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In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Tra- ditional tensor-based models in context-aware recommendation scenario only consider user-item-context interactions. In this paper, we argue that rating can’t be totally explained by the interactions and the rat- ing also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based on this hypothesis, we propose a novel context-aware recommendation model named
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Knowledge-Based Systems 128 (2017) 71–77
Contents lists available at ScienceDirect
Knowle dge-Base d Systems
journal homepage: www.elsevier.com/locate/knosys
Improving performance of tensor-based context-aware recommenders
using Bias Tensor Factorization with context feature auto-encoding
Wenmin Wu
a
, Jianli Zhao
a , ∗
, Chunsheng Zhang
a
, Fang Meng
a
, Zeli Zhang
a
, Yang Zhang
a
,
Qiuxia Sun
b
a
School of Information Science & Engineering, Shandong University of Science and Technology, Qingdao, China
b
College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, China
a r t i c l e i n f o
Article history:
Received 16 November 2016
Revised 15 April 2017
Accepted 24 April 2017
Available online 26 April 2017
Keywords:
Context-aware recommendation
Tensor factorization
Regression tree
Context features selection
a b s t r a c t
In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Tra-
ditional tensor-based models in context-aware recommendation scenario only consider user-item-context
interactions. In this paper, we argue that rating can’t be totally explained by the interactions and the rat-
ing also influenced by the combined impact of overall mean, user bias, item bias and context bias. Based
on this hypothesis, we propose a novel context-aware recommendation model named Bias Tensor Fac-
torization, which take all this factors into account. Additionally, traditional context-aware recommenders
with tensor factorization still have three main drawbacks: (1) the model complexity of those models
increase exponentially with the number of context features, (2) those models can only handle context
features with categorical values and (3) the models fail to select effective features from available con-
text features. To address those problems, we propose a context features auto-encoding algorithm based
on regression tree which can both handle numerical features and select effective features. Then we inte-
grate this algorithm with Bias Tensor Factorization. Experiments on a real world contextual dataset and
Movielens show that our proposed algorithms outperform the state-of-art context-aware recommenda-
tion algorithms, namely tensor factorization and factorization machine.
©2017 The Author(s). Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license.
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
1. Introduction
Recommender Systems (RSs) [1,2] are information filtering tools
to help users overcome the information overload problem and
are more and more popular in different application areas [28,29] .
The core of RSs is recommendation algorithm. Traditional rec-
ommendation algorithms like user-based collaborative filtering
[3,4,5] , item-based collaborative filtering [6] and matrix factoriza-
tion [7] mostly rely on the information of user, item and the in-
teractions (e.g. rate, purchase, etc.) between them. In the real rec-
ommender system, there is plenty of information about the situa-
tion of the interaction happens, e.g. the time, the location, or the
mood of the user. Such additional information are called context
and have been proven to be important to inferring the users’ pref-
erences. Recommenders which take this information into account
are called context-aware recommendation algorithms.
∗
Corresponding author:.
E-mail addresses: wuwenmin1991@gmail.com (W. Wu), jlzhao@sdust.edu.cn (J.
Zhao).
Tensor factorizations [8,9,10,11,23] are one of the most success-
ful approaches to context-aware recommendations. But, traditional
context-aware recommenders based on tensor factorization have
three main drawbacks: (1) these models use user-item-context in-
teractions to explain the whole rating. We argue that besides the
interactions the rating can be affected by some biases, e.g. some
users in general give higher rating to movies, for instance, they are
optimistic people. (2) These models fail to select effective features
from available context features which will make them not applica-
ble to real recommender systems since there are tens of contextual
features in real data. (3) These models only work for context fea-
tures with categorical features.
In this work, the main objective is to make tensor-based rec-
ommenders applicable to real world recommender systems as well
as improve their performance. To do this, we propose a Bias Ten-
sor Factorization model and a Context Features Auto-encoding Al-
gorithm. The contribution of our paper is summarized as follows:
• We propose a Bias Tensor Factorization model which takes the
influences of user bias, item bias, context bias and the overall
mean into account.
http://dx.doi.org/10.1016/j.knosys.2017.04.011
0950-7051/© 2017 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
72 W. Wu et al. / Knowledge-Based Systems 128 (2017) 71–77
• We propose a context feature auto-encoding algorithm based
on regression tree, which can both handle the continuous con-
text features and control the dimension of encoded context fea-
tures by only selecting the useful context features.
• We integrate context feature auto-encoding algorithm with Bias
Tensor Factorization and conduct several experiments to show
that our proposed models outperform the state-of-art context
modeling algorithms namely classical tensor factorization and
factorization machine. To the best of our knowledge, this paper
is the first to integrate tree-based feature selection algorithm
with tensor-based recommenders.
2. Related work
Context-aware Recommendation has drawn much research at-
tentions in recent years. This is probably because the context in-
formation is much easier to get today and the success stories of
some contextual applications (e.g. Frappe
1
)
Adomavicius and Tuzhilin give a thorough review of approaches
to contextual recommendations and categorize context-aware rec-
ommendation methods into three types [16] : contextual pre-
filtering, contextual post-filtering and contextual modeling . In contex-
tual pre-filtering and post-filtering, a context-unaware recommen-
dation algorithm is applied and the data is either preprocessed
based on the given context or the results are post-processed ac-
cording to the context [17] . Examples for pre-processing are di-
mension reduction [18] and item splitting [19] . The main advan-
tage of pre-processing and post-processing is we can use tra-
ditional context-unaware recommendation algorithms. However,
both of them suffer from the lack of effective context generalized
methods.
The models proposed in this paper belong to the contextual
modeling . Several contextual modeling methods have been pro-
posed so far. Oku introduced a Context-aware Support Vector
Machine which considers support vectors in a multidimensional
space and find the separating hyperplane [20] . Koren proposed a
temporal matrix factorization model called timeSVD ++ and show
its effectiveness in the Netflix Prize. Alexandros proposed a ten-
sor factorization model for Context-aware Collaborative Filtering
[21] which is a basis of this paper. Rendle introduced a factoriza-
tion model namely factorization machine which is a panacea of
regression problem. Barmpoutis proposed a new tensor structure
that grasps both user and image relations for image tag recom-
mendation problem [24] . Shan proposed a fully coupled interac-
tions tensor factorization model based on Tucker decomposition to
model three pairwise interactions between the user, publisher, and
advertiser for CTR estimation problem in Real Time Bidding (RTB)
[25] . In [26] , Panagiotis combined the clustering of tags with the
application of High Order Singular Value Decomposition on ten-
sors which pre-grouping neighboring tags and produces represen-
tative tag centroid which are inserted into the tensor to alleviate
the data sparsity problem in Social Tagging System. Kim proposed
the 4-way Tensor, a parallel tensor factorization algorithm, to ac-
celerate the tensor factorization of large datasets to support effi-
cient context-aware recommendations [27] .
However, none of those works address the context features se-
lection problem. To address this problem, we utilize regression tree
for context features selecting after got inspiration from the work of
Facebook [22] .
3. Tensor-based recommenders
In this section, we will firstly introduce classical tensor factor-
ization with CP model [11] which is a basis of our contributions
1
http://baltrunas.info/research-menu/frappe .
Fig. 1. Illustration of tensor factorization with CP model.
followed by the main contributions of this paper: (1) our pro-
posed Bias Tensor Factorization model ; (2) a novel context features
auto-encoding algorithm based on regression tree; and (3) an inte-
grated model of Bias Tensor Factorization and context features auto-
encoding.
3.1. Classical tensor factorization
The classical tensor factorization assumes that the rating ten-
sor R ∈ R
M×N×K
from M users to N items under K types of context
can be factorized into a product of three low rank matrices U
M × D
,
V
N × D
and C
K × D
as shown in Fig. 1 . Then user m ’s rating to item
i under context k can be predicted as following:
ˆ
r
mik
=
D
d=1
U
md
V
id
C
kd
(1)
3.2. Bias Tensor Factorization
3.2.1. Model
Bias Tensor Factorization which takes the overall mean, user
bias, item bias, context bias into account which can be represented
by the following equation.
ˆ
r
mik
= μ + b
m
+ b
i
+ b
k
+
D
d=1
U
md
V
id
C
kd
(2)
In this model, we explain the rating with 5 components: (1) the
overall mean denoted by μ; (2) the rating bias of user m denoted
by b
m
; (3) the score bias when users rating item i denoted by b
i
;
(4) the score bias when users rating under context k denoted by
b
k
; and (5) the interaction of user, item and context which is the
whole component in classical tensor factorization model.
3.2.2. Training
The parameters in Bias Tensor Factorization can be learned by
minimizing the following equation:
min
b∗,U∗,V ∗,C∗
1
2
(
u,i,k
)
∈ Y
y
mik
−
ˆ
r
mik
2
+
λ
2
U
m
||
2
+ V
i
||
2
+ C
k
||
2
+ b
2
m
+ b
2
i
+ b
2
k
(3)
Note that, we add the Frobenius norms of the model param-
eters to avoid overfitting and the model complexity can be con-
trolled by λ.
We use stochastic gradient descent [12] to minimize the objec-
tive function Eq. (3) , the loss function for each observed rating r
mik
in R can be defined as below:
L =
1
2
r
mik
−
ˆ
r
mik
2
+
λ
2
(
U
m
||
2
+ V
i
||
2
+ C
k
|
2
+ b
2
m
+ b
2
i
+ b
2
k
(4)
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