By Sridivya Gajjarapu (sg12k)

1. Toward the Next Generation of RecommenderSystems: A Survey of the State-of-the-Art andPossible Extensions(Gediminas Adomavicius, Member, IEEE, and Alexander Tuzhilin, Member, IEEE,IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 6, JUNE 2005 ) By Sridivya Gajjarapu (sg12k)

2. Overview • Motivation • Types of recommendation methods • Examples • Limitations • Possible extensions

3. Motivation • Recommender systems assist users to select an item among several available items. • Increase in the use of recommender systems in many online websites. • Research is being made to improve recommendation capabilities and make recommender systems applicable to even broader range of applications.

4. What does a recommender system do? • It predicts how much a user likes a particular item/product. • Gathers a list of N best items for the user. • Filters out those that are already rated by the user. • Recommends the top most items in that list. • Changes the recommendations according to the user’s feedback and other users.

5. Types of recommendation methods • Content-based recommendations: The user will be recommended items similar to the ones the user preferred in the past. • Collaborative-based recommendations: The user will be recommended items that people with similar tastes and preferences liked in the past. • Hybrid approaches: These methods combine content and collaborative methods.

6. Content-based recommendation • In this approach, a set of items which are rated or searched by the user in the past are analyzed and a profile of user is built based on the features of the objects rated by the user. • The profile is a structured representation of user interests. • This profile is used to recommend new interesting items to the user.

7. Features can be attributes or keywords of the item. For eg, a movie can have director, hero, genre as its features. • These keywords or attributes are important in determining the recommendations. • The importance of each keyword is determined by a weighting measure. • Weighting measure can be calculated using many methods; usually using cosine or TF-IDF(term frequency/inverse document frequency) measure.

8. For example, let ContentBasedProfile(C) can be defined as a vector of weights(Wc1,Wc2,Wc3,...Wck) • Each vector denotes the importance of keyword ki to the user C. • Based on these weights, items are recommended to the user.

9. Limitations of Content-based method • Data scarcity – problem of insufficient data. • Cold start problem- problem of making effective recommendations to new users without any past or historical data. • User is limited to items that are similar.

10. Collaborative-based filtering • This method works by associating a user with a set of like-minded users whose ratings and purchased items are similar to each other. • Similarity of users is computed instead of similarity of items. • Idea: If a person ‘A’ has liked an item which a person ‘B’ has also liked, then A is more likely to have B’s opinion on a different item.

11. The similarity of two users can be computed in many ways. Most common method is to measure the cosine of angle between two vectors • Similarity () = cos () =

12. Limitations of Collaborative-based method • Cold-start problem- a new item will not be recommended to a user until it is rated by another user. • Requires large number of users, so large amount of computation power is required to make recommendations. • A user with unusual tastes might have poor recommendations. • Users who have similar interests can also have interests which are different to each other.

13. Examples • Facebook, MySpace, LinkedIn and some other social networks use collaborative filtering to recommend new friends, groups and other social connections. • Most of the content-based recommender systems provide movie recommendations. Eg: Rotten Tomatoes, Internet Movie Database etc.

14. Hybrid Recommendation approach • Combination of collaborative and content based methods. • Both the methods can be combined in three ways: 1) By implementing both the methods separately and then combining their results. 2)Incorporating some of the characteristics of one approach into another. 3) Constructing a model that uses both their characteristics.

15. Example-Netflix • Recommends movies based on other users as well as using our own ratings.

16. This approach avoids certain limitations of both content-based and collaborative based approaches. • Cold-start problem still exists.

17. Some Possible Extensions • Comprehensive understanding of users and items: Collaborative filtering methods can use user and item profiles in addition to the ratings of users or items. • Multidimensionality of Recommendations: Recommender systems operate in two dimensional user x item space. Additional contextual information like time of the year, place etc. may be crucial in some applications. Eg: For recommending a vacation package

18. Keyword-based search: This approach can be used to recommend items in which the keywords searched by the user are used to find other similar records. • Non-intrusiveness: The amount of time spent on certain item or application can be used as proxy rating.

19. Conclusion • The current generation recommendation systems still require further improvements. • Research is being carried out extensively to improve the effectiveness of the recommender systems.

20. References • Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions GediminasAdomavicius, Member, IEEE, and Alexander Tuzhilin, Member, IEEEExtensions • Amazon.com Recommendations: Item-to-Item Collaborative Filtering Greg Linden, Brent Smith, and Jeremy York • Amazon.com • A Survey of Collaborative Filtering Techniques XiaoyuanSu and Taghi M. Khoshgoftaar

21. Thank you