A Preference-based Recommender System
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1 A Preference-based Recommender System Benjamin Satzger, Markus Endres, Werner Kießling University of Augsburg Institute for Computer Science EC-Web 2006 Markus Endres A Preference-based Recommender System EC-Web 1 / 24
2 Outline 1. Recommender Systems 2. Preferences 2.1 Categorical Preferences 2.2 Numerical Preferences 2.3 Complex Preferences 3. Similarity of users 4. Evaluation 5. Summary and Outlook Markus Endres A Preference-based Recommender System EC-Web 2 / 24
3 1. Recommender Systems Markus Endres A Preference-based Recommender System EC-Web 3 / 24
4 1. Recommender Systems Collaborative filtering is guessing Preferences - Joe Barillari Super- Lord of Star Anatomy Titanic man the Rings Wars Anton Bert Charly Donald ??? Markus Endres A Preference-based Recommender System EC-Web 4 / 24
![1. Recommender Systems Collaborative Filtering Pearson Correlation Coefficient [P. Resnik, J. S. Breese] Prediction of movie vote based on user ratings User rating necessary for prediction Used e.](/docs-images/85/92063420/images/5-0.jpg "g. by the GroupLens project, Amazon,... Markus Endres A Preference-based Recommender System EC-Web 5 / 24")
5 1. Recommender Systems Collaborative Filtering Pearson Correlation Coefficient [P. Resnik, J. S. Breese] Prediction of movie vote based on user ratings User rating necessary for prediction Used e.g. by the GroupLens project, Amazon,... Markus Endres A Preference-based Recommender System EC-Web 5 / 24
6 1. Recommender Systems The Target Based on user preferences [W. Kießling, J. Chomicki] Prediction of movie vote based on user preferences Number of consistent and inconsistent elements Multi-attribute recommender Better than Pearson s correlation Markus Endres A Preference-based Recommender System EC-Web 6 / 24
7 2. Preferences are ubiquitous in all our daily lives Markus Endres A Preference-based Recommender System EC-Web 7 / 24
![Preference [W. Kießling] 2.](/docs-images/85/92063420/images/8-0.jpg "Preferences Database Community is a strict partial order P = (A, < P ), where < P dom(a) dom(a).")
8 Preference [W. Kießling] 2. Preferences Database Community is a strict partial order P = (A, < P ), where < P dom(a) dom(a). x < P y means I like y better than x Markus Endres A Preference-based Recommender System EC-Web 8 / 24
9 2.1 Categorical Preferences Overview P=POS(Director,{Peter Jackson}) George Lucas < P Peter Jackson James Cameron < P Peter Jackson NEG-preference dual POS-preference POS/POS, POS/NEG as combinations Markus Endres A Preference-based Recommender System EC-Web 9 / 24
10 2.1 Categorical Preferences Similarity Measure Similarity of categorical preferences P 1 and P 2 Algorithm Example numc(p 1, P 2 ) numi (P 1, P 2 ), n = dom(a) n (n 1) P 1 : Cameron < P1 Lucas < P1 Jackson P 2 : Jackson < P2 Lucas < P2 Cameron Markus Endres A Preference-based Recommender System EC-Web 10 / 24
11 2.1 Categorical Preferences Similarity Measure Similarity of categorical preferences P 1 and P 2 Algorithm Example numc(p 1, P 2 ) numi (P 1, P 2 ), n = dom(a) n (n 1) P 1 : Cameron < P1 Lucas < P1 Jackson P 2 : Jackson < P2 Lucas < P2 Cameron numc(p 1, P 2 ) = < P1 < P2 = 0 numi (P 1, P 2 ) = < P1 < δ P 2 = 3 Similarity = = 0 Markus Endres A Preference-based Recommender System EC-Web 10 / 24
12 Examples 2.2 Numerical Preferences Overview LOWEST / HIGHEST (Runtime) AROUND(Runtime, 120min) BETWEEN(Year of publication, [1997, 2000]) Specials: No known similarity computation until now dom(a) can be infinite (numerical data) Concepts for categorical preferences inapplicable P can be expressed by the SCORE preference Markus Endres A Preference-based Recommender System EC-Web 11 / 24
13 2.2 Numerical Preferences Choosing the SCORE Preference SCORE preference represents all numerical preferences (depending on the choice of f ) Preference Choice of f LOWEST(A) f (x) = x HIGHEST(A) f (x) = x AROUND(A, x) f (x) = { x if x x 2 x x if x > x BETWEEN(A, [x 1, x 2 ]) f (x) = { x 1 if x [x 1, x 2 ] x if x < x 1 x 1 + x 2 x if x > x 2 Markus Endres A Preference-based Recommender System EC-Web 12 / 24
14 2.2 Numerical Preferences Similarity Measure Similarity of numerical preferences P 1 and P 2 Algorithm 1. Express preferences as SCORE(A, f 1 ) and SCORE(A, f 2 ) 2. Similarity of P 1 and P 2 in [a, b] 1 b a f 1 f 2 2 (b a) Markus Endres A Preference-based Recommender System EC-Web 13 / 24
15 2.2 Numerical Preferences Example P 1 = BETWEEN(A, [5, 8]) 5 if x [5, 8] f 1 (x) = x if x < 5 13 x if x > 8 P 2 = HIGHEST (A) f 2 (x) = x Markus Endres A Preference-based Recommender System EC-Web 14 / 24
16 2.2 Numerical Preferences Similarity in [0, 10] 10 0 f 1 f 2 = 7 Similarity: 1 R 10 0 f 1 f 2 2 (b a) = = 0.65 Markus Endres A Preference-based Recommender System EC-Web 15 / 24
17 2.3 Complex Preferences Overview How important is a preference for a person? Combination of basic preferences Prioritized: P 1 & P 2 Preference P 1 is more important than P 2 Pareto: P 1 P 2 Preference P 1 is equal important than P 2 Example POS(Director, Lucas) & POS(Actor, Dreyfuss) Markus Endres A Preference-based Recommender System EC-Web 16 / 24
18 2.3 Complex Preferences Similarity Measure Build preference order based on all complex preferences Preference order is a partial order on a set of preferences Compare users based on these preference orders Example Anne: POS(DVD, {Superman}) HIGHEST (RT ) P 1 P 2 P 1 POA P 2 P 2 POA P 1 Julia: HIGHEST (RT ) & POS(DVD, {Superman}) P 2 & P 2 P 2 POJ P 1 Markus Endres A Preference-based Recommender System EC-Web 17 / 24
19 2.3 Complex Preferences Similarity Measure Similarity of two preference orders numc(po 1, PO 2 ) numi (PO 1, PO 2 ) + numeq(po 1, PO 2 ) n(n 1) numeq(, ): number of equal important preferences Markus Endres A Preference-based Recommender System EC-Web 18 / 24
20 Given two users Similarity of users Determine all user preferences Build preference orders based on their complex preferences Compute similarity of all corresponding preferences and preference orders, respectively Average the results Markus Endres A Preference-based Recommender System EC-Web 19 / 24
21 Given two users Similarity of users Determine all user preferences Build preference orders based on their complex preferences Compute similarity of all corresponding preferences and preference orders, respectively Average the results Example Anne: POS(DVD, {Superman}) HIGHEST (RT ) Julia: HIGHEST (RT ) & POS(DVD, {Superman}) Result: 0.83 Markus Endres A Preference-based Recommender System EC-Web 19 / 24
22 4. Evaluation Data Set MovieLens + further data from IMDB (director, country,...) 948 users with votes on movies Markus Endres A Preference-based Recommender System EC-Web 20 / 24
23 4. Evaluation Training and Test Data 5 different passes Training-/Test data: 80% : 20% Test data disjoint Markus Endres A Preference-based Recommender System EC-Web 21 / 24
24 4. Evaluation Results Neighborhood Algorithm Pearson PrefMovID PrefMovID+Dir Mean average error Multi-attribute recommender MovieID + Director Only differ in the computation of the user similarity Markus Endres A Preference-based Recommender System EC-Web 22 / 24
25 Summary 5. Summary and Outlook Preference-based approach better than Pearson for a small neighborhood Advantages for start-up e-shops with only a few customers (cold start problem) Multi-attribute recommender better than single-attribute User preferences can be automatically mined from log files Markus Endres A Preference-based Recommender System EC-Web 23 / 24
26 Summary 5. Summary and Outlook Preference-based approach better than Pearson for a small neighborhood Advantages for start-up e-shops with only a few customers (cold start problem) Multi-attribute recommender better than single-attribute User preferences can be automatically mined from log files Outlook Reduce increased runtime compared to Pearson Multi-attribute recommender with other data sets Preference-based item-to-item recommender system Markus Endres A Preference-based Recommender System EC-Web 23 / 24
27 Questions and Comments A Preference-based Recommender System Markus Endres endres@informatik.uni-augsburg.de Markus Endres A Preference-based Recommender System EC-Web 24 / 24
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