Property1 Property2. by Elvir Sabic. Recommender Systems Seminar Prof. Dr. Ulf Brefeld TU Darmstadt, WS 2013/14

Transcription

1 Property1 Property2 by Recommender Systems Seminar Prof. Dr. Ulf Brefeld TU Darmstadt, WS 2013/14

2 Content-Based Introduction Pros and cons Introduction Concept 1/30

3 Property1 Property2 2/30

4 Based on item profiles and user profiles e.g. with keywords, values System recommends items based on a comparison between the item profiles and a user profile Item profiles need to be specified objectively (features) User profiles are created by User settings Item-history of user (e.g. purchased, rated, viewed) Machine Learning Algorithms 3/30

5 User independence + Pros Contras Analyzing item profiles with user profile Transparancy Explicably of recommended items New Item Unpopular items can be recommended aswell Subjectivity System cannot estimate item quality Limited content analysis Analyzed content could contain too few information Over-specialization Limited degree of novelty 4/30

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9 Predictions Predict, whether Tony likes Super-man or not 8/30

10 User-based Recommendations Item-Based 9/30

11 Use the entire or a sample of user-item database to compute similarity between users or items 12/30

12 R = Value: Rating r, of item i by user u e.g. 0 to 5, where 0 represents not purchased User Vector of m different items Item Vector of n different users 13/30

13 Similarity represents distance, correlation or weight Calulate similarity w, between two users u and v, if user-based CF w, between two items i and j, if item-based CF 14/30

14 For User-based CF For Item-based CF Weight is between -1 and 1 r = average rating of user u I = items, that both users u and v have rated r, = rating for item i by user u r = average rating for item i U = users, that rated both items i and j 15/30

15 e.g. for item-based CF 16/30

16 User-based e.g. Weighted Sum of Others Ratings a = active user to predict U = users, that rated item i Item-based e.g. Simple weighted average N = all rated items 18/30

17 k-nearest neighbors (e.g. of item A with k = 2) 19/30

18 Given an active user a, identify the k most similar users Aggregate their corresponding rows in the user-item Matrix R Identify set of items C by grouping these users by their purchase as frequencies C = c, c,, c, where m is the item count and c is the frequency for all i {1,2,, m} Recommend top-n most frequent items which user a did not purchase yet Problem Limitations to scalability and performance 20/30

19 Better scalability than user-based Top-N Recommendations For each item (i.e. each column), identify the k most similar items (e.g. k = 2) Given an active user a, M = encode his purchases as an vector U 0,1 where the value 1 states a purchased item Calculate the union of the k most similar items by x = M U Recommend top-n most frequent items which user a did not purchase yet I1 I2 I3 I4 I1 0,00 0,73 0,74 0,66 I2 0,73 0,00 0,00 0,00 I3 0,74 0,17 0,00 0,95 I4 0,00 0,00 0,95 0,00 21/30

20 Data Sparsity and Scalability Implies Cold start for new users or new items Solution: Dimensionality reduction techniques Trade-off between recommendation quality and scalability Synonymy Items are treated different mistakenly Gray Sheep Inconsistent rating Shilling Attacks Companies who rate their own products most 22/30

21 Let system learn to recognize complex patterns based on training data. 23/30

22 Divide customer base into segments Treat task as a classification problem Without any other techniques, recommendation quality is low 24/30

23 Tries to explain the ratings by characterizing both items and users Factors can be obvious, like in movies e.g. action, drama, comedy But they also can be less well-defined 25/30

24 Let f be the dimensionality of the latent factor space Associate each user u with a vector p R each item i with a vector q R Interaction between user and item can be written as inner product r = q p To learn factor vectors q and p, the system minimizes the error on known ratings (training set) 26/30

25 n m n f f m User-Item Matrix R User- Feature Matrix P Item- Feature Matrix Q 27/30

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27 Content-Based Filtering Useful, if items can be described objectively Filtering One of the most successful recommender techniques Computes similarities, Neighborhood-based algorithms Machine learning, Data Mining (Cluster Models) 29/30

28 Some main challanges that must be tackled in CF Sparsity Cold start Scalability If data sets get very large Dimensionality reduction techniques are required Hybrid CF techniques are mostly more reliable e.g. Content-boosted CF Overcomes cold start Adresses sparsity problem better 30/30

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30 Linden, Greg, Brent Smith, and Jeremy York. "Amazon. com recommendations: Item-to-item collaborative filtering." Internet Computing, IEEE 7.1 (2003): Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009): 4. Deshpande, Mukund, and George Karypis. "Item-based top-n recommendation algorithms." ACM Transactions on Information Systems (TOIS) 22.1 (2004): Sarwar, Badrul, et al. "Item-based collaborative filtering recommendation algorithms." Proceedings of the 10th international conference on World Wide Web. ACM, Koren, Yehuda, Robert Bell, and Chris Volinsky. "Matrix factorization techniques for recommender systems." Computer 42.8 (2009):