Service bridges the gap among bodily and

Service Rating Prediction by Exploring Social Mobile
Users’ Geographical Locations

 

                 
T. Sai Kishore                                                 
Dr. A V SRIHARSHA

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 II.M.Tech student, Department of Computer
Science,                      Professor,
Department of Computer science,

Sree Vidyanikethan Engineering College
Tirupati, India.      Sree Vidyanikethan
Engineering CollegeTirupati,India

 

Abstract: Currently, advances in shrewd mobile tool and
positioning strategies have fundamentally improved social networks, which
allows users to share their reports, opinions, scores, pictures, take a look
at-ins, and many others. The geographical data positioned by way of clever
cellphone bridges the gap among bodily and virtual worlds. Region records
functions as the connection between consumer’s physical behaviors and virtual
social networks established by using the clever smartphone or internet
services. We confer with these social networks regarding geographical
information as region-based social networks (LBSNs). On this paper, we make
full use of the mobile users’ place sensitive traits to carry out score
predication. We mine: 1) the relevance among person’s ratings and person-object
geographical region distances, called as user-item geographical connection, 2)
the relevance among users’ score variations and user-consumer geographical
location distances, known as user-person geographical connection. it’s miles
observed that people’ rating behaviors are stricken by geographical place
considerably.

 

 

Keywords:
 physical
behaviors geographical data, mobile users, social networks.

 

1.INTRODUCTION:

Recently
, with the fast development of cell devices and ubiquitous net get entry to,
social community offerings, along with facebook, Twitter, Yelp, Foursquare,
Epinions, emerge as regular. consistent with information, clever smartphone
customers have produced records quantity ten times of a popular cellular phone.
In 2015, there had been 1.nine billion clever phone customers within the
international, and 1/2 of them had accessed to social community services. The
mobile device or on-line place based totally social networks (LBSNs), we can
proportion our geographical role data or test-ins. This provider has attracted
tens of millions of customers. It additionally lets in users to share their
reviews, which includes opinions, ratings, pics, take a look at-ins and moods
in LBSNs with their pals. Such data brings opportunities and challenges for
recommender systems.

 

2. LITERATURE SURVEY:

 

2.1.
G. Adomavicius, and A. Tuzhilin:

An
overview of the field of recommender systems and describes the current
generation of recommendation methods that are usually classified into the
following three main categories: content-based, collaborative, and hybrid
recommendation approaches. This paper also describes various limitations of
current recommendation methods and discusses possible extensions that can
improve recommendation capabilities and make recommender systems applicable to
an even broader range of applications. These extensions include, among others,
an improvement of understanding of users and items, incorporation of the
contextual information into the recommendation process, support for multi
criteria ratings, and a provision of more flexible and less intrusive types of
recommendations.

RECOMMENDER
systems have become an important research area since the appearance of the
first papers on collaborative filtering in the mid-1990s 45, 86, 97.
There has been much work done both in the industry and academia on developing
new approaches to recommender systems over the last decade. The interest in
this area still remains high because it constitutes a problem-rich research
area and because of the abundance of practical applications that help users to
deal with information overload and provide personalized recommendations,
content, and services to them. However, despite all of these advances, the
current generation of recommender systems still requires further improvements
to make recommendation methods more effective and applicable to an even broader
range of real-life applications, including recommending vacations, certain
types of financial services to investors, and products to purchase in a store
made by a “smart” shopping cart .

These
improvements include better methods for representing user behavior and the
information about the items to be recommended, more advanced recommendation
modeling methods, incorporation of various contextual information into the
recommendation process, utilization of multi criteria ratings, development of
less intrusive and more flexible recommendation methods that also rely on the
measures that more effectively determine performance of recommender systems.
Although the roots of recommender systems can be traced back to the extensive
work in cognitive science 87, approximation theory 81, information
retrieval 89, forecasting theories 6, and also have links to management
science.

 

2.2.
Y. Koren

Recommender systems
provide users with personalized suggestions for products or services. These
systems often rely on Collaborating Filtering (CF), where past transactions are
analyzed in order to establish connections between users and products. The two
more successful approaches to CF are latent factor models, which directly
profile both users and products, and neighborhood models, which analyze
similarities between products or users. In this work we introduce some
innovations to both approaches. The factor and neighborhood models can now be
smoothly merged, thereby building a more accurate combined model. Further
accuracy improvements are achieved by extending the models to exploit both explicit
and implicit feedback by the users. The methods are tested on the Netflix data.
Results are better than those previously published on that dataset.

Modern
consumers are inundated with choices. Electronic retailers and content
providers offer a huge selection of products, with unprecedented opportunities
to meet a variety of special needs and tastes. Matching consumers with most
appropriate products is not trivial, yet it is a key in enhancing user
satisfaction and loyalty. This emphasizes the prominence of recommender
systems, which provide personalized recommendations for products that suit a
user’s taste 1. Internet leaders like Amazon, Google, Netflix, TiVo and Yahoo
are increasingly adopting such recommenders. . Notably, CF techniques require
no domain knowledge and avoid the need for extensive data collection. In
addition, relying directly on user behavior allows uncovering complex and
unexpected patterns that would be difficult or impossible to profile using
known data attributes. As a consequence, CF attracted much of attention in the
past decade, resulting in significant progress and being adopted by some
successful commercial systems, including Amazon 15, TiVo and Netflix.

Neighborhood
models are most effective at detecting very localized relationships. They rely
on a few significant neighborhood relations, often ignoring the vast majority
of ratings by a user. Consequently, these methods are unable to capture the
totality of weak signals encompassed in all of a user’s ratings. Latent factor models
are generally effective at estimating overall structure that relates
simultaneously to most or all items. However, these models are poor at
detecting strong associations among a small set of closely related items,
precisely where neighborhood models do best.

 

2.3.          
N. N. Liu, M. Zhao, and Q. Yang

 

A
central goal of collaborative filtering (CF) is to rank items by their
utilities with respect to individual users in order to make personalized
recommendations. Traditionally, this is often formulated as a rating prediction
problem. However, it is more desirable for CF algorithms to address the ranking
problem directly without going through an extra rating prediction step. In this
paper, we propose the probabilistic latent preference analysis (pLPA) model for
ranking predictions by directly modeling user preferences with respect to a set
of items rather than the rating scores on individual items. From a user’s
observed ratings, we extract his preferences in the form of pairwise
comparisons of items which are modeled by a mixture distribution based on
BradleyTerry model. An EM algorithm for fitting the corresponding latent class
model as well as a method for predicting the optimal ranking are described.
Experimental results on real world data sets demonstrated the superiority of
the proposed method over several existing CF algorithms based on rating
predictions in terms of ranking performance measure NDCG.

 

Recommender
system is a promising technology that aims to automatically generate item
recommendations from a huge collection of items based on users’ past feedback.
Broadly speaking, existing technologies underlying recommender systems fall
into either of the following two categories: content-based filtering versus
collaborative filtering. Content-based filtering approach analyzes the content
information associated with the items and users such as product descriptions,
user profiles etc., in order to represent users and items using a set of
features. To recommend new items to a user, content-based filters match their
representations to those items the user has expressed interests on. In
contrast, the collaborative filtering(CF) approach does not require any content
information about the items, it works by collecting ratings on the items by a
large number of users and make recommendations to a user based on the
preference patterns of other users. The CF approach is based on the assumption
that a user is often interested in those items that have been selected by some
users with similar tastes.

A
very important function of most recommender systems is the generation of the
Top-N item list for each user in order to make personalized recommendations,
which essentially involves solving a ranking problem. To rank items, most
collaborative filtering algorithms formulate this as a rating prediction
problem in which a user’s potential ratings on the items are first predicted
and then used to order the items. However, there are several drawbacks with
such rating prediction based framework.

 

2.4.
M. Jamali, and M. Ester

Recommender
systems are becoming tools of choice to select the online information relevant
to a given user. Collaborative filtering is the most popular approach to
building recommender systems and has been successfully employed in many
applications. With the advent of online social networks, the social network
based approach to recommendation has emerged. This approach assumes a social
network among users and makes recommendations for a user based on the ratings
of the users that have direct or indirect social relations with the given user.

 

As one of their major benefits, social network based
approaches have been shown to reduce the problems with cold start users. In
this paper, we explore a model-based approach for recommendation in social
networks, employing matrix factorization techniques. Advancing previous work,
we incorporate the mechanism of trust propagation into the model. Trust
propagation has been shown to be a crucial phenomenon in the social sciences,
in social network analysis and in trust-based recommendation. We have conducted
experiments on two real life data sets, the public domain Epinions.com dataset
and a much larger dataset that we have recently crawled from Flixster.com. Our
experiments demonstrate that modeling trust propagation leads to a substantial
increase in recommendation accuracy, in particular for cold start users.

For instance, while we seek a restaurant thinking
about comfort, we will by no means pick out a far flung one. Furthermore, if
the geographical vicinity information and social networks can be mixed, it
isn’t always tough to find that our mobility can be influenced by way of our
social relationships as customers may also favor to go to the places or eat the
gadgets their friends visited or consumed before. In our opinion, while customers
take a long journey, they will preserve an excellent emotion and attempt their
fine to have a pleasant journey. Maximum of the services they devour are the
local featured things.

 

2.5.            
 J.
Zhang, C. Chow, and Y. Li

Geographical influence has been intensively
exploited for location recommendations in location-based social networks
(LBSNs) due to the fact that geographical proximity significantly affects
users’ check-in behaviors. However, current studies only model the geographical
influence on all users’ check-in behaviors as a universal way. We argue that
the geographical influence on users’ check-in behaviors should be personalized.
In this paper, we propose a personalized and efficient geographical location
recommendation framework called iGeoRec to take full advantage of the
geographical influence on location recommendations.

In iGeoRec, there are
mainly two challenges: (1) personalizing the geographical influence to
accurately predict the probability of a user visiting a new location, and (2)
efficiently computing the probability of each user to all new locations. To
address these two challenges, (1) we propose a probabilistic approach to
personalize the geographical influence as a personal distribution for each user
and predict the probability of a user visiting any new location using her
personal distribution. Furthermore, (2) we develop an efficient approximation
method to compute the probability of any user to all new locations; the
proposed method reduces the computational complexity of the exact computation
method from O(ILIn3) to O(ILIn) (where ILI is the total number of locations in
an LBSN and n is the number of check-in locations of a user).

Finally, we conduct
extensive experiments to evaluate the recommendation accuracy and efficiency of
iGeoRec using two large-scale real data sets collected from the two of the most
popular LBSNs: Foursquare and Gowalla. Experimental results show that iGeoRec
provides significantly superior performance compared to other state-of-the-art
geographical recommendation techniques.

 

2.6.            
J. Zhang and C. Chow

 

 

5. CONCLUSION:

Recommending
users with their preferred points-of-interest (POIs), e.g., museums and
restaurants, has become an important feature for location-based social networks
(LBSNs), which benefits people to explore new places and businesses to discover
potential customers. However, because users only check in a few POIs in an
LBSN, the user-POI check-in interaction is highly sparse, which renders a big
challenge for POI recommendations. To tackle this challenge, in this study we
propose a new POI recommendation approach called GeoSoCa through exploiting
geographical correlations, social correlations and categorical correlations
among users and POIs.

The geographical, social and categorical correlations
can be learned from the historical check-in data of users on POIs and utilized
to predict the relevance score of a user to an unvisited POI so as to make
recommendations for users. First, in GeoSoCa we propose a kernel estimation
method with an adaptive bandwidth to determine a personalized check-in
distribution of POIs for each user that naturally models the geographical
correlations between POIs. Then, GeoSoCa aggregates the check-in frequency or
rating of a user’s friends on a POI and models the social check-in frequency or
rating as a power-law distribution to employ the social correlations between
users.

Further, GeoSoCa applies the bias of a user on a POI
category to weigh the popularity of a POI in the corresponding category and
models the weighed popularity as a power-law distribution to leverage the
categorical correlations between POIs. Finally, we conduct a comprehensive
performance evaluation for GeoSoCa using two large-scale real-world check-in
data sets collected from Foursquare and Yelp. Experimental results show that
GeoSoCa achieves significantly superior recommendation quality compared to
other state-of-the-art POI recommendation techniques.

 

3. CONCLUSION:

We
mine: 1) the relevance among customers’ scores and consumer-item geographical
place distances, 2) the relevance among customers’ score differences and
consumer-consumer geographical region distances. it’s far discovered that
people’ score behaviors are tormented by geographical vicinity extensively. a
personalized location based totally rating Prediction (LBRP) version is
proposed through combining 3 factors: user-object geographical connection,
person-user geographical connection, and interpersonal hobby similarity.
Specifically, the geographical place denotes consumer’s real-time mobility,
especially while customers journey to new cities, and those elements are fused
collectively to improve the accuracy and applicability of recommender systems.
In our destiny paintings, test-in behaviors of users can be deeply explored by
thinking about the thing of their multi-activity facilities and the
characteristic of POIs.

 

REFERENCES:

1.       
G. Adomavicius, and A. Tuzhilin, “Toward
the next generation of recommender systems: a survey of the state-of-the-art
and possible extensions,” IEEE Transactions on Knowledge and Data Engineering,
pp. 734-749, Jun. 2005.

2.       
Y. Koren, “Factorization meets the
neighborhood: a multifaceted collaborative filtering model,” KDD’08, 2008.

3.       
N. N. Liu, M. Zhao, and Q. Yang,
“Probabilistic latent preference analysis for collaborative filtering,”
CIKM’09, pp. 759-766, 2009.

4.       
M. Jamali, and M. Ester, “A matrix
factorization technique with trust propagation for recommendation in social
networks,” ACM RecSys, 2010.

5.       
C. Zhang, L. D. Shou, K. Chen, G. Chen,
and Y. J. Bei, “Evaluating Geo-Social Influence in Location-Based Social
Networks,” CIKM’12, Oct. 2012.

6.       
H. Gao, J. Tang, X. Hu, H. Liu,
“Exploring Temporal Effects for Location Recommendation on Location-Based
Social Networks,” RecSys’13, 2013.

7.       
L. Hu, A. Sun, Y. Liu, “Your Neighbors
Affect Your Ratings: On Geographical Neighborhood Influence to Rating
Prediction,” ACM SIGIR’14, 2014.

 

8.       
J. Zhang and C. Chow, “CoRe: Exploiting
the Personalized Influence of Two-dimensional Geographic Coordinates for
Location Recommendations,” Information Sciences, vol. 293, pp.163-181, 2015.

 

9.       
J. Zhang, C. Chow, and Y. Zheng, “ORec:
An Opinion-Based Point-of-Interest Recommendation Framework,” ACM CIKM’15,
2015.

10.    J.
Zhang and C. Chow, “GeoSoCa: Exploiting Geographical, Social and Categorical
Correlations for Point-of-Interest Recommendations,” ACM SIGIR’15, 2015.

 

 

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