, such as:
        • Steam's Game Recommender-based System utilizing item recommender-based player behavior analysis.
        • PSN's Video Game Recommender-based System implementing item recommender-based social filtering.
      • Image Recommender-based Systems, such as:
        • Pinterest's Image Recommender-based System using item recommender-based visual similarity and item recommender-based user board analysis.
        • Instagram's Photo Recommender-based System leveraging item recommender-based engagement patterns.
    • Social Recommender-based Systems, such as:
      • Person Recommender-based Systems, such as:
        • LinkedIn's Person Recommender-based System using item recommender-based professional network analysis.
        • Facebook's Person Recommender-based System implementing item recommender-based social graph analysis.
      • Content Recommender-based Systems, such as:
        • Twitter's Content Recommender-based System using item recommender-based engagement prediction.
        • TikTok's Video Recommender-based System implementing item recommender-based reinforcement learning.
    • Professional Recommender-based Systems, such as:
      • Job Recommender-based Systems, such as:
        • LinkedIn's Job Recommender-based System matching item recommender-based candidate profiles with item recommender-based job requirements.
        • Indeed's Job Recommender-based System using item recommender-based search behavior and item recommender-based resume analysis.
    • Advertising Recommender-based Systems, such as:
      • Ad Recommender-based Systems, such as:
        • Google Ads Recommender-based System utilizing item recommender-based click prediction models.
        • Facebook Ads Recommender-based System implementing item recommender-based audience targeting.
        • Twitter Ads Recommender-based System employing item recommender-based user interest modeling.
  • ...
• Counter-Examples:
  • Search Query Systems, which focus on retrieving information based on explicit search query rather than predicting item recommendation user preferences.
  • Spam Filtering Systems, which classify content as unwanted rather than recommending items of potential interest.
  • Information Retrieval (IR) Systems, which respond to explicit information need expressions rather than proactively suggesting item recommendations based on user models.
  • Content Management Systems, which organize and store content without necessarily providing personalized item recommendations.
  • Inventory Management Systems, which track and manage product availability without making item recommendations to users.
• See: Item Recommendation Event, Item Recommendation Model, Item Recommendation Algorithm, Item Recommendation Evaluation Framework, Collaborative Filtering System, Content-based Filtering System, Matrix Factorization Technique, User Preference Modeling, Product Rating System, Predictive Modeling System, Recommendation Diversity Optimization, User Feedback Loop.

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References

2017a

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/recommender_system 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. 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, collaborators, jokes, restaurants, garments, financial services, life insurance, romantic partners (online dating), and Twitter pages.

2017b

• (Zhao et al., 2017) ⇒ Qian Zhao, Gediminas Adomavicius, F. Maxwell Harper, Martijn Willemsen, and Joseph A. Konstan. (2017). “Toward Better Interactions in Recommender Systems: Cycling and Serpentining Approaches for Top-N Item Lists.” In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ISBN:978-1-4503-4335-0 doi:10.1145/2998181.2998211
  • QUOTE: Current recommender systems often show the same most-highly recommended items again and again ignoring the feedback that users neither rate nor click on those items. We conduct an online field experiment to test two ways of manipulating top-N recommendations with the goal of improving user experience: cycling the top-N recommendation based on their past presentation and serpentining the top-N list mixing the best items into later recommendation requests.

2017c

• http://coursera.org/specializations/recommender-systems
  • 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.

2016

• http://mapr.com/products/mapr-sandbox-hadoop/tutorials/recommender-tutorial/
  • QUOTE: This tutorial will give step-by-step instructions on how to:
    • Use sample movie ratings data from http://grouplens.org/datasets/movielens/
    • Use a collaborative filtering algorithm from Apache Mahout to build and train a machine learning model
    • Use the search technology from Elasticsearch to simplify deployment of the recommender

2015

• https://blogdotrichanchordotcom.wordpress.com/2015/11/12/user-based-collaborative-filtering-with-apache-mahout/
  • QUOTE: … a simple tutorial to build an Apache Mahout’s user-based Collaborative Filtering Recommender System. …

2005a

• (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.

2004

• (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 http://scholar.google.com/scholar?cites=11267964832348181563

2002

• (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.

2001

• (Schafer et al., 2001) ⇒ J. Ben Schafer, Joseph A. Konstan, and John Riedl. (2001). “E-Commerce Recommender Applications.” In: Data Mining and Knowledge Discovery, vol 5(1). doi:10.1023/A:1009804230409
  • Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase.

1997

• (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.

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