1. Introduction
Algorithmic Components
Experiments
Summary
Improving Social Recommendations by
applying a Personalized Item Clustering policy
Georgios Alexandridis, Georgios Siolas
Andreas Stafylopatis
School of Electrical and Computer Engineering
National Technical University of Athens
15780 Zografou, Athens, Greece
The 5th ACM RecSys Workshop on Recommender
Systems & the Social Web (RSWeb 2013)
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

2. Introduction
Algorithmic Components
Experiments
Summary
Problem Statement
Intuition
Objectives & System Outline
Problem Statement
Human taste is influenced by many factors
People tend to consume items that are not alike
Pure CF or item-based approaches quite often miss those
peculiarities of human taste
Recommender Systems should be able to identify
connections between seemingly uncorrelated items
that are of interest, though, to a particular user
In this way, the overall user satisfaction is
expected to increase because
the recommended items would be novel
compared to what has been previously consumed
the list of recommended items would be more diverse
compared to the list of items returned by pure CF or
item-based techniques
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

3. Introduction
Algorithmic Components
Experiments
Summary
Problem Statement
Intuition
Objectives & System Outline
Intuition
Homophily: In social networks, people establish bonds
predominantly with people they share common interests
with
In social RS, people follow/befriend people with similar
taste
We share evaluations with our peers on common subsets
of items
Those items have some characteristics in common
Even if they’re considered to be uncorrelated by standard
similarity techniques
Intuition: locate common consumption patterns of items
in the subsets and of other items in the system
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

4. Introduction
Algorithmic Components
Experiments
Summary
Problem Statement
Intuition
Objectives & System Outline
Objectives & System Outline
Socially-aware personalized item clustering recommendation
system
Main Objectives: Make recommendations that are accurate,
novel and diverse
System Outline
1
Place the items that a specific user has evaluated into
clusters according to the rating behavior of the members of
his Personal Network
Peers in his/her social network
Similar peers
2
For each cluster
Construct the Item Consumption Network
Perform a Random Walk on the aforementioned network
and return the most visited nodes
3
Merge the returned nodes of each cluster and return N
recommendations
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

5. Introduction
Algorithmic Components
Experiments
Summary
The Personal Network
The Item-to-Item Adjacency Matrix
Personalized Clustering
Item Consumption Network
The Random Walk on the ICN
The Personal Network
The Personal Network of user u
Neighbors in the social network
that bear a similarity to u
Other similar users
Other users in the social network
(e.g “friends-of-friends”) that are
similar to u
Other users in the social network
Similarity is measured by
readily-applied indices in RS
literature
e.g. Pearson, Cosine, Manhattan
Alexandridis, Siolas, Stafylopatis
s7
u7
t3,7
u3
s3 , t3
u5
t2,1
t2
ut
t3,5
u2
t2,4
s1 , t1
t1,4
s6
u4
u1
u6
Social Recommendations via Personalized Item Clustering

6. Introduction
Algorithmic Components
Experiments
Summary
The Personal Network
The Item-to-Item Adjacency Matrix
Personalized Clustering
Item Consumption Network
The Random Walk on the ICN
The Item-to-Item Adjacency Matrix
i1
i1 0
items having been evaluated
i2 0
by u above a relevance

threshold
i3 0

A = i4 3
Elements:

i5 0
ai,j denotes the frequency

that items i and j have been
i6 4
evaluated together by u’s
i7 0
Rows and Columns

i2
0
0
0
0
0
0
1
i3
0
0
0
0
3
2
0
i4
3
0
0
0
0
8
0
i5
0
3
0
0
0
0
4
i6
4
0
2
8
0
0
0
i7

0
1

0

0

4

0
0
peers in his/her Personal
Network
By definition, matrix A is
square and symmetric
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

7. Introduction
Algorithmic Components
Experiments
Summary
The Personal Network
The Item-to-Item Adjacency Matrix
Personalized Clustering
Item Consumption Network
The Random Walk on the ICN
Personalized Clustering
4
Matrix A: adjacency matrix of an
undirected graph
i1
3
i4
i7
1
i2
2
8
Nodes: items consumed by u
Edges: frequency of items having
been accessed together by u’s
peers
Perform spectral clustering
on this graph to locate clusters of
items accessed together
i6
4
3
i5
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering
i3

8. Introduction
Algorithmic Components
Experiments
Summary
The Personal Network
The Item-to-Item Adjacency Matrix
Personalized Clustering
Item Consumption Network
The Random Walk on the ICN
Item Consumption Network
Nodes: items
Black: members of the cluster
Gray: other items, accessed by u’s
peers along with members of the
cluster
s1 (10)
number in parenthesis are total
evaluations by all users
Edges: frequency of common
access by u’s peers
The ICN graph
is connected and non-bipartite
assumes the properties of a
symmetric time-reversible finite
Markov chain
Alexandridis, Siolas, Stafylopatis
i2 (30)
2
5
i4 (80)
s2 (20)
4
i1 (50)
3
i3 (40)
3
s3 (15)
Social Recommendations via Personalized Item Clustering

9. Introduction
Algorithmic Components
Experiments
Summary
The Personal Network
The Item-to-Item Adjacency Matrix
Personalized Clustering
Item Consumption Network
The Random Walk on the ICN
The Random Walk on the ICN
Random walks on connected, non-bipartite graphs reach
their steady-state distribution regardless of the
staring node
Modified Random Walk on the ICN graph
Return the most visited non-seed nodes as
recommendations
Modified: next node is not sampled uniformly at random
but depends on the
edge weight
number of evaluations of both the current and the
following node
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

10. Introduction
Algorithmic Components
Experiments
Summary
Datasets
Evaluation Methodology
Reference Systems
Results
Datasets
Performance Evaluation on the Epinions dataset
Medium-sized dataset
50k users
140k items
664k ratings
487k trust statements
Very sparse dataset as measured by the
rating’s density and the clustering coefficient of the trust
network (power law distributions)
Ratings are skewed towards the upper scale (4-5) by a
ratio of 1 to 3
Behavioral phenomenon of users predominantly rating
items they’ve both consumed and liked
Any naive RS that would blindly recommend any item
with a high score would perform satisfactory!
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

11. Introduction
Algorithmic Components
Experiments
Summary
Datasets
Evaluation Methodology
Reference Systems
Results
Evaluation Methodology
Evaluation Objectives
Accuracy of predictions
Coverage of predictions
Qualitative criteria for the list of recommended items
How novel they are (in terms of what has already been
consumed)
How diverse they are from one another
Evaluation Metrics
1
2
3
4
Root Mean Square Error (RMSE)
Ratings’ Coverage
Distance-based Item Novelty
Intra-list Diversity
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

12. Introduction
Algorithmic Components
Experiments
Summary
Datasets
Evaluation Methodology
Reference Systems
Results
Reference Systems
Baseline Systems
UserMean
ItemMean
Traditional Recommender Systems
Collaborative Filtering
Item-based Recommendation
Trust Aggregation RS
MoleTrust (with propagation horizon up to 3)
TidalTrust
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering

13. Introduction
Algorithmic Components
Experiments
Summary
Datasets
Evaluation Methodology
Reference Systems
Results
Results
Results on the Epinions Dataset (for a list of 5 recommended items)
Performance Metrics
RMSE
Coverage
Novelty
A. Baseline
A.1 ItemMean
A.2 UserMean
B. Collaborative Filtering
B.1 Manhattan Similarity (All Neighbors)
C. Item-Based Recommendation
C.1 Manhattan Similarity (All Similar Items)
D. Trust-based Approaches
D.1 MoleTrust-1
D.2 MoleTrust-2
D.3 MoleTrust-3
D.4 TidalTrust
E. Our Recommender
E.1 Personalized Item Clustering
Alexandridis, Siolas, Stafylopatis
Diversity
1.09
1.20
86.43%
98.58%
11.89%
9.70%
24.23%
19.42%
1.07
79.57%
20.11%
56.23%
1.20
39.29%
16.86%
45.26%
1.23
1.16
1.12
1.08
25.58%
56.52%
70.89%
74.67%
29.16%
32.31%
42.13%
45.38%
43.62%
54.02%
56.65%
59.17%
1.05
58.17%
53.11%
63.04%
Social Recommendations via Personalized Item Clustering

14. Introduction
Algorithmic Components
Experiments
Summary
Summary
We proposed a novel social RS based on personalized
item clustering
Our approach yields satisfactory results on most of our
evaluation objectives (accuracy, novelty, diversity)
It could be further improved in the personal network
formation phase and the clustering algorithm
Alexandridis, Siolas, Stafylopatis
Social Recommendations via Personalized Item Clustering