Sequential Recommender Systems

Transcription

1 Recommender Stammtisch, Zalando, 26/6/14 Sequential Recommender Systems! Knowledge Mining & Assessment

2 Collaborative Filtering Prof. Dr. 2

3 Collaborative Filtering Prof. Dr. 3

4 Collaborative Filtering Prof. Dr. 4

5 Collaborative Filtering Prof. Dr. 5

6 Collaborative Filtering Prof. Dr. similar similar 6

7 Collaborative Filtering similar Prof. Dr. similar similar 7

8 Collaborative Filtering similar Prof. Dr. similar similar 8

9 Collaborative Filtering similar recommend Prof. Dr. similar similar 9

10 Machine Learning/Data Mining Information Extraction & Aggregation Personalization Recommendations Prof. Dr. 10

11 The Web... 11

12 and beyond 12

13 Computer Games Prof. Dr. 13

14 Adaptive Game Masters 1.Estimate strength θ of human player 2.Play game with P(player wins θ ) = 1/2 3.Receive feedback 4.Update estimate θ Prof. Dr. 14

15 Testing Adaptive Game Masters competency testee 1.Estimate strength θ of human player Choose item 2.Play game with P(player wins θ ) = 1/2 correct answer 3.Receive feedback 4.Update estimate θ Prof. Dr. 15

16 estimate belief competency 16

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56 Sports Analytics Haase & Brefeld, Mining Positional Data Streams, in preparation Prof. Dr. 56

57 Positional Data Streams Trajectories = Positions of objects/people over time Team sports Routing problems Public transport Computer games Analyse tactics, movement patterns Recommend (counter) strategy 57

58 Y!News Recommendation Haider, Chiarandini, Brefeld, Jaimes, Contextual Models for User Interaction on the Web, IPAT 2012 Haider, Chiarandini, Brefeld, Discriminative Clustering for Market Segmentation, KDD 2012 Input Output timestamp sequence of subsequent pageviews referrer first pageview Observed variables used to classify a new session location of click anchor text of clicked link Targeting strategy devised only from behavior All variables are used for segmentation 58

59 Textbook Personalization Analyse sequential navigation patters: Prof. Dr. 59

60 Its all about User Understanding Prof. Dr. 59

61 and Behaviour over Time sequence of actions sequential feedback trajectories sequence of viewed pages Take sequential nature of the data into account Include context (e.g., time) The goal is a ranked list of appropriate candidates Intrinsically, the above problems are recommendation tasks! 61

62 Inter-dependent Variables Prof. Dr. 60

63 Collaborative Filtering A user views the following product:!!!! Which item shall we recommend next? 63

64 Detecting Topics of User Sessions Tavakol & Brefeld, MDPs for Detecting Topics of User Sessions, submitted to RECSYS 2014 A user views the following sequence of products:!!!! What is the user s goal of the session? Which item shall we recommend next? 64

65 Attribute View Topic Category Shirt Shirt Shirt Shirt Shirt Colour Dark Blue Black Dark Brown Black Dark Colours Gender Women Women Unisex Women Women Price Cheap Expensive Expensive Cheap Any t = 1 t = 2 t = 3 t = 4 65

66 Independence Assumption exact model infeasible feasible approximation category brand price category brand price X 1 t k X j t k X n t k X 1 t k X j t k X n t k? X 1 t 2 X 1 t 1 X 1 t X j t 2 X j t 1 X j t X n t 2 X n t 1 X n t X 1 t 2 X 1 t 1 X 1 t X j t 2 X j t 1 X j t X n t 2 X n t 1 X n t 66

67 P P Factored MDPs ca Learn Q( -function using historic data an can be use The value of Q(s j,x j ) is proportional e us to the probability n b that an a item user wclicks on item x j given session (s j, m w ite Compute topic by min-max normalisation:!! q(x j = x j s j )= Q(s j,x j ) min x 0 j [Q(s j,x 0 j)] max x 0 j [Q(s j,x 0 j )] min x 0 [Q(s j,x 0 j j )], Thresholding! 67

68 Empirical Evaluation Transaction data from Zalando About 1.7 million user sessions More than 24 million clicks Markov assumption of order 1-4 Accuracy: Measure whether next viewed item is in the predicted topic of the session 68

69 Impact of Threshold Number of Topics k = 1 k = 2 k = 3 k = 4 Accuracy Accuracy M2: k = M2: 1 k = 1 M2: k = M2: 2 k = 2 M2: k = M2: 3 k = 3 M2: k = M2: 4 k = Threshold Threshold Threshold topics. Center: Size of topics. Right: Impact of topic threshold. Size of topics decreases ictability of the icted with high wrt threshold Topic accuracy decreases wrt threshold tified using Latent Dirichlet Allocation (LDA) [6] and varia thereof. The evolution of topics in data streams is for instanc tected by Knowledge modelling Mining & Assessment time [33] Groupor by introducing additional 69 de

70 Variance of Topics Variety in Topics M2: k = 1 M2: k = 2 M2: k = 3 M2: k = Time Step within the Session Uncertainty decreases in length of session Markov assumption influences convergence 70

71 Topic-driven Recommendations Turn!! ca Q( -values into probabilities (softmax) an Pr(X j = x j s j )= exp{q(s j,x j )} P exp{q(s j,x 0 i j} x 0 j (i; Rank items according to sum of log-probabilities (exploiting independence) ny nx score(i; s) = P (X j = x j s j ) / log P (X j = x j s j ) j=1 j=1 71

72 Topic-driven Recommendations Average Rank M2: k = 1 M2: k = 2 M2: k = 3 M2: k = 4 TM CF collaborative filtering w matrix factorisation collaborative filtering w topic models Time Step within the Session our approach w different Markov assumptions Topic-driven recommendation outperforms traditional CF/MF approaches 72

73 Machine Learning/Data Mining Information Extraction & Aggregation Personalization Recommendations Prof. Dr. 73

74 Information Extraction & Aggregation Personalization Big Data Recommendations Prof. Dr. 74

75 Conclusion Individual recommendations can increase user experience Exploit sequential nature of the data Incorporate context variables Draw from tasks that exhibit the same/similar abstract problem setting Prof. Dr. 75