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  • 32 bytes (3 words) - 04:25, 7 June 2011
  • Although research on adaptive news retrieval and [[recommendation]] has been performed for many years, most research has been focused on rath == Open Recommendation Platform (ORP) ==
    974 bytes (146 words) - 05:55, 4 December 2014
  • '''Group recommendation''' is the task of recommending items to groups of users instead of single u or TV program recommendation for a household (groups tend not to change much over time).
    1 KB (167 words) - 08:51, 21 July 2011
  • 29 bytes (3 words) - 11:53, 10 August 2011
  • ...h a self-implemented software, a machine learning package or a specific '''recommendation-focused''' software package or library. ...below shows a comparison of some of the more common software packages for recommendation.
    2 KB (175 words) - 09:23, 14 February 2016
  • The table below shows a comparison of some of the more common datasets for recommendation. * [[Recommendation Software]]
    1 KB (120 words) - 04:18, 11 August 2015
  • #REDIRECT [[Music recommendation]]
    34 bytes (3 words) - 13:21, 7 November 2011
  • #REDIRECT [[Music recommendation]]
    34 bytes (3 words) - 13:21, 7 November 2011
  • '''Music recommendation''' is the personalized suggestion of musical [[item]]s, e.g. tracks, albums ...ticular task is the generation of personalized playlists, i.e. '''playlist recommendation'''.
    873 bytes (100 words) - 17:57, 15 August 2012
  • '''Tag recommendation''' is the task of predicting [[folksonomy]] [[tag]]s for a given [[user]] a ...Roelof van Zwol]]: ''[http://research.yahoo.net/files/WWW08.pdf Flickr Tag Recommendation based on Collective Knowledge]'', WWW 2008
    1 KB (142 words) - 11:49, 30 May 2012
  • ...re Movie Recommendation|2010]] and [[2011 Challenge on Context-aware Movie Recommendation]] * [[Workshop on Context-awareness in Retrieval and Recommendation]] (CaRR 2011)
    2 KB (229 words) - 17:43, 20 August 2012
  • '''Open Recommendation Platform''' (ORP) enables users to test and track their algorithm's statist ...Plista]] and allows researchers and practitioners to run A/B tests on news recommendation algorithms.
    401 bytes (51 words) - 09:33, 20 September 2014
  • '''Research paper recommendation'''.
    301 bytes (34 words) - 16:29, 10 August 2011
  • #REDIRECT [[Context-aware recommendation‎]]
    45 bytes (3 words) - 11:28, 23 September 2011
  • '''Top-N recommendation''' is a variant of or an evaluation mode for the [[item prediction]] task. Typical [[evaluation measure]]s for top-N recommendation are [[normalized discounted cumulative gain]] (NDCG) and [[precision]].
    430 bytes (63 words) - 06:18, 17 January 2012
  • [[Category: Movie recommendation]]
    522 bytes (61 words) - 12:17, 18 November 2011
  • The '''2010 Challenge on Context-aware Movie Recommendation''' ('''CAMRA 2010''') was held at the [[RecSys2010|2010 Recommender Systems ...Mood-specific Movie Similarity with Matrix Factorization for Context-aware Recommendation''' - [[Yue Shi]], [[Martha Larson]], [[Alan Hanjalic]].
    3 KB (459 words) - 12:24, 23 January 2012
  • The '''Workshop on Context-awareness in Retrieval and Recommendation''' is a series of workshops held in conjunction with the Intelligent Users ...oncerning, among other [[machine learning]], [[information retrieval]] and recommendation.
    984 bytes (130 words) - 04:57, 11 October 2011
  • '''Crowd-powered recommendation for continuous digital media access and exchange in social networks''' (''' * Stream Recommendation: real-time combination of information from collection, context, user intera
    1 KB (124 words) - 10:13, 20 September 2014

Page text matches

  • '''Tag recommendation''' is the task of predicting [[folksonomy]] [[tag]]s for a given [[user]] a ...Roelof van Zwol]]: ''[http://research.yahoo.net/files/WWW08.pdf Flickr Tag Recommendation based on Collective Knowledge]'', WWW 2008
    1 KB (142 words) - 11:49, 30 May 2012
  • * [http://www.dai-labor.de/camra2010/ CAMRa2010] Context-Aware Movie Recommendation ...60310010222/http://2011.camrachallenge.com/ CAMRa2011] Context-Aware Movie Recommendation
    2 KB (159 words) - 11:14, 28 January 2021
  • ...data and [[tag recommendation|tags]], into consideration when creating the recommendation model.
    186 bytes (27 words) - 04:25, 7 June 2011
  • ...'''Filmaster API''' is a RESTful web service API for [[Filmaster]]'s movie recommendation service, which allows 3rd party developers to develop Filmaster clients on ...rticle/filmaster-unveils-movie-recommendation-api/ Filmaster unveils movie recommendation API]
    447 bytes (53 words) - 10:30, 4 January 2014
  • '''Music recommendation''' is the personalized suggestion of musical [[item]]s, e.g. tracks, albums ...ticular task is the generation of personalized playlists, i.e. '''playlist recommendation'''.
    873 bytes (100 words) - 17:57, 15 August 2012
  • '''Item prediction''' or '''item recommendation''' is the task of predicting [[item]]s (movies, books, products, videos, jo * [[tag recommendation]]
    850 bytes (108 words) - 15:30, 1 September 2011
  • ...ndation applications]] in a heartbeat through a wide variety of built-in [[recommendation]] algorithms like user-user [[collaborative filtering]], item-item collabor ...on queries (written in SQL) and hence provides near real-time personalized recommendation to a high number of end-users who expressed their opinions over a large poo
    1 KB (162 words) - 06:04, 4 December 2014
  • ...4965-principal-product-manager-recommendation/ Principal Product Manager - Recommendation] [[Category:Fashion recommendation]]
    792 bytes (70 words) - 11:23, 26 September 2020
  • '''Open Recommendation Platform''' (ORP) enables users to test and track their algorithm's statist ...Plista]] and allows researchers and practitioners to run A/B tests on news recommendation algorithms.
    401 bytes (51 words) - 09:33, 20 September 2014
  • Although research on adaptive news retrieval and [[recommendation]] has been performed for many years, most research has been focused on rath == Open Recommendation Platform (ORP) ==
    974 bytes (146 words) - 05:55, 4 December 2014
  • '''Group recommendation''' is the task of recommending items to groups of users instead of single u or TV program recommendation for a household (groups tend not to change much over time).
    1 KB (167 words) - 08:51, 21 July 2011
  • #REDIRECT [[Music recommendation]]
    34 bytes (3 words) - 13:21, 7 November 2011
  • #REDIRECT [[Music recommendation]]
    34 bytes (3 words) - 13:22, 7 November 2011
  • #REDIRECT [[Music recommendation]]
    34 bytes (3 words) - 13:22, 7 November 2011
  • #REDIRECT [[book recommendation]]
    33 bytes (3 words) - 07:17, 17 July 2011
  • #REDIRECT [[Music recommendation]]
    34 bytes (3 words) - 13:21, 7 November 2011
  • ...sists of three major components: Generic Interfaces, Data Structures and [[Recommendation Algorithms]].
    394 bytes (47 words) - 05:43, 4 December 2014
  • #REDIRECT [[:Category: Movie recommendation]]
    45 bytes (4 words) - 07:17, 17 July 2011
  • #REDIRECT [[Open Recommendation Platform]]
    42 bytes (4 words) - 09:40, 20 September 2014
  • #REDIRECT [[Context-aware recommendation]]
    42 bytes (3 words) - 08:37, 29 September 2011
  • #REDIRECT [[:Category:Movie recommendation]]
    44 bytes (4 words) - 08:40, 29 September 2011
  • ...ick of a button calculate and visualize recommendation results of multiple recommendation algorithms.
    427 bytes (56 words) - 06:03, 4 December 2014
  • #REDIRECT [[2011 Challenge on Context-aware Movie Recommendation]]
    66 bytes (6 words) - 10:04, 20 September 2014
  • #REDIRECT [[2010 Challenge on Context-aware Movie Recommendation]]
    66 bytes (6 words) - 13:49, 14 February 2011
  • #REDIRECT [[2010 Challenge on Context-aware Movie Recommendation]]
    66 bytes (6 words) - 10:03, 20 September 2014
  • #REDIRECT [[2011 Challenge on Context-aware Movie Recommendation]]
    66 bytes (6 words) - 04:21, 19 April 2011
  • #REDIRECT [[Workshop on Context-awareness in Retrieval and Recommendation]]
    75 bytes (8 words) - 11:32, 23 September 2011
  • #REDIRECT [[Workshop on Context-awareness in Retrieval and Recommendation]]
    75 bytes (8 words) - 03:04, 17 February 2011
  • '''Top-N recommendation''' is a variant of or an evaluation mode for the [[item prediction]] task. Typical [[evaluation measure]]s for top-N recommendation are [[normalized discounted cumulative gain]] (NDCG) and [[precision]].
    430 bytes (63 words) - 06:18, 17 January 2012
  • ...h a self-implemented software, a machine learning package or a specific '''recommendation-focused''' software package or library. ...below shows a comparison of some of the more common software packages for recommendation.
    2 KB (175 words) - 09:23, 14 February 2016
  • .... Currently, the prize is 250€ per week. The contest is a good way to test recommendation algorithms with real users instead of using offline datasets.
    650 bytes (82 words) - 05:43, 5 April 2013
  • #REDIRECT [[Crowd-powered recommendation for continuous digital media access and exchange in social networks]]
    110 bytes (13 words) - 10:15, 20 September 2014
  • ...h tag. To encourage the user to utilize structured tags, a ''self learning recommendation engine'' provides suggestions of structured tags during the input process. [[Category: Tag recommendation]]
    601 bytes (80 words) - 12:07, 23 September 2011
  • [[Category: Movie recommendation]] '''moviepilot''' is the Berlin-based company behind the German movie recommendation community [http://www.moviepilot.de| moviepilot.de] and the international [
    561 bytes (71 words) - 10:55, 29 January 2014
  • ...he Galaxy of Movies''' is a Flash-based movie visualization, discovery and recommendation application, developed by A. Paterek, a top-50 [[Netflix Prize]] contestant [[Category: Movie recommendation]]
    707 bytes (91 words) - 21:00, 13 July 2014
  • '''Crowd-powered recommendation for continuous digital media access and exchange in social networks''' (''' * Stream Recommendation: real-time combination of information from collection, context, user intera
    1 KB (124 words) - 10:13, 20 September 2014
  • The '''2010 Challenge on Context-aware Movie Recommendation''' ('''CAMRA 2010''') was held at the [[RecSys2010|2010 Recommender Systems ...Mood-specific Movie Similarity with Matrix Factorization for Context-aware Recommendation''' - [[Yue Shi]], [[Martha Larson]], [[Alan Hanjalic]].
    3 KB (459 words) - 12:24, 23 January 2012
  • [[Category: Movie recommendation]] [[Category: Music recommendation]]
    744 bytes (79 words) - 12:11, 9 January 2012
  • '''Filmtipset''' is a Swedish movie recommendation community. The use case is similar to that of [[Moviepilot]], [[Jinni]] or ...ipset/tailpages.cgi?page=faq#driversajten</ref>. The company also runs the recommendation websites [[Boktipset]]<ref>http://www.boktipset.se</ref> for books, [[Somme
    1 KB (207 words) - 09:30, 19 January 2012
  • ...ade it simpler than ever to find and access music. In this scenario, music recommendation systems have become increasingly important for listeners to discover and na ...e achieved by taking into account the peculiarities of music and the music recommendation process. A successful music recommender should combine insights from user p
    3 KB (426 words) - 23:39, 9 June 2011
  • ...ommendation applications in a heartbeat through a wide variety of built-in recommendation algorithms like user-user [[collaborative filtering]], item-item [[collabor ...out-of-the-box tool for web and mobile developers to implement a myriad of recommendation applications. The system is easily used and configured so that a novice dev
    4 KB (587 words) - 20:42, 12 March 2014
  • The table below shows a comparison of some of the more common datasets for recommendation. * [[Recommendation Software]]
    1 KB (120 words) - 04:18, 11 August 2015
  • '''Research paper recommendation'''.
    301 bytes (34 words) - 16:29, 10 August 2011
  • ...re Movie Recommendation|2010]] and [[2011 Challenge on Context-aware Movie Recommendation]] * [[Workshop on Context-awareness in Retrieval and Recommendation]] (CaRR 2011)
    2 KB (229 words) - 17:43, 20 August 2012
  • ...22/is-the-kdd-cup-really-music-recommendation/ Is the KDD Cup really music recommendation?] by [[Paul Lamere]] [[Category: Music recommendation]]
    1 KB (140 words) - 06:48, 1 November 2013
  • ...s, we invite any researcher to work with our recommenders using the [[Open Recommendation Platform]] (ORP). ...ther article recommendations, advertising recommendations or meta-learning recommendation on the same platform
    933 bytes (128 words) - 08:54, 15 October 2013
  • * Circle-based Recommendation in Online Social Networks. Xiwang Yang*, ECE department, Polytechnic In; Ha * Cross-domain Collaboration Recommendation. Jie Tang*, Tsinghua University; Sen Wu, Tsinghua University; Jimeng Sun, I
    2 KB (279 words) - 12:28, 25 September 2012
  • * [[:Category:Fashion recommendation|Fashion Recommendation]] * [[:Category:Movie recommendation|Movie Recommendation]]
    5 KB (568 words) - 00:04, 1 December 2021
  • ...w.quora.com/How-do-LinkedIns-recommendation-systems-work How do LinkedIn's recommendation systems work?] at Quora
    247 bytes (24 words) - 05:43, 4 December 2014
  • * [http://tech.hulu.com/blog/2011/09/19/recommendation-system/ Hulu's Recommendation System] (blog post)
    242 bytes (27 words) - 05:41, 4 December 2014
  • ...tion and enhancement of [[novelty]] and [[diversity]]. RankSys derives its name from explicitly targeting the [[ranking]] task problem, rather than [[ratin ...mework (0.4.2) includes implementations of several collaborative filtering recommendation algorithms, a wide variety of novelty and diversity metrics and re-ranking
    2 KB (287 words) - 07:43, 14 February 2016
  • The '''Netflix Prize''' was a movie recommendation, or movie [[rating prediction]] challenge held between October 2006 and Sep [[Category: Movie recommendation]]
    1 KB (164 words) - 06:44, 11 July 2012
  • The '''Workshop on Context-awareness in Retrieval and Recommendation''' is a series of workshops held in conjunction with the Intelligent Users ...oncerning, among other [[machine learning]], [[information retrieval]] and recommendation.
    984 bytes (130 words) - 04:57, 11 October 2011
  • == Item Recommendation ==
    3 KB (303 words) - 14:24, 12 September 2012
  • ...ased iterative method in the spirit of PageRank that can be used for [[tag recommendation]].
    372 bytes (51 words) - 13:18, 6 June 2011
  • * A Framework for Trust-based Multidisciplinary Team Recommendation. Lorenzo Bossi, Stefano Braghin, Anwitaman Datta and Alberto Trombetta. ...in a Cold-Start Context: The impact of User Profile Size on the Quality of Recommendation. Shaghayegh Sahebi and Peter Brusilovsky.
    3 KB (427 words) - 11:40, 3 April 2013
  • [[Category: Tag recommendation]]
    342 bytes (40 words) - 04:47, 26 September 2011
  • * DIGTOBI: A Recommendation System for Digg Articles using Probabilistic Modeling. Younghoon Kim, Yoonj * Diversified Recommendation on Graphs: Pitfalls, Measures, and Algorithms. Onur Kucuktunc, Erik Saule,
    4 KB (493 words) - 06:00, 4 June 2013
  • [[Category: Music recommendation]]
    458 bytes (64 words) - 05:59, 4 December 2014
  • [[Category: Movie recommendation]]
    522 bytes (61 words) - 12:17, 18 November 2011
  • ...rk well in the most common real world recommendation scenario i.e. [[top-n recommendation]] based on [[implicit feedback]]. It also supplies tools for consistent and ...] item similarity method.<ref>Mark Levy, Kris Jack (2013). Efficient Top-N Recommendation by Linear Regression. In Large Scale Recommender Systems Workshop in RecSys
    2 KB (288 words) - 02:09, 4 January 2014
  • [[Category: Music recommendation]]
    488 bytes (61 words) - 08:09, 5 June 2013
  • [[Category: Tag recommendation]]
    527 bytes (71 words) - 04:47, 26 September 2011
  • [[Category: Movie recommendation]]
    443 bytes (57 words) - 15:08, 25 January 2012
  • ...in real time within the RichRelevance cloud environment (as real [[product recommendation]]s to Overstock.com’s customers). [[Category: Product recommendation]]
    2 KB (250 words) - 07:17, 30 November 2011
  • ** [http://womrad.org/] Workshop on Music Recommendation and Discovery (WOMRAD) ...op on Context-Aware Recommender Systems & Challenge on Context-Aware Movie Recommendation
    5 KB (635 words) - 13:26, 25 October 2013
  • ...elivery of recommendations are provided via [[REST]] or SOAP Web services. Recommendation algorithms are integrated through a plugin mechanism and can be added or re * managing recommendation algorithms (aka generator plugins)
    2 KB (239 words) - 04:09, 5 August 2015
  • * [[Item recommendation]]
    786 bytes (101 words) - 15:30, 1 September 2011
  • ...nge''' aims at being the best possible [[offline evaluation]] of a [[music recommendation]] system. [[Category: Music recommendation]]
    2 KB (361 words) - 08:09, 5 June 2013
  • ...jandro Bellogín]]: Comparative Recommender System Evaluation: Benchmarking Recommendation Frameworks, [[RecSys 2014]]
    745 bytes (84 words) - 08:56, 30 July 2014
  • '''SLIM''' is a library that implements a set of top-N recommendation methods based on sparse linear models. These models are a generalization to
    785 bytes (105 words) - 07:11, 4 April 2013
  • ...: Evaluation measure|evaluation measures]], recommender scenarios ([[Top-N recommendation|top-n]], [[rating prediction]], etc.) and evaluate those on datasets from d
    727 bytes (88 words) - 04:39, 26 September 2011
  • ...y includes state-of-the-art [[Collaborative Filtering]] algorithms used in recommendation systems as well as graph algorithms such as partitioning, clustering and sy
    676 bytes (97 words) - 05:56, 4 December 2014
  • * The [[MyMediaLite]] software supports [[item recommendation]] from implicit feedback.
    997 bytes (137 words) - 05:53, 12 November 2011
  • for each [[:Category:Task|recommendation task]], there is an API and a base class that make it easy to implement new * attribute-based [[diversification]] of recommendation lists
    3 KB (398 words) - 06:56, 19 December 2012
  • '''Gravity''' was the name of the [[Netflix Prize]] participants that were part of the team "[[The Ens The team later moved on to found a Budapest-based company that sells recommendation services and personalized advertisement mostly on the European market.
    1 KB (167 words) - 04:05, 23 November 2012
  • ...recommendations. Next, you’ll learn how and where to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netf
    1 KB (149 words) - 09:53, 27 September 2015
  • Adaptive kNN learns a similarity matrix that is particularly suitable for the recommendation task.
    878 bytes (116 words) - 13:13, 6 June 2011
  • The topic of the [[KDD Cup 2011]] was [[music recommendation]] from [[taxonomy|taxonomic]] and [[user-item matrix|interaction data]]. ...-Chung Chen, Bo Long: ''Locality-Sensitive Factor Models for Multi-Context Recommendation''
    3 KB (348 words) - 12:12, 18 November 2011
  • ...em.uantwerpen.be/bibrem/pubs/Jeunen2021PhDThesis.pdf Offline Approaches to Recommendation with Online Success] - [[Olivier Jeunen]] .../viewcontent.cgi?article=2209&context=oa_dissertations A Model-Based Music Recommendation System for Individual Users and Implicit User Groups] - [[Yajie Hu]]
    21 KB (2,655 words) - 18:44, 31 January 2022
  • Additional metrics can be used to measure recommendation '''biases''', e.g.,
    1 KB (159 words) - 06:03, 23 November 2015
  • * [http://ir.ii.uam.es/rue2012/ Workshop on Recommendation Utility Evaluation: Beyond RMSE]
    2 KB (264 words) - 08:17, 18 June 2014
  • [[Category: Movie recommendation]]
    1 KB (139 words) - 07:17, 19 March 2013
  • The Apaxo GmbH offers [[recommendation]] experiences and systems for merchants. The system is hosted as a multi te
    2 KB (218 words) - 05:57, 4 December 2014
  • ...mender engine for [[Python]] that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (Numpy, Scipy , Matpl ...://www.slideshare.net/marcelcaraciolo/crab-a-python-framework-for-building-recommendation-systems Presentation about Crab
    2 KB (269 words) - 11:08, 3 February 2012
  • [[Category: Movie recommendation]]
    2 KB (225 words) - 05:43, 4 December 2014
  • [[Category: Movie recommendation]]
    2 KB (192 words) - 07:22, 19 March 2013
  • * "This small design change correlated with a dramatic increase in % useful recommendation (p7)" ...ndations were provided by their friends, may have caused the difference in recommendation quality.
    6 KB (883 words) - 04:39, 26 September 2011
  • ...ining features. If you want to research new algorithms for [[context-aware recommendation]] or compose some existing models together (such as [[SVD++]], [[neighborho
    2 KB (321 words) - 22:09, 11 December 2012
  • ...[http://opim.wharton.upenn.edu/~kartikh/reading/ib1.pdf e-Commerce Product Recommendation Agents: Use, Characteristics, and Impacts]". Although the paper focuses mai ...mmender system. The framework breaks down into perceived system qualities (recommendation quality, interaction adequacy, interface adequacy; similar to Knijnenburg e
    6 KB (909 words) - 00:56, 1 March 2011
  • [[Category: Movie recommendation]]
    3 KB (363 words) - 06:57, 19 March 2013
  • ...trand of research, '''TagRec also contains algorithms for the personalized recommendation of items''' in social tagging systems. In this respect TagRec includes a no ...Krestel, P. Fankhauser, and W. Nejdl. Latent dirichlet allocation for tag recommendation. In Proceedings of the third ACM conference on Recommender systems, pages 6
    7 KB (996 words) - 07:19, 31 July 2015
  • [[Category: Movie recommendation]]
    4 KB (585 words) - 09:39, 20 August 2013
  • [[Category: Movie recommendation]]
    4 KB (283 words) - 07:15, 19 March 2013
  • :Retrieves the complete recommendation set for the specified user. If possible, items are returned in ranked order
    9 KB (1,490 words) - 19:25, 26 July 2020
  • ...menters to test the effect of aspect P on several outcomes, e.g. perceived recommendation quality (X) and expenditures (Y). However, X can in this case also be used
    9 KB (1,453 words) - 04:59, 26 September 2011

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