topical media & game development
prediction techniques
social-based -- dependent on (group) rating of item(s)
information-based -- dependent on features of item(s)
hybrid methods -- combining predictors
definition(s)
rating
-- a value representing a user's interest
recommendation
-- item(s) that might be of interest to the user
regret
-- a function to measure the accuracy of recommendations
...
Fig. 1: Concept graph
...
Fig. 2: Content gadget
...
{\rm\normalsize\hspace{1.0cm} Fig. 3: Reconstruction of
Terra della Dea Madre
.}
...
Fig. 4:
Construction
tool
...
Fig. 5: (a) Light
(b)
Material
...
Fig 6. Overview
Interview
Agent
recommender model
U = user
I = item
B = behavior
R = recommendation
F = feature
observations --
U \* I \* B
recommendations --
U \* I
B
= [ time = 20sec, rating = r ]
F
= [ artist = rembrandt, topic = portrait ]
R
= [ artist(rembrandt) = r, topic(portrait) = r ]
A
= [
p_{1}, p_2
, ... ] where
p_{k}
= [
f_1 = v_1
,
f_2 = v_2
,
...
]
with as an example
A_{nightwatch}
= [ artist=rembrandt, topic=group ]
A_{guernica}
= [ artist=picasso, topic=group ]
...
Fig 7. Users, artworks and properties
distance metric
d(x,y) = d(y,x)
d(x,y) <= d(x,z) + d(z,y)
d(x,x) = 0
positive vs negative
individual vs community/collaborative
feature-based vs item-based
neutral interpretation -- use
d(s_{n}, a_{k}) < d(s_{n}, s_{n+1} )
positive interpretation -- increase
w(feature(a_{k}))
negative interpretation -- decrease
w(feature(s_{n+1}))
