sample to write research report

1. Factorization Methods in Context-Aware Recommendation
System
Shaina Raza
DepartmenthofhComputerhScience
Ryerson University
Toronto, ON, CA
shaina.raza@ryerson.ca,
ABSTRACT.
Being context-aware improves the way the recommendation
algorithms adapts to dynamic users behaviour and systems
operations. Context-aware recommendation algorithms are widely
applied in various applications but are faced by challenges such as
scalability, sparsity and curse of dimensionality issues. We can
deal with these challenges by applying factorization methods such
as matrix and tensor decomposition on recommendation
algorithms. We aim to cover some of the potential state-of-the-art
context-aware recommendation algorithms that are based on
factorization methods in this survey. Each of these algorithms is
briefly summarized, discussed and compared with each.
Moreover, the ability of each approach to handle these
aforementioned challenges is also highlighted. Finally, based on
our report, we can say that further research is needed on this topic
and deployment of these methods will continue to improve the
recommendations in terms of quality.
Keywords
Context; Factorization Method; Latent features; Dimensionality
reduction; Scalability; Cold-start problem.
1. INTRODUCTIONε
The information overload on the internet has created trouble
for its stake-holders to get the right information at the right time
as per their interests and needs. A Recommender System (RS)
have become an important software application to provide specific
information to the users and help them narrow down countless
choices of items in web applications [1]. Modern RS are
characterized by additional ability to collect users’ preferences by
switching contexts such as mood, time, locality, demographics,
emotions or similar information. The term context has been used
widely in cognitive sciences, computer technology and related
disciplines. A detailed analysis of context is given by Baziri and
Brezillion [2] comprises of about hundred and fifty definitions
from different fields. However, in its general terms, context is
defined as “ any piece of information that characterizes the
situation of an entity ” [3]. Users’ behavior is highly inspired by
these contexts and that should be reflected in the recommender
systems. This has given birth to a new type of RS that is Context-
Aware Recommender System (CARS).
Adomavicius and Tuzhilin are among the pioneers to work on
CARS [4]. They researched approach to extend typical rating
function (R: User × Item → Rating) to support additional
contextual dimensions in the form (R: User × Item × Context →
Rating). They also explained its applications in different fields
such as e-commerce, data mining, databases, mobile and
ubiquitous, personalization, marketing and management. Verbert
et al. [5] also investigated different definitions of context and
summed up with its implication in academic domain. While the
ongoing research on CARS is still to continue, many survey
papers have been published that summarizes the most important
aspects in CARS. Some significant surveys are about: taxonomy,
applications and challenges of different Computational
Intelligence (CI) techniques [6], framework to identify contexts in
e-learning [4], analysis of users’ behavior and its social influence
in social–TV network [7], techniques and methods of web-
services [8].
The main motivation to introduce CARS algorithms is to generate
ratings from the target contextual information so as to give more
relative predictions in a specific situation. But these techniques
introduce new problems and issues; some of which are already
faced by the conventional RS like cold-start, privacy, sparsity,
scalability, synonymy [9] but some of them are totally new like
explosion of the dimensional space, learning rate and so on [10].
A comprehensive study of these papers reveals to us that there are
certain challenges that can particularly affect the quality of
recommendation in CARS [10]. Besides many existing
challenges, some of the pressing issues that are explored as: data
sparsity, cold-start and high dimensionality.
o Data sparsity issue in typical RS arises when the user-item
rating matrix is growing and the given ratings are very less [11].
The sparsity problem even worsens in the case of CARS,
because they mostly use multidimensional rating function to
compute user preferences; and many dimensions have scarce
data points to describe each context dimension. Therefore, it is a
big challenge for CARS to have predictive accuracy with few
available non-zero ratings.
o Cold-start problem arises when we have insufficient records for
the newly entered users or products in the system. In the case of
CARS, it gets worse when we have new contextual information
and there is no sufficient rating in the system [6].
o Compared to conventional recommendation algorithms, CARS
techniques are faced severely with the explosion of contextual
dimensions also referred to as high dimensionality. The idea is
to incorporate most appropriate and least number of dimensions

2. in the rating function, and to discover latent features from the
unexplored dimensional space [10]. By dimensionality
reduction techniques, we can find more ratings and increase the
density of the rating function.
Among the various state-of-the-art proposals, one way to deal
with these aforementioned challenges is to apply matrix and
tensor decomposition methods (also known as factorization
methods) to the context-aware recommendation algorithms. These
techniques are quite appealing to the researchers and application
designers in this field to exploit and work on.
Matrix Factorization (MF), as its name suggests, is the way of
decomposing a matrix into product of two or more matrices, such
that original matrix is retained when multiplied. In recommender
systems, MF can be used to discover hidden patterns that exhibit
the interaction between two entities of different kind [12]. Matrix
Factorization (MF) methods gained first recognition and exposure
in RS with 2009 Netflix Prize for movie recommendation [12].
Kolda & Bader [13] are the pioneers to present key concepts and
ideas behind tensor factorization (TF). The basic idea is to extend
MF methods to multiple modalities in order to have more
meaningful summarization of latent features [13].
This.survey.report is focused on factorization methods used in
CARS that try to meet the above mentioned challenges. The
papers for this survey are selected from IEEE Transactions on
Knowledge and Data Engineering1
(TKDE), Elsevier's
Knowledge-Based Systems2
; and from the proceedings of ACM
International Conference on Conference on Information and
Knowledge Management3
(CIKM) and RecSys4
. We have
finalized five publications out of potential candidate papers. These
papers are from the authors: Wuᴏet al.[14], Ungerᴏet al.[15],
Zhengᴏet al.[16], HᴏGe et al. [17] and Balázs & Tikk [18].
The paper isεstructured in.the following manner: Section.II
presents the review of selected papers. Section III presents the
critique to individual papers. Section IV summarizes the survey
and the last section V is about references.
2. SURVEYε
Thisεsection presents a briefεsummary of each of the selected
paper. Weεpresentedεa.reviewεof each paper; the algorithm used
and compared them against the aforementioned design challenges.
1
https://www.computer.org/web/tkde
2
https://www.journals.elsevier.com/knowledge-based-systems/
3
http://www.cikmconference.org/
4
https://recsys.acm.org/
2.1 COT - Contextual£Operation for
Recommender Systems
The authors in this paper [14] addressed the problem of
limited context coverage faced by the existing CARS methods.
They proposed a novel context modeling method: Contextual
Operating Tensor model (COT), where each context variable is
represented with a latent vector. Taking idea from natural
language processing, each context is modeled as a semantic
operation on user item pair and the contextual effects are captured
using operating tensors. They tried to mitigate the dimensionality
reduction problem by using Principal Component Analysis (PCA)
that projects context dimensions into two vectors.
In the experimentation, they have used three datasets Foods
[19], MovieLens-1M5
and Adom[20] to evaluate the performance
of COT for convergence, scalability and cold start users. The
results show that COT has a RMSE improvement of about 10% in
average for cold start users; it also achieves convergence with
increasing matrix diversity and scale up linearly with increasing
data size. The approach generates recommendations well from
the latent context but could be further saved from the outburst of
dimensions by producing specific contexts only; and by using top-
N recommendation algorithms properly.
2.2 Towards latent context-aware
recommendation systems
Unger et al.[15] proposed a technique to extract
environmental features in the form of low featured and
unsupervised latent context variables. They applied two different
algorithms i.e. (i) deep learning using auto-encoder algorithm and
(ii) PCA to extract latent context in low dimensional space. The
recommendation model is trained by the MF technique where
stochastic gradient descent (SGD) is used as the learning
algorithm to provideεsolutionεto the optimization problem. The
prediction accuracy of the system is evaluated by conducting a
series of offline simulations with the Point of Interest (POIs) data
collected from Foursquare6
API and comparing it with related
methods.
To overcome the high dimensionality problem, the authors
have applied random and time-based splits on users' preferences
by using ranking metric Hit@K. The results show that Hit@K
gives better results for time-based splits where the value of K is
smaller. The results show that proposed model works better when
most users' preferences are known. Although the approach extract
latent implicit features well in reduced dimensions, but this
method seem to be more appropriate for extracting explicit
features.
5
https://grouplens.org/datasets/
6
https://developer.foursquare.com/

3. 2.3 CSLIM - Deviation-Based£Contextual
SLIM Recommenders
Zheng et al. [16] proposed a MF approach for contextual
recommendations by extending the sparse linear method (SLIM)
[21]. SLIM is basically a MF approach for top-N
recommendations in a typical RS that aims to deal with sparsity
problem and reduce model learning time through feature selection
[21]. The authors in [16] presented three variants of CSLIM
(contextual SLIM) model where they use the intuition behind
itemKNN (Item-based k-nearest-neighbour collaborative filtering)
and UserKNN (user-based k-nearest-neighbour) algorithms by
taking aggregated users’ contextual ratings on items.
The experimental evaluation on Food[19], restaurant [22] and
music[23] datasets using metrics: (i) precision, (ii) recall and (iii)
mean average precision (MAP) shows that the proposed
algorithms has somewhat better performance compared to the
baselines. The proposed method works well when the matrix is
dense, but is limited to perform well on narrow matrices when the
users don’t give same rating consistently within the same context
on multiple items.
2.4 TAPER- AᴏContextualᴏTensor-Based
Approach£for£PersonalizedᴏExpertᴏ
Recommendation
Ge et al.[17] addressed the challenges of typical personalized
expert recommendation models which are: (i) lack of personalized
recommendations (ii) sparsity and (iii) complexity of underlying
relationships. They proposed a framework: Tensor-based
Approach for Personalized Expert Recommendation (TAPER)
that can assign specific personalized experts to the users basedᴏon
the past data of the like-mindedᴏusers.
Inᴏtheᴏmethodology, they have used tensor based approach
to discover latent matrices for users, experts and topics. They
integrated geo-spatial, topical and social context between
homogenous and heterogeneous entities and try to regulate them
using optimization function; in order to make them as close as
possible into the tensor-factorization framework. Experimental
evaluation on twitter dataset using precision and recall
performance measures shows an improvement of approximate
30% over the state-of-the-art baselines. It can be seen that most of
the experimentation is conducted on training data, so a general
question that arises is whether the results be same or better if these
methods are applied on increased number of testing samples.
2.5 GFF - Generalbfactorizationbframework
for contextbaware recommendations
Hidasi is one of the researchers who contributed well in the
area of CARS. He proposed a couple of implicit based
factorization recommendation algorithms into various forms; and
then later generalized them into one by the name General
Factorization Framework (GFF) [18]. GFF, as compared to its
predecessor algorithms, is a sole generalized algorithm which
input any preference model and project entities into low
dimensional latent feature space.
The goal is to develop an algorithm that can work with any
linear context-aware recommendationεmodel, either it be implicit
or explicit and make it usable to real-life projects by its scaling
properties. The performance evaluation on 5 datasets: LastFM
1K7
, Grocery8
, VoD9
, TV1 [24], TV2[24] shows GFF
improvement over the traditional models by a range of 12~30%
for different datasets. Preference modeling using GFF achieved
success over the baselines in somewhat similar scenarios. But in
real life setting, context entities have multiple set of features
which is a challenge for GFF.
In the table 1, we have pinpointed the key feature(s) and types
of challenges dealt by each publication. The symbol shows if
a paper addresses a particular challenge,  if it is not being met
and letter P for partial fulfillment.
Table 1. CARS based on factorization methods
Algorithm Key features Design Challenges Met
High
dimens
ionality
Data
sparsity
Cold
-start
COT[14] TF  P 
Latent
context
matrix
factorization
[15]
MF with SGD;
Deep learning
with auto-
encoder;
PCA   
CSLIM[16] MF   P
TAPER[17] TF   
GFF [18] TF with
coordinate
descent (CD) and
conjugate
gradient (CG) for
feature selection,
Cholesky
decomposition
for features
compression;
Optimization via
ALS
  
3. DISCUSSION
The five papers reviewed in section II present a focused
overview on how to find latent contextual patterns from
underlying entities using factorization methods. The rating
function grows with increased number of dimensions due to the
addition of various contextual elements in context-aware
recommendation algorithms. The factorization methods (MF and
TF) as discussed in these papers show us how to compress the
latent representations. In this section, we present critique to these
7
https://labrosa.ee.columbia.edu/millionsong/lastfm
8
http://recsyswiki.com/wiki/Grocery_shopping_datasets
9
http://www.comp.lancs.ac.uk/~elkhatib/p2p14/

4. papers; how well the authors are able to achieve the purpose and
meet the addressed challenges.
We begin with the issue of contextual information that is
overloaded in CARS. First, it is important to capture contexts
related to items, users and user-item interactions; and store them
as continuous vectors so as to accommodate multiple contexts. A
similar methodology can be seen in COT [14] where each context
is modeled as a semantic operation on users and items and
represented with a latent vector. We are also faced with the
challenge of dimensionality explosion when the accompanying
method has such large collection of contexts. COT[14], by its
tensor modeling is able to model contexts in lower dimensional
space. The authors in [14] used large scale benchmark datasets to
support the claim that COT can accommodate huge number of
latent context values; and used a weighing factor to control the
relative importance of changing contexts. Experimental evaluation
shows that COT has the ability to meet the performance criteria
over baselines; but ranking performance of the framework that is
typical of a recommendation method is not being measured here.
Now that we know a way of extracting and compressing large
contextual information from explicit feedback data to model user
behavior, we need to discover more meaningful and hidden
unsupervised contexts. The method proposed in [15] extract large
contextual information from mobile sensors and used deep
learning techniques to infer concealed user contexts in a non-
supervised manner. Unlike COT [14], here a basic factorization
method MF is used to reduce high dimensional vector space. The
authors [15] claim through experimentation that latent context
variables are better extracted using non-explicit features. In order
to strengthen their claim, they pre-configured the proposed
algorithms with parameters (such as mapping certain tags with
positive or negative score) to deal with no-negative feedback or
one-class problem [25] typical of implicit modeling.
Zheng et al.[16] emphasized more on the quality of
recommendation instead of capturing contextual information
merely. They preferred to use MF instead of TF for making
recommendations due to high computational cost of tensor
modeling. In order to support their claim, they introduced the
notion of contextual factors to refer to different context variables.
For using more detailed contextual information, they decomposed
contextual factors into contextual condition to describe the
features of each context. The experimental evaluation
demonstrated that this approach can deal well with sparsity and
high-dimensionality issue. However, if we analyze the work we
can find that due to very limited contextual information, this
approach is prone to cold-start issue. Also the paper was
published in year 2014, the claims might be valid according to the
status of context-aware research during that time. But latest
context-aware publications based on extended factorization
methods (TF) are better ranked in the research; and they can deal
well with all or most of the challenges addressed above.
So we have seen so far that a major group of researchers focus
on contextual acquisition and user modeling methods; and they
have proposed different factorization techniques to reduce the
high dimensional space resultant of massive number of contexts
available. One way of generating relevant contexts to mitigate
sparse rating matrices is to give some control to the experts in
personalized recommendation systems [17]. The system can
recommend top-N experts to the users instead; who can assign
intelligently different contextual factors to similar users based on
their historical data. TAPER [17] uses semi-autonomous ways by
merging tensor modeling with experts’ opinions to meet the
challenges of CARS. We believe that the authors claim to
generate recommendations using experts’ involvement could be
more strengthen if they use varying amount of test data instead of
relying on training data solely.
As a final note, we would like to discuss a paper that
integrates all these design points into one generalized model
GFF[18]. GFF can work with both explicit and implicit feedback;
has ability to accommodate any type of context; and most
importantly it takes any preference model as input of the
algorithm to incorporate recommendation context. GFF has
opened several research paths in context-aware recommendation
research to work upon, out of which optimality of preference
modeling is still an open question.
4. FUTURE CHALLENGES
In this section, we talk about some of the future challenges
briefly that can improve the working of factorization methods in
CARS. We listed these future directions based on the analysis of
the results and what these researchers have proposed in their
future work.
Most of the contemporary factorization methods, even some
from the selected papers in the report [14][18], make use of
pointwise ranking ({i1, r1} {i2, r2} {i3, r3} : i1 =item1,
r1=associated rank) in the methodology [26]. They basically score
the items based on their independent scores. These techniques can
be improved if we use pairwise approach [26] to give optimal
ordering to the pairs of items ({i1 > i2} {i2 > i3} {i3 > i4}) as
discussed in [27].
Some of the techniques as seen in [15][18] use learned weight
parameters to tune their algorithms so that they can work more
effectively, but this practice results in overfitting. One possible
future direction is to handle these issues related to overfitting as
observed in these proposed models.
Although some of the contemporary methods use implicit
feedback data [15][18], while explicit context is still being used in
others. Explicit feedback is quite often used in conventional RS.
Also it is believed that implicit feedback can bring more
contextual factors in the recommendation function. Researchers in
this field seem to work hard to mitigate the issues associated with
implicit feedback such as noisy positive or no-negative feedback
[18]. And they seem to ignore the strength of explicit feedback
which is prone to privacy issues such as exact context of the user
is known. These factorization methods uses latent context to
tackle with these privacy issue from the implicit or inferred
feedback. As a future direction, factorization methods should also
be devised to provide privacy protection in explicit feedback data.

5. It can be seen from some potential proposed algorithms such
as iTALX[18] and TFMAP[27] that TF based methods are
optimized for ranking based recommendations; but they have a
large number of model parameters that sooner or later grow
exponentially with an increasing number of contexts. One
possible future direction is to tune or reduce these parameters so
that TF based algorithms don’t suffer from computational
complexity.
5. CONCLUSION
In this survey report, we have presented few potential state-of-
the-art CARS algorithms based on factorization methods such as
matrix and tensor decomposition methods. Besides discussing
these algorithms, we also highlighted major key features and
design challenges that are being addressed. To our interest, it is
noted that each of the technique has the capability to deal with one
or more design challenges.
We can see that TF is most novel of these techniques that is
applied to CARS algorithms and it gives promising results.
Although it is a huge topic and still much research is at abstract
level, but its capability to provide quality recommendations and to
address the challenges, such as dimensionality reduction, cold-
start, and scalability is an indication that they will be researched
extensively in the future.
We concluded that application of these factorization
techniques in context aware recommendation algorithms is rather
a new subject and there are numerous challenges that prevent the
widespread implementation of these methods in various
environments. Possibly, there is a strong urge to utilize these
factorization methods in conjunction with each other and other
related methods to further support the implementation of these
techniques in CARS.
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