1. 1 Comparing item-based collaborative and preference based group recommending methods Roger Carson 7.June 2007 Master of Science, Media Technology

2. 2 Outline Background Research Question Group recommending Item-based Collaborative group recommending Personal preference based recommending Collecting the information Creating the groups

3. 3 Background Recommending items to users has been used for many years. Group recommending is a little explored topic. Most recommender system focus on individuals. Being able to recommend items, or adapt environments to groups of users is becoming more and more important.

4. 4 Research question Does item-based collaborative group recommending produce the same result as personal preference-based group recommending with different group sizes.

5. 5 Group recommending Recommending an item to two or more people. The recommendation has to be something that is desired or wished by all the members of the group.

6. 6 Item-based collaborative group recommending Build an item to item matrix determining the relationships between pairs of items. (Vector Space Model) Aggregate the group preference using fuzzy majority. The group will arrive at a most preferred item. The most similar item to the item preferred by the group is recommended.

7. 7 Fuzzy preference Values for non fuzzy preference are either 1 if preferred or 0 if not. Known as a crisp set. Fuzzy preference with the help of a membership function determines the grade of preference.The values are between 1 and 0. Known as a fuzzy set.

8. 8 Fuzzy and crisp set x μ (x) 1.0 0.0 CRISP SET FUZZY SET Membership function μ(x)

9. 9 The items FILM TITLE DIRECTOR CAST KEYWORDS GENRES RELEAS YEAR DIRECTOR

10. 10 Personal preferenced recommending Based on the personal preferences of the users. Such as preference of genre, actors and directors. Aggregate the group preference Sort the films in a descending order. Resort the films for each preference that is added.

11. 11 The users PERSON AGE SEX GENRE PREF. ACTOR PREF. DIRECTOR PREF. VIEWER CATEGORY

12. 12 Collecting the information Information about each film/item was collected from www.imdb.com with the help of a php script.www.imdb.com The information about the personal preferences and viewing history was collected with a questioner.

13. 13 The questioner

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16. 16 Creating the groups Calculate the correlation coefficient between each user. Create a dissimilarity Matrix Based on the level of dissimilarity, the users are divided into 3 categories. The group sizes are 2, 3, 6, 9 and 12

17. 17 The comparison Create one recommendation for each group as well as a shortlist for each group. Compare the recommendation and the shortlist against what the users in the group wanted to see. Compare the results of the two methods

18. 18 Questions?