Changes between Version 5 and Version 6 of Drupal/UserBehaviourAnalyser


Ignore:
Timestamp:
07/21/10 14:15:34 (9 years ago)
Author:
eleni.kargioti
Comment:

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  • Drupal/UserBehaviourAnalyser

    v5 v6  
    66The following are considered part of the user behaviour and worth analysing: 
    77 
    8 ·        ''Browsing history'': Drupal has built-in support for storing the date and time of the last interaction of a user with a page. This information is stored in a database table and is considered useful because it may reveal the pages of interest to a user, along with the recency of the visit. Nevertheless, the information of the intensity of the user's interest towards a page is missing, since it is not possible to know how many times and for what purpose a user has interacted with a page. 
     8*        ''Browsing history'': Drupal has built-in support for storing the date and time of the last interaction of a user with a page. This information is stored in a database table and is considered useful because it may reveal the pages of interest to a user, along with the recency of the visit. Nevertheless, the information of the intensity of the user's interest towards a page is missing, since it is not possible to know how many times and for what purpose a user has interacted with a page. 
    99 
    10 ·        ''User actions'': The lack of information by the browsing history is compensated by the tracking of user actions. The OrganiK developers have extended the Drupal code in order to store every interaction between a user and a node. Thus, a suitable database table stores and differentiates between the user actions on a content item, i.e. viewing, editing, viewing a posted comment, posting a comment. This information is then utilised to compute a function for the importance of content items to users. 
     10*        ''User actions'': The lack of information by the browsing history is compensated by the tracking of user actions. The OrganiK developers have extended the Drupal code in order to store every interaction between a user and a node. Thus, a suitable database table stores and differentiates between the user actions on a content item, i.e. viewing, editing, viewing a posted comment, posting a comment. This information is then utilised to compute a function for the importance of content items to users. 
    1111 
    12 ·        ''Use of annotation terms'': The core Drupal code is extended in order to track and store the use of annotation terms. Drupal only stores the list of terms used to annotate a content item, but misses the information of which user has created and/or used an annotation term. This information is utilised to compute a function for the popularity of annotation terms, based on their use within OrganiK. 
     12*        ''Use of annotation terms'': The core Drupal code is extended in order to track and store the use of annotation terms. Drupal only stores the list of terms used to annotate a content item, but misses the information of which user has created and/or used an annotation term. This information is utilised to compute a function for the popularity of annotation terms, based on their use within OrganiK. 
    1313 
    1414·        ''Ratings'': By utilising a suitable module (“[http://drupal.org/project/fivestar Fivestar]” ratings) users of OrganiK may rate the content items they read on a scale of 1 to 5 stars. These rating are stored and when analysed can offer valuable information on the preferences of users. 
     
    3434===== '''[http://drupal.org/project/fivestar_rec Fivestar recommender]''': ===== 
    3535This recommender uses the pair “user – content item” with weight equal to the rating. The recommender can then compute a list of users with similar tastes to the current user as well as a list of recommended content items which were highly rated by users with similar tastes to the current user.''''''''''' 
     36====== Code ====== 
     37 * [source:trunk/drupal/organikdrupal/sites/all/modules/fivestar_rec fivestar_rec] and [source:trunk/drupal/contributions/modules/organik_fivestar_rec organik_fivestar_rec] 
    3638 
    3739===== '''[http://drupal.org/project/history_rec Browsing History Recommender]''': ===== 
    3840This recommender takes into consideration the browsing history of users and uses the pair “user – content item” with weight equal to the recency of the user's interaction with the item. As such, the recommender is able to compute a list of recommended content items for the current user based on the browsing history of similar users.''''''''''' 
     41====== Code ====== 
     42 * [source:trunk/drupal/organikdrupal/sites/all/modules/history_rec history_rec] and [source:trunk/drupal/contributions/modules/organik_history_rec organik_history_rec] 
    3943 
    4044===== User Actions Recommender: ===== 
    4145This recommender takes into consideration the actions a user performed on a content item and uses the pair “user – content item” with weight equal to the importance of the item for the specific user. The recommender then computes a list of recommended content items for the specific user, as it is described below: 
     46 
     47====== Code ====== 
     48 * [source:trunk/drupal/contributions/modules/user_action_history_rec user_action_history_rec] 
    4249 
    4350====== Content Recommendations ====== 
     
    5764This recommender takes into consideration the use of annotation terms by users and uses the pair “user – annotation term” with weight equal to the popularity of a term based on its internal use. The recommender can then compute a list of recommended annotation terms for the specific user, as described below: 
    5865 
     66====== Code ====== 
     67 * [source:trunk/drupal/contributions/modules/tag_history_rec tag_history_rec] 
     68 
    5969====== Tag Recommendations ====== 
    6070The purpose of this recommender is to support users in retrieving resources organised in an ad-hoc manner, based on a structure that emerges from the tagging actions of the community. The recommender extends the collaborative filtering technique in the domain of folksonomies, in order to predict tags that the user might not have used before, but which could be relevant to her interests. The prediction results from analysing the tagging activity of the community of users by exploiting the collaborative filtering methodology. In this case we are defining the recommendation problem in the space of users, C, and tags, T. The usefulness of a tag, t, to a user, c, is estimated based on a utility function, (formula 3), that takes into consideration the number of times the tag is used by the user and combines it with a measure of tag popularity. In various papers the popularity of a tag is synonymous to the frequency of its use. In the current implementation a tag popularity measure was employed that silences the influence of tags that were used only once or used multiple times by a single user. The resulting recommendations are based on user-to-user collaborative filtering analysis and make personalised suggestions of tags outside the scope of a specific content item.