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隐式反馈下的协同过滤

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隐式反馈下的协同过滤

2016-01-30 15:33:59   5  举报

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AI智能生成

论文笔记，Collaborative Filtering for Implicit Data

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作者其他创作

大纲/内容

previous work

neighborhood models

common approach

user-oriented

early

item-oriented

better

amenable

no distinction

preferences

confidence

latent factor models

SVD

adequate regularized model

superior to neighborhood model

modification

model formulation

optimization technique

our model

borrow from LFM

model formulation modification

optimization technique

confidence levels

no positive action

other reasons

not liking

positive action

other reasons

liking

notion

r

confidence

raw observations

p

binary

derived by r

user preference

p = x * y

c

confidence of p

alpha

x

user factors

y

item factors

m

number of users

n

number of items

matrix factorization

similar to explicit

distinctions

varying confidence levels

account for all

cost functions

huge numbers

optimization techniques

ALS

main properties

transfer r to p and c

efficient algorithm

explaining recommendations

reduce

linear model

weighted similarity

pastactions

break

significance to user

similarity to target

different similarity for user

experimental study

data description

television

300,000 users(set-top boxes)

17000 programs

32 million data(rui)

filter test data

large range

log scaling scheme

momentum effect

down-weight subsequent

evaluation methodology

no negative feedback

precision based matrix

not appropriate

recall-oriented measures

ours

percentile-ranking

evaluation results

baseline

popular item

neighborhood based

ours best

lowest percentile-ranking

better in top

better than middle modle

importance of confidence levels

analyze performance

better for popular shows

heavy watchers not better

recommendation explaination

just similar shows

intro

content based

need external info

CF

better

cold start

explicit feedback

vast literature

implicit feedback

characteristics

no negative

inherently noisy

numerical value

confidence

evaluation

preliminaries

zero is ok

discussion

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