Presentation on theme: "Tagommenders: Connecting Users to Items Through Tags Shilad Sen Macalester College Jesse Vig, John Riedl GroupLens Research."— Presentation transcript:
Tagommenders: Connecting Users to Items Through Tags Shilad Sen Macalester College Jesse Vig, John Riedl GroupLens Research
Tagommenders 1.Analyze user interactions to infer liking (preferences) for tag concepts. 2.Recommend items related to tag concepts liked by users.
9 of 32 Tagommender Goals Recommend items using just tags. (Delicious) Improve item recommendations with ratings by by using tags. (LibraryThing / Amazon) accuracy flexibility explainability (Vig, IUI 2009).
10 of 32 Tagommender Flow Chart WALL-E animationrobotspixar tag preference inference tag-based recommendation
11 of 32 MovieLens Tagging Tagging introduced in ,000 distinct tags 127,000 tag applications: 4000 users applied >= 1 tag 7700 movies with >= 1 tag app
12 of 32 Outline Tag preference inference Item recommendation Auto-tagging and wrap-up
13 of 32 Outline Tag preference inference Item recommendation Auto-tagging and wrap-up
14 of 32 Step 1: Tag Preference Inference animation robots pixar ? Infer a users interest in tags from: tags user applied tags user searched for users clicks on movie hyperlinks users movie ratings
15 of ,017 ratings by 995 users 118,017 ratings by 995 users
16 of 32 Preferences for Tags Searched / Applied
17 of 32 Movie-rating algorithm cars
18 of 32 Movie-Rating Algorithm cars 4 of 12 1 of 36 9 of
19 of 32 Bayes-Rating Algorithm Generative Model: Expressive probabilistic processes. Model movie ratings. Separate model for every user, tag.
20 of 32 Jills Ratings for animated Movies N(μ=3.8,σ=0.7) Bayes-Rating Algorithm
21 of 32 all possible normal dists for ratings for animated movies WALL-E not tt = animation p(t | WALL-E)1.0 - p(t | WALL-E) N(μ u,t,σ u,t ) N(μ u,σ u ) N(μ=2.0,σ=1.0)N(μ=4.0,σ=0.5) Bayes-Rating Algorithm
22 of 32 All movies m rated by Jill tagged with animation not tt = animation Toy Story WALL-E Shrek all possible normal dists for ratings for animated movies Bayes-Rating Algorithm
24 of 32 Outline Tag preference inference Item recommendation Auto-tagging and wrap-up
25 of 32 Tagommender Flow Chart WALL-E animationrobotspixar tag preference inference tag-based recommendation
26 of 32 Step #2: Tag-Based Recommendation Standard machine learning problem With / without ratings Six standard recommender baselines Evaluate predictive performance
28 of 32 Outline Tag preference inference Item recommendation Auto-tagging and wrap-up
29 of 32 Inferred pref for girlie movie: Rating for Runaway Bride Alice Bob Mike (other users)….… cosine similarity = 0.45 Using Tag Preferences for Tag Inference
30 of 32 Top 10 Inferred Tags Not Already Applied movietagcosine sim Pearl Harbor (2001)disaster0.47 Runaway Bride (1999)girlie movie0.45 Beauty and the Beast (1991)talking animals0.42 Armageddon (1998)will smith0.41 Cinderella (1950)cartoon0.40 Inconvenient Truth (2006)documentary0.40 The Little Mermaid (1989)musical0.40 Gone in 60 Seconds (2000)exciting0.39 My Best Friends Wedding (1997)chick flick0.39 Billy Madison (1995)very funny0.39
31 of 32 Summary of Tagommenders Tag preference inference: Systems can infer user preferences for tags. Item ratings help tag pref inference. Tag prefs can be used for auto-tagging. Tagommenders outperform traditional recommenders: Without ratings: moderate edge (10%). With ratings: slight edge (2%).
32 of 32 Future Work 1.Alternative modalities for tags. 2.Quality vs. preference. Thank You! 1.GroupLens. 2.MovieLens users. 3.NSF grants IS and IIS Macalester College.