A time-based approach to effective recommender systems using implicit feedback

1. A time-based approach to effective recommender systems using implicit feedback Tong Queue Lee1, Young Park2, Yong-Tae Park3 Dongyang Technical College1, Braddley University2, Seoul National University3 Expert Systems with Applications, vol 34, issue 4, 2008 2009. 03. 27. Presented by Jae-won Lee, IDS Lab., Seoul National University

2. Introduction • Most collaborative filtering systems rely on explicit feedback such as ratings • Sometimes it is difficult to obtain explicit feedback • To acquire more accurate results, this paper constructs a pseudo rating matrix from implicit feedback • Incorporate temporal information such as user purchase time and item launch time

3. Related Works • Using time information • There are two kinds of time based approaches • Product launch time • User rating time • These data are used in CFs with explicit feedback • This paper proposes a novel CF with implicit feedback incorporating both time information (product launch time and user rating time)

4. Time based CF with Implicit Feedback • Pseudo rating matrix using temporal information • This paper constructs a pseudo rating matrix from implicit feedback such as purchase information • Simple Pseudo Rating Matrix • We can simply assign 1 as a rating value when a user purchases an item

5. Time based CF with Implicit Feedback • Time-based Pseudo Rating Matrix • We incorporate two kinds of temporal information • LTime ; an item’s launch time, PTime : a user’s purchase time • We define a rating function w that derives ratings from implicit feedback such as LTime and PTime

6. Time-based Approach to CF • Time-based CF consists of the following steps • Collecting implicit feedback data • Constructing a pseudo rating matrix • Computing neighbors • Recommending items

7. Time-based Approach to CF • Collecting implicit feedback data • This paper collects two kinds of data • User purchase time data • Item launch time data • These data are usually available in a typical e-commerce environment • Constructing a pseudo rating matrix • The rating function will depend on the type of product/services • e.g., If users are sensitive to the item’s launch time, the rating function should give more weight to new products • We can find the right rating function among several candidates • Using training data, we compare the effectiveness of each rating function to the given product and choose the best function

8. Time-based Approach to CF • Computing similar neighbors • User based CF; finding similar users from pseudo rating matrix • Pearson correlation • Cosine similarity • Item based CF; finding similar items from pseudo rating matrix • Pearson correlation • Cosine similarity • Where Pai is the user a’s current preference to the item i

9. Time-based Approach to CF • Recommending items • User-based CF • a and c are two users, i is an item, sim(a, c) is the similarity between users a and c • Item-based CF • a is a user, i and k are an item, sim(i, k) is the similarity between users i and k

10. Experiments and Results • Implicit Feedback Dataset • Transaction data of SK Telecom from June 2004 to August 2004 • Total number of users was 1922 • Total number of items was 9131 • Experiments Design • We used two kinds of rating functions • Coarse rating function denoted as W3 • Fine rating function denoted as W5

11. Experiments and Results • Experiments Design (cont’d) • W3 is designed as follows • Divide the item’s launch time to three groups • Divide the user’s purchase time to three groups • Each cell is assigned a rating weight from 0.7 to 3.3

12. Experiments and Results • Experiments Design (cont’d) • W5 is designed similarly from 0.2 to 5.0

13. Experiments and Results Empirical Results – User-based CF

14. Experiments and Results Empirical Results – Item-based CF

15. Conclusion - Experimental Summary In the case of User-based CF, we can see quite significant improvement (34~47%) by incorporating temporal information In the case of Item-based CF, the improvement (10~25%) is less than that of the user-based CF The improvement of the finer rating function W5 over the coarser rating function W3 is not that significant (1~3% for user-based CF and 1~8% for item-based CF)

16. Opinion • Pros. • Novel approach incorporating implicit feedback and temporal information • Cons. • There is no experiments related to the effects of different temporal information • Too ad hoc to define the rating function’s weights