Implicit Feedback Recommendation via Implicit-to-Explicit Ordinal Logistic Regression Mapping


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Presentation at the CARS Workshop in the context of the Conference of Recommender Systems 2011, held in Chicago.

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  • Implicit Feedback Recommendation via Implicit-to-Explicit Ordinal Logistic Regression Mapping*Xavier Amatrian works currently at Netflix, but by the time we were working on this research, he was still at Telefonica I+D
  • .. And why CARS? The reviewers of the paper criticized that this article was interesting but did not contribute extensively to the area of Context-aware Recommender Systems.However, of the different workshops so far, I considered this one the most relevant although the music recommendation was another interesting one.Still think that we contribute to this area by showing the minimal effects of several variables in the results, that I would expect other researchers could investigate and compare results.
  • - Our main motivation is the deal with the fact that the largest amount of recommender systems approaches rely on explicit information such as ratings, or metadata of users and items, butAlthough mostrecsys rely on explicit data, we face the situation that explicit feedback is scarcer than implicit feedback.- Hu, Koren in 2008 on the paper “Implicit feedback for Recommender Systems”, which is one of the few works in this area, defined some characteristics of implicit data that make difficult to work with it.
  • During September and October of the last year we run a user study on users.In a web page, they had to provide their username, we checked some
  • Sampling strategy – 100 albums were rated by each users. We discretized 3 variables to be able to do a more
  • Image on the left is the screenshot of the 1st part of the survey (demographics and consumption information) the image on the right is a sample of one of the 100 albums that each user had to rate from 0 to 5
  • Change description to the right side and change color
  • This model should account for larger variance of the DV.
  • Beta represents the coefficients for the fixed factors (if, re, gp)G_u represents the random factors
  • - Why the dataset 1 wasn’t enough- We needed to crawl more users …. But at the expense of missing some important information (we w
  • MERL stands for Mixed-Effects Linear Regression Model
  • Check which kind of popularity is used here (global or user) and check if it is an appropriate baseline.
  • Replace equations by words (if: implicit feedback, Re: recentness, gp: global popularity)
  • Implicit Feedback Recommendation via Implicit-to-Explicit Ordinal Logistic Regression Mapping

    1. 1. Implicit Feedback Recommendation via Implicit-to- Explicit OLR MappingDenis Parra (Pitt), Alexandros Karatzoglou (TID), Xavier Amatriain (TID), Idil Yavuz (Pitt) CARS 2011 October 23rd 2011
    2. 2. Outline• Introduction• Datasets• Models• Results• Discussion• Conclusion
    3. 3. A More Clear Outline• This presentation has, IMHO, 3 sections: 1. Good News 2. Not that Good News 3. Good News• Which represent, respectively 1. Results of first study on presented in UMAP 2011  2. Initial results of the study we present here  3. Expected Results after analysis of 2. (once I finish my comps) – and your feedback! 
    4. 4. Introduction• Most of recommender system approaches rely on explicit information of the users, but…• Explicit feedback: scarce (people are not especially eager to rate or to provide personal info)• Implicit feedback: Is less scarce, but (Hu et al., 2008) There’s no negative feedback … and if you watch a TV program just once or twice? Noisy … but explicit feedback is also noisy (Amatriain et al., 2009) Preference & Confidence … we aim to map the I.F. to preference (our main goal) Lack of evaluation metrics … if we can map I.F. and E.F., we can have a comparable evaluation7/12/2011 Parra, Amatriain "Walk the Talk" 4
    5. 5. Section I : Good News • User Study• Linear regression results
    6. 6. Recalling the 1st study (1/5)• users (114 in total after filtering)• For each user, we crawled all the albums they listened to send them a personalized survey
    7. 7. Recalling the 1st study (2/5)• What items should they rate? Item (album) sampling: – Implicit Feedback (IF): playcount for a user on a given album. Changed to scale [1-3], 3 means being more listened to. – Global Popularity (GP): global playcount for all users on a given album [1-3]. Changed to scale [1-3], 3 means being more listened to. – Recentness (R) : time elapsed since user played a given album. Changed to scale [1-3], 3 means being listened to more recently. 7/12/2011 Parra, Amatriain "Walk the Talk" 7
    8. 8. Recalling the 1st study (3/5)• Demographics Survey + Rating 100 albums7/12/2011 Parra, Amatriain "Walk the Talk" 8
    9. 9. Recalling the 1st study (4/5)• Gender• Age• Country• Hours per week spent on internet [int_hrs_per_week]• Hours per week listening to music online [msc_hrs_per_week]• Number of concerts per year [conc_per_year]• Do you read specialized music blogs or magazines? [blogs_mag]• Do you have experience evaluating music online? [rate_music]• How frequently do you buy physical music records? [buy_records]• How frequently do you buy music online? [buy_online]• Do you prefer listening to single tracks, whole albums or either way? [track_or_CD]7/12/2011 Parra, Amatriain "Walk the Talk" 9
    10. 10. Recalling the 1st study (5/5)• Prediction of rating by multiple Linear Regression evaluated with RMSE.• Results showed that Implicit feedback (play count of the album by a specific user) and recentness (how recently an album was listened to) were important factors, global popularity had a weaker effect.• Results also showed that listening style (if user preferred to listen to single tracks, CDs, or either) was also an important factor, and not the other ones.
    11. 11. ... but• Linear Regression didn’t account for the nested nature of ratings User 1 User n ...1 3 5 3 0 4 5 2 2 1 5 4 3 2 3 2 1 0 4 5 25 4 3 21 3 5• And ratings were treated as continuous, when they are actually ordinal.
    12. 12. So, Ordinal Logistic Regression!• Actually Mixed-Effects Ordinal Multinomial Logistic Regression• Mixed-effects: Nested nature of ratings• We obtain a distribution over ratings (ordinal multinomial) per each pair USER, ITEM -> we predict the rating using the expected value.• … And we can compare the inferred ratings with a method that directly uses implicit information (playcounts) to recommend ( by Hu, Koren et al. 2007)
    13. 13. Model [to predict rating user x item]• Model• Predicted value
    14. 14. Final MELR Model with 4 fixed effects
    15. 15. Section II : Not that Good News • Datasets I and II• Results measured as MAP and nDCG.
    16. 16. Datasets• D1: users, albums, if, re, gp, ratings, demographics/consumption• D2: users, albums, if, re, gp, NO RATINGS.
    17. 17. Experiments• First step: build MELR model using D1• For D1 and D2: split dataset in 5 parts to perform a 5-fold cross validation
    18. 18. Results
    19. 19. Section III: Expected Good News• After Analyzing our data/process
    20. 20. Lessons / Challenges (1/2)Problem/ Challenge1. Ground truth: Playcounts of albums or tracks?2. Quantization of playcounts (implicit feedback),recentness, and overall number of listeners of an album(global popularity) [1-3] scale v/s raw playcounts3. Defining Relevancy of recommended elements (tocompare with the raw playcounts)
    21. 21. Lessons / Challenges (2/2)Problem/ Challenge4. Additional/Alternative metrics for evaluation [MAPand nDCG used in the paper]5. New Survey (In order to deal with the issues ofidentifying “actual” relevancy in dataset2)6. Significance of level-2 variables: track_or_CD (study1 v/s 2, where concerts_per_year was significant)
    22. 22. … so• Lots of work to do [after my comps]• Questions, Suggestions?
    23. 23. Is this the end?7/12/2011 Parra, Amatriain "Walk the Talk" 23
    24. 24. Thanks!• Denis Parra
    25. 25. Backup slides
    26. 26. Complete Results for D2 AVG (MAP) AVG(nDCG) SD (MAP) SD(nDCG)Koren 0.101428473 0.271841949 0.001504383 0.001906666KorenLog 0.123444659 0.295368576 0.002105906 0.002239792logit_3 0.12225702 0.294351155 0.000868738 0.001100289popularity 0.01777665 0.136672774 0.000937768 0.00086512linear_2 0.123417895 0.295026219 0.001830371 0.001554675linear_3 0.122317675 0.294211465 0.001744534 0.00212961
    27. 27. Distribution of ratings
    28. 28. Actual Distribution of ratings
    29. 29. Some Intuition About the Results• Distributions of ratings in both datasets
    30. 30. M2:implicitfeedback &recentness 4 Regression Analysis M1: implicit feedbackM4: M3: implicitInteraction of feedback,implicit recentness,feedback & globalrecentness popularity • Including Recentness increases R2 in more than 10% [ 1 -> 2] • Including GP increases R2, not much compared to RE + IF [ 1 -> 3] • Not Including GP, but including interaction between IF and RE improves the variance of the DV explained by the regression model. [ 2 -> 4 ] 7/12/2011 Parra, Amatriain "Walk the Talk" 30
    31. 31. 4.1 Regression AnalysisModel RMSE1 RMSE2User average 1.5308 1.1051M1: Implicit feedback 1.4206 1.0402M2: Implicit feedback + recentness 1.4136 1.034M3: Implicit feedback + recentness + global popularity 1.4130 1.0338M4: Interaction of Implicit feedback * recentness 1.4127 1.0332• We tested conclusions of regression analysis by predicting the score, checking RMSE in 10- fold cross validation.• Results of regression analysis are supported.7/12/2011 Parra, Amatriain "Walk the Talk" 31
    32. 32. 4.2 Regression Analysis – Track or AlbumModel Tracks Tracks/ Albums AlbumsUser average 1.1833 1.1501 1.1306M1: Implicit feedback 1.0417 1.0579 1.0257M2: Implicit feedback + recentness 1.0383 1.0512 1.0169M3: Implicit feedback + recentness + global 1.0386 1.0507 1.0159popularityM4: Interaction of Implicit feedback * recentness 1.0384 1.049 1.0159 • Including this variable that seemed to have an effect in the general analysis, helped to improve accuracy of the model 7/12/2011 Parra, Amatriain "Walk the Talk" 32
    33. 33. Is this the end?7/12/2011 Parra, Amatriain "Walk the Talk" 33