Transcript Challenge the long tail recommendation

Challenge the long tail
recommendation
Hongzhi Yin, Bin Cui, Jing Li,
Junjie Yao, Chen Chen
Peking University
Outline
• The Long Tail Market
• The Long Tail Recommendation
– Hitting Time Model
– Absorbing Time Model
– Absorbing Cost Model
• Experimental Evaluation
• Conclusion
The Long Tail Market
What is Long Tail?
• Chris Anderson, Wired
Editor, October 2004
• The Long Tail: Why the
future of business is
selling less of more,
June 2006
• Niches – unpopular
items, tail market
• Hits – popular items,
mass market
4 / 35
What is Long Tail?
• The Long Tail concept was introduced by Chris
Anderson in his famous book ‘The Long Tail’
• It is a business strategy that enables them to
reach significant profits by selling smaller
quantities of hard to find items.
• This idea enables the business to target niche
audiences around the world which would not
be possible in local outlets.
Long Tail Market
•Borderless market
Rhapsody Music Downloads
Downloads
180,000
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
0
In Wal-Mart, only the first 4500
albums are on shelf, and the first 200
count for 90% revenue.
The curve does not reach 0.”
0
5,000
10,000
15,000
20,000
Ranking of songs
source : The long Tail Book
25,000
Long Tail Market(cont.)
But, if we continue
downloads
800
700
600
500
400
300
200
100
0
The area under the curve is the
revenue, counting for 1/4 of
Rhapsody's total revenue.
25
35
45
55
65
Ranking (in thousands)
source : The long Tail Book
75
85
95
The Market Without End
……continue
downloads
140
120
100
80
60
40
20
0
100
The demand of niches never
fell to zero .
200
300
400
500
Ranking (in thousands)
source : The long Tail Book
600
700
800
New Type of Market
Netflix
Total Inventory：
55K DVD
Rhapsody
Total Inventory：
1.5M songs
1,500,000
60,000
1,250,000
50,000
500,000
Wal-Mart
40,000
Regular Inventory：
30,000
50,000
20,000
250,000
10,000
1,000,000
750,000
Amazon
Total Inventory：
3.7M books
4,000,000
Blockbuster
Regular
Inventory：
3,000 DVD
2,000,000
1,000,000
0
0
0
40
%
Total Sales
Border:
Regular Inventory：
100K books
3,000,000
21
%
Total Sales
Products not sold at Brick-and-Mortars
source : The long Tail Book
25
%
Total Sales
Benefits of Long Tail
Recommendation
• All those long tail items, when aggregated, can make
up a significant market.
• Long tail items embrace relatively large marginal
profit, which means endeavor to expand the tail
market can bring much more profit besides revenue.
• Popular items are so obvious that it is not necessary
for personalized recommenders to recommend them
to users.
• Availabilities to tail items can also boost the sales of
the head due to the so called “one-stop shopping
convenience” effect.
Rules of Long Tail
• Two imperatives to create a thriving long tail
business:
– Make everything available.
• The explosion of electronic commerce , such as Amazon,
Netflix, and iTune Music Store, has opened the door to
the so-called infinite-inventory retailers, which makes
the first condition fulfilled .
– Help me find it.
• There is still a long way to go for the second imperative
since most of the existing recommendation algorithms
can only provide popular recommendations.
Help! I’m stuck in the head
72%
Source: Last.fm Dataset
Limitations of existing works
• Most of existing recommender systems cannot
help users find their interested long tail items.
• Although most of them can push each person
to new products, they often push different
users toward the same products.
• They may create a rich-get-richer effect and
vice-versa for unpopular items.
Item-Based Collaborative Filtering
The item-based CF method has been deployed at Amazon since 2001,
and Amazon is well known for it fun feature
“Customers
who
boughtisthis
also bought “
But item
based CF
method
popularity-biased.
The Harry Potter Problem :Harry Potter is a runaway bestseller.
So, take a book, any book. If you look at all the customers who
bought that book, then look at what other books they bought,
most of them have bought Harry Potter. So no matter what books
you bought, the Harry Potter would be recommended to you.
User-Based Collaborative Filtering
• The basic idea of user-based CF is as follows: for a
given user, it first finds k most similar users by
using Pearson correlation or Cosine similarity ,
and then recommend the most popular items
among these k users.
• So, the User-Based CF cannot help users find long
tail items, which was first analyzed and proved by
D.M.Fleder .
• “Recommender systems and their impact on
sales diversity(EC 07)”
Latent Factor Model
rˆui   puk qik
k
Science Fiction
0.5
Science Fiction
0.9
Universe
0.9
Universe
0.9
Physical
0.8
Physical
0.5
Space Travel
0.8
Space Travel
0.7
Animation
0.3
Animation
0.1
Romance
0.0
Romance
0.0
Latent Factor Model
• Although various latent factor models are
recently proposed and perform well in
recommending popular items, they cannot
address the problem of long tail recommendation.
• Because latent factor models can only preserve
principal factors
• These principal factors can only capture
information of popular items while ignoring those
niche items.
The Long Tail Recommendation
Graph Modeling of User-item
Interaction Information
Example of a user-item bipartite graph
Our Goal
PROBLEM DEFINATION:
Given : a user-item graph G(V, E) with adjacency
matrix A, and a query user node q. Find: top-k item
nodes that
(1) are close enough to q, and
(2) lie in the long tail.
Long Tail Recommendation
Methods
• A basic solution- Hitting Time
• Absorbing Time
• Absorbing Cost
Hitting Time
H(q|i) = 0.7 H(q|j) + 0.3 H(q|k) + 1
h=0
0.7
k
q
i
0.3
H (q | i) 
j
 p(i  j)H (q | j)
• Hitting Time
– The hitting time from i to q, denoted as
H(q|i) , is the expected number of steps
that a random walker starting from i will
take to reach q for the first time.
– We compute hitting time by iterating
over the dynamic programming step for
a fixed number of times.
 1, for i  q
jV
0, for i  q
In order to compute the hitting time H(q|i), we can first compute the hitting times of
his adjacent nodes, H(q|j) and H(q|k), and then proceed from there.
Nice Properties of Hitting Time
• Hitting time has two nice properties:
– It can capture the graph structure, e.g., node j and q
have many common neighbors or many short paths
between them
– the stationary probability of the starting node
• A small hitting time from an item node j to the
query node q means:
– There are many short paths from j to q
– The item node j with low stationary probability,
which implies the item j is hard to find.
A running example
CF
Given query user U5
Given the query
user U5
Our solution
h(U5,M4)
h(U5,M1)
h(U5,M5)
h(U5,M6)
17.7
19.6
20.2
20.3
Absorbing Time
• Based on hitting time, we propose absorbing
time
– We extend the single query node q to a set of
query nodes S;
– The Absorbing Time AT(S|i) is defined as the
average number of steps that a random walker,
starting from i, will take to reach any node in S.
– The recurrence relations for computing Absorbing
Time can be easily defined as follows:
Recommendation based on Absorbing Time
User
Item
0.56
5
j
4
0.44
S
i
k
x
b
q
c
d
e
• For a query user q, find his
historical preferred item set S;
• Construct a subgraph from the
user action log data centering on q;
• Compute the edge probabilities
based on the rating scores
• Compute absorbing time AT(S|i)
for each item node;
• Rank candidate items according to
AT(S|i)
The time complexity is O(t*m), where t is the number of iterations and
m is the number of edge of subgraph.
The challenge of long tail
recommendation
• The rating data matrix of long tail items is more
sparse.
• The long tail item-item similarity based on rating
data is not reliable.
• So more valuable prior information should be
deeply mined to improve the recommendation
accuracy.
Intuition from Information Theory
• Suppose there is a user who has a wide range
of interests and tastes, which tends to
increase the ambiguity of the user.
• On the other hand, if the user centers on few
specific interests, this tends to increase the
specificity of the user.
• For example, if two different items are
connected by a specific user, then they are
more likely to be similar in topics and tastes.
If two different items are connected by a specific user,
then they are more likely to be similar in topics and tastes
Both U2 and U4 provide
the same rating scores
for the movie M3, but U4
is more interest-specific
than U2, so M3 is more
Similar with M4 than M5.
In other words, the ratings provided by U4 are more valuable.
Absorbing Cost
Distinguish the variation on different user-item
rating pairs besides the rating scores.
Where c(j|i) denotes the cost of jumping from i to j.
If c(j|i)=1, the absorbing cost model is degraded into:
User Entropy
• We propose the concept of User Entropy to
weight the cost of jumping from one item
node to different user nodes.
• Two heuristic methods are proposed to
compute User Entropy
– Item based User Entropy
– Topic based User Entropy
Item based Entropy
• Assumption: if a user rates large number of items,
especially with equal probability, the ambiguity
(uncertainty) of the user tends to increase, otherwise if
a user rated only a few items, the specificity of the user
tends to increase.
• Using information theory, the item based user entropy
is defined as follows:
Topic based Entropy
• The assumption made above is not always
true since an interest-specific user can also
rate large number of items belonging to the
same class.
• We propose a LDA-based topic model to
compute User Entropy:
Experiments
Dataset1
– The MovieLens (http://www.grouplens.org/data)
• A web-based movies recommender system;
• Contains multi-valued ratings that indicate how much
each user liked a particular movie or not;
• Each user has rated at least 20 movies.
Table 1: The characteristics of the MovieLens dataset
# of Users # of Items Density1
6040
1Density:
3883
4.26%
#Ratings
1,000,209
the percentage of nonzero entries in the user-item matrix.
Dataset2
– The Douban book dataset (http://book.douban.com/)
• A Chinese Web 2.0 website, the largest book reviews
website in China
• Users can assign 5-scale integer ratings to books
Table 1: The characteristics of the Douban book dataset
# of Users # of Items Density1
383,033
89,908
0.039%
#Ratings
13,506,215
Our methods
• Hitting Time (HT)
• Absorbing Time (AT)
• Absorbing Cost (AC)
– Item based Absorbing Cost (AC1)
– Topic based Absorbing Cost (AC2)
State-of-the-art recommendation
techniques
• Latent Factor Model
– Pure SVD
• LDA-Based Model
• Discounted Personalized PageRank(DPPR)
What makes a good recommendation?
•
•
•
•
•
•
Accuracy
Coverage
Long Tail
Serendipity
Novelty
…
Accuracy-Recall@N
• Experiment Design
– Split the dataset into training and test sets by
• Randomly select one rated long tail item of each user
to form the test set
• Use the remaining ratings for training
– Evaluation Metric
Recall@N = #Hits/n
Where #Hits denotes the number of items in the test set
that were also in the top-N lists, n is the number of items
in the test set
Experimental result
Movielens
Douban
Coverage
• Metric
Coverage
Where U is the set of testing users and I is the set of items. Ru
denotes the set of recommended items for u. In this experiment,
|U|=2000, |Ru|=10, and |I|=20000
AC2
Douban 0.58
Movielens 0.42
AC1
0.625
0.425
AT
0.58
0.42
HT
0.55
0.41
DPPR
0.45
0.35
PSVD
0.325
0.245
LDA
0.035
0.025
Long Tail – Popularity@N
Serendipity and Novelty
• We employ a user survey on Movielens dataset by
hiring 50 movie-funs as evaluators to answer the
following questions.
AC2
DPPR
PureSVD
LDA
Preference
Novelty
Serendipity
Score
4.32
3.12
4.34
4.12
0.98
0.89
0.64
0.66
4.78
3.95
2.12
2.15
4.41
3.65
4.25
4.22
Conclusion
• We first analyzed the long tail phenomenon and
proposed long tail recommendation problem.
• We developed three long tail recommendation
algorithms: Hitting Time, Absorbing Time and
Absorbing Cost.
• We conducted extensive experiments on two
real datasets and the experimental results show
the superiority of our proposed algorithms on
recommending long tail items.