Talk:Collaborative filtering

message: I think we can add a section "Applications" to introduce how the collaborative filtering techniques are applied in different products and websites.    — Preceding unsigned comment added by Nova2358 (talk • contribs) 00:05, 22 March 2012 (UTC)
thread_title: Adding an Applications Section

message: This section seems more like a tentative opinion piece. DanielLemire (talk) 20:21, 25 August 2009 (UTC)
thread_title: Innovations in Collaborative Filtering

message: # Audiobaba                     Alexa rank =  760,909          Possibly commercial
# FilmAffinity                  Alexa rank =  18,305          Commercial.
# KindaKarma                    Alexa rank =  2,780,513         And Commercial
# MovieCritic                   Alexa rank =  -                 Now Closed from Macromedia so clearly was commercial 
# Musicmobs                     Alexa rank =  193,467           And Commercial
# MyStrands                     Alexa rank =  153,177           Commercial.
# Popularism                    Alexa rank =  NO DATA           Not working 
# Rate Your Music               Alexa rank =  11,012            Commercial
* Speaking of Alexa ranks... Why is Everyone's a Critic (AR 3,392,523) listed, but not Criticker (AR 89,981) or Movielens (AR 916,562)...?  --88.193.78.58 (talk) 17:32, 7 June 2009 (UTC)
thread_title: Analysis of applications

message: Some enterprising graduate may want to create a tool that recommends Wikipedia articles you would like to edit based on the set of edits made by users.
: Heh, see SuggestBot. -- ForteTuba 00:14, 8 April 2006 (UTC)
thread_title:  For Wikipedia 

message: What really is the difference between "passive filtering" and "implicit filtering"? --Amit 20:22, 18 September 2006 (UTC)
replies: text: Only if there are sufficient references stating that they are the same would a merger be recommended. At this time the sections could probably be made clearer. --Amit 00:01, 25 October 2006 (UTC)
thread_title:  Difference bet. "passive filtering" and "implicit filtering"? 

message: The first sentence in the introduction is patently (but amusing) nonsense.
"The growth of the Internet has made it much more difficult to effectively extract useful information from all the available online information."
 Akselx (talk) 20:21, 11 September 2016 (UTC)
thread_title:  Introduction 

message: I wanted to explain why I wrote an introductory paragraph.    The term collaborative filtering is in fact a general term, like
planning and scheduling or robotics.  It includes recommender systems, and they are very popular at the moment,
however it seems insufficiently general to define CF without a broader inclusion of other applications.
Cindy Mason
www-formal.cs.stanford.edu/~cmason  —Preceding unsigned comment added by Cindymason (talk • contribs) 21:29, 27 December 2007 (UTC)
thread_title:  Addition of introductory paragraph on the definition of CF 

message: I just completed an evaluation of some of the available tools to aid in CF. I used the list provided here as a starting point, but quickly learned that a few of the open source tools are no longer being actively supported (in particular, both Cofi and Cofe are not being supported). I was wondering if someone could either remove these or potentially add a note that they are unsupported open source tools?  —Preceding unsigned comment added by Cjpeltz (talk • contribs) 13:07, 10 August 2008 (UTC)
thread_title: Cleanup References to Software Libraries 

message: The taxonomy of CF in this article is rather confusing and is, i think, inconsistent. 
I would suggest two possible taxonomies:
* J. S. Breese et al. (ref) categorized CF algorithms into two classes, either memory-based or model-based. The former is relatively slow and is suitable for small or medium scale computation; while the latter needs additional training time but offers rapid yet precise prediction (e.g., by using a Bayesian network).
* B. Sarwar et al. (ref) suggested item-based CF and attributed the old CF algorithms as "user-based".
A detailed "Algorithms" section is also probably needed, as CF is strongly related to machine learning and data mining. Merging with Recommender systems can be considered as a subtask of this process. I may set about doing this later. --Allenchue (talk) 02:42, 12 January 2009 (UTC)
thread_title:  Confusing taxonomy 

message: The cold copy of the present article header fails to make it clear that the subject involves computer software, rather than human beings. CF is as old as one fisherman asking another where the big ones are. The header is not only unclear but unneccessarily full of jargon and academe-speak -- the topic is not all that complicated to explain in a more prosaic way. Maybe explain so that even an ad exec could understand. Twang (talk) 23:11, 22 September 2009 (UTC)
thread_title: Article header is unclear, mechanical

message: I would also seek for the roots of collaborative filtering into econophysics papers.
Not to forget game theories.
Where CF is just a representation of a given state.  —Preceding unsigned comment added by 84.75.166.62 (talk) 00:23, 30 May 2010 (UTC)
thread_title:  econophysics 

message: The two steps description is simply wrong.
At least in theory. All you need is the matrix (users,objects).
Then the CF will simply evaluate the "overlap".  —Preceding unsigned comment added by 84.75.166.62 (talk) 00:32, 30 May 2010 (UTC)
thread_title:  CF in matrixes 

message: Recommendation_system#Collaborative_Filtering has significant coverage of CF. I think some information there is not present here. --Chealer (talk) 13:59, 16 January 2012 (UTC)
thread_title:  Coverage in [[Recommender system]] 

message: Apart from the summary, the rest of article does not appear to have any copyright issues. As the summary is mean to reflect the content of the article, this should be an easy fix. Cheers! Encycloshave (talk) 16:36, 9 May 2012 (UTC)
thread_title:  GA fail 

message: In the "memory based" section constant k is denoted as 1/sum(abs(sim)) but I believe it should be n/sum(abs(sim)). Someone with better knowledge of this area should however confirm before updating the article.  — Preceding unsigned comment added by JohanLand (talk • contribs) 17:43, 12 August 2012 (UTC)
thread_title:  Error in formula 

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thread_title: Impact of recent student edits

message: It looks like almost all of the material added to this article on 26 March 2012 were copied directly from a review paper.  Xiaoyuan Su and Taghi M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Adv. in Artificial Intelligence, vol. 2009, Article ID 421425, 2009. 
In particular, the paragraphs beginning with the following lines all include several sentences taken directly from the paper, with no attribution and at most extremely minor changes.
  "In practice, many commercial recommender systems. . ."
  "As the numbers of users and items grow. . ."
  "For example, the seemingly different items. . ."
  "Gray sheep refers to the users whose. . ."
Although the publisher of the journal may raise suspicions, as far as I can tell it was originally published in 2009, and seems to be the legitimate origin of this text.  The original is published under a CC Attribution license, so in principle I suppose it could be legitimately included here; however, proper attribution seems complicated.
As someone without much experience editing wikipedia articles, I'm hesitant to remove the offending material wholesale, and would welcome suggestions from seasoned editors about how best to proceed.
HiramJ (talk) 02:26, 7 December 2012 (UTC)
thread_title:  Substantial, unattributed text copied from a journal paper. 

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thread_title:  External links modified 

message: The Wikipedia article for collaborative filtering (CF) lacks contents about recent research progress in this field. We suggest to cite a paper that was published in 2014 entitled "Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges". In this survey, Shi et al. claim that user-Item (U-I) matrix provides a basis for collaborative filtering techniques, however in recent years, a large body of researches of CF algorithms being benefit from other data sources beyond U-I matrix. The data consists of additional information regarding users or items (e.g. social links, tags and comments etc), as well as information about interactions between users and items. They present an introduction to the recommendation algorithms that are developed by incorporating auxiliary data sources. This paper can not only demonstrate the importance of this topic - collaborative filtering - as well as this Wikipedia article, but also improve its quality by making it more comprehensive and state of the art. 01:12, 26 February 2017 (UTC)
replies: text: Great idea and very good reference, Xianteng Rostaf (talk) 14:54, 28 February 2017 (UTC)
thread_title:  New research progress in collaborative filtering 

message: In this article, there is a lack of discussion about recent state-of-the-art techniques that could tackle real-world problems. In recent years, with availability of different data sources which reflects various dimensions of users' preferences, the traditional representation of user-item matrix has been extended into higher order tensor representation. In a common scenario, for a 3rd order tensor we can have three dimension of users, items, and context. The context dimension usually represents time, location or devices associated to the target users that the recommendation is for. It is shown that, leveraging this additional aspect could significantly increase the effectiveness of a collaborative filtering in providing effective recommendation. Therefore, in my opinion, adding context-aware collaborative filtering to this article alongside a brief introduction of a common technique in this field, like tensor factorization, would make this article more beneficial for practical use. However before talking about tensor factorization, I suggest to provide a sub-section about matrix factorization approach that is being use to both reduce high dimensions of user-item matrix and handle data sparsity. In compare to tensor factorization, this topic it is more understandable for readers.
Besides, I think there should be more discussion about item-based and user-based collaborative filtering. The pros and cons of these two important variation of CF should be compared together and provide more details about how they can be modified to handle cold start problem in collaborative filtering.
Sdjavadi (talk) 03:51, 26 February 2017 (UTC)
thread_title:  Adding Context-aware collaborative filtering 

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thread_title:  External links modified 

message: The conclusion of the Memory-based section is dubious. Most of these conclusions seem implementation specific, while much of the previous discussion is general. As a key example, the statement "Although it can efficiently handle new users because it relies on a data structure" is not particularly meaningful or necessarily true.  — Preceding unsigned comment added by Ivirshup (talk • contribs) 06:18, 17 June 2020 (UTC)
thread_title:  Conclusion of Memory-based section needs improvement