Changes between Version 10 and Version 11 of Drupal/Recommender


Ignore:
Timestamp:
07/27/10 10:01:24 (9 years ago)
Author:
eleni.kargioti
Comment:

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  • Drupal/Recommender

    v10 v11  
    1212=== Using === 
    1313When editing a content item (node) OrganiK analyses the text of the node and suggests (based on content analysis) the most descriptive and important keywords which may be used for annotation purposes. As the user edits and changes the text, the list of suggested tags is updated. 
     14 
     15=== How it works === 
     16The text of the content item is analysed based on a linguistic ([wiki:ContentAnalyser/ContentAnalyserImplementation natural language processing]) algorithm, a statistical algorithm ([wiki:ContentAnalyser/LatentTopicAnalysis latent Dirichlet allocation]) and an algorithm that is based on the [http://en.wikipedia.org/wiki/Tf%E2%80%93idf TF-IDF] (term frequency–inverse document frequency) measure which helps to evaluate how important a word is to a document in a collection or corpus. Each algorithm identifies the most important words or phrases of the text to be suggested as tags. Every word or phrase has a score with value within the range 0 and 1; the greater the score, the more important and descriptive the tag is for the specific text. 
     17 
     18After each algortihm has analysed the text the results are aggregated and combined. For the cases where the same word or phrase is suggested by more than one algorithm its final score results from summing up all individual scores. Finally the scores of all results are divided by 3 (to make sure that the score remains between 0 and 1 even for the results that have an aggregated score. 
     19 
    1420 
    1521=== Configuration === 
     
    3945=== Using === 
    4046When visiting the page of a content item (node) a block with a list of similar content items shows up on the right side. Moreover the same block appears when the user edits the node. The list of related content items is updated as the user changes the text of the node. 
     47 
     48=== How it works === 
     49The related content recommender combines results from a content-based recommender (CN-based) and collaborative filtering-based recommenders (CF-based).  
     50 
     51The content-based recommender is based on a statistical algorithm ([wiki:ContentAnalyser/LatentTopicAnalysis latent Dirichlet allocation]) to identify the most important topics of the text. Based on this analysis and the topic to topic similarity, the algorithm is able to compute similarities between content items, and finally suggest the most similar content items for the specific text. 
     52 
     53The collaborative filtering recommenders run periodically and analyse the existing information on user behaviour to compute similarities between users and items, and to predict interesting items for users. The information analysed involves the browsing history (CF-B), the actions users perform on content items (edit, comment, etc.) (CF-UA), and the ratings of users on content items (CF-R), (see [wiki:Drupal/UserBehaviourAnalyser here] for further details). 
     54 
     55Each algorithm suggests similar and/or related content item. Every content item has a score with value within the range 0 and 1; the greater the score, the more similar and/or related the content item is. 
     56 
     57The results of each algortihm are aggregated and combined. For the cases where the same content item is suggested by more than one algorithm its final score results from aggregating all individual scores: 
     58 
     59Final Score = 0.4 * [CF-based] + 0.6 * [CN-based], 
     60 
     61where CF-based = (0.2 * CF-B ) + (0.4 * CF-UA) + (0.4 * CF-R) 
     62 
    4163 
    4264=== Configuration === 
     
    6890In OrganiK every user has a personal space (My Account) where she can manage some personalised settings (e.g. subscriptions) and view personalised messages (notifications) and recommendations. The personalised recommendations pages provides personalised recommendations of content items (nodes), tags and users that are predicted to be interesting and important for the specific user. The predictions are based on analysing the user behaviour and employing Collaborative Filtering algorithms. 
    6991 
     92=== How it works === 
     93The personalised items recommender offers recommendations of content items and tags that are predicted to be interesting for the specific user, as well as recommendations of users with similar interests. To this end 4 collaborative filtering-based recommenders (CF-based) are employed that analyse the existing information on user behaviour. The information analysed involves the browsing history (CF-B), the actions users perform on content items (edit, comment, etc.) (CF-UA), the ratings of users on content items (CF-R), and the tagging activity of users (CF-T). 
     94 
     95For further details see the [wiki:Drupal/UserBehaviourAnalyser User Behaviour Analysis]. 
     96 
     97==== Recommended Content Items ==== 
     98The CF-B, CF-UA and CF-R algorithms suggests content items that are predicted to be related to user interests. Every content item has a score with value within the range 0 and 1; the greater the score, the more relevant the content item is. 
     99 
     100The results of each algortihm are aggregated and combined. For the cases where the same content item is suggested by more than one algorithm its final score results from aggregating all individual scores: 
     101 
     102Final Score =  (0.2 * CF-B ) + (0.4 * CF-UA) + (0.4 * CF-R) 
     103 
     104==== Recommended Tags ==== 
     105The CF-T algorithm exploits the tagging behaviour of users (which tags are used by which users, how popular a tag is) and predicts tags that are considered important for users. Each recommended tag has a score in the range of 0 to 1. The greater the score the more relevant the tag is. 
     106 
     107==== Similar Users ==== 
     108The CF-UA and CF-R algorithms base their predictions on the computed similarities between users. These similarities are used to suggest users that are though to have similar interests with the specific user. Every suggested user has a score with a similarity value within the range 0 and 1; the greater the score, the more similar the content item is. 
     109 
     110The results of each algortihm are aggregated and combined. For the cases where the same user is found similar by more than one algorithm its final score results from aggregating the individual scores: 
     111 
     112Final Similarity Score =  (0.6 * CF-UA) + (0.4 * CF-R) 
     113 
    70114=== Configuration === 
    71115To view the personalised items recommendations it is necessary that the modules below are enabled.