Improving Context Aware Recommendation Performance by Using Social Networks

2014 ◽  
Vol 7 (3) ◽  
pp. 1-14
Author(s):  
Golshan Assadat Afzali Boroujeni ◽  
Seyed Alireza Hashemi Golpayegani
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
High Volume ◽  
Context Aware ◽  
Mobile Data ◽  
The People ◽  
Proposed Model ◽  
The Social ◽  
Current Context

Ecommerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. In collaborative filtering—as the most popular method in recommender systems—an implicit network is formed among all the people. In any network, there are some individuals who have some inspirational power over the others leading them to influence their decisions and behaviours. But it seems that these methods do not support context awareness in mobile commerce environments. Furthermore, they lack high accuracy and also require high volume of computations due to not distinguish between neighbours as a friend or a stranger. This paper proposes a new model for recommender systems which are based on mobile data. This model uses these data to extract current users' context and also to identify individuals with the highest influence. Then, the system uses the information of these identified impressive users in the current context existed in the social networks for making recommendations. Beside of achieving higher accuracy, the proposed model has resolved cold start problem in collaborative filtering systems.

Improving Context Aware Recommendation Performance by Using Social Networks

2015 ◽  
Vol 8 (3) ◽  
pp. 73-87
Author(s):  
Golshan Assadat Afzali Boroujeni ◽  
Seyed Alireza Hashemi Golpayegani
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
High Volume ◽  
Context Aware ◽  
Mobile Data ◽  
The People ◽  
Proposed Model ◽  
The Social ◽  
Current Context

Ecommerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. In collaborative filtering - as the most popular method in recommender systems - an implicit network is formed among all the people. In any network, there are some individuals who have some inspirational power over the others leading them to influence their decisions and behaviours. But it seems that these methods do not support context awareness in mobile commerce environments. Furthermore, they lack high accuracy and also require high volume of computations due to not distinguish between neighbours as a friend or a stranger. This paper proposes a new model for recommender systems which are based on mobile data. This model uses these data to extract current users' context and also to identify individuals with the highest influence. Then, the system uses the information of these identified impressive users in the current context existed in the social networks for making recommendations. Beside of achieving higher accuracy, the proposed model has resolved cold start problem in collaborative filtering systems.

scholarly journals Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1257 ◽  
Author(s):  
Liang Zhang ◽  
Quanshen Wei ◽  
Lei Zhang ◽  
Baojiao Wang ◽  
Wen-Hsien Ho
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relationships ◽  
Two Stage ◽  
Proposed Model ◽  
The Social ◽  
Item List ◽  
Ranking Strategy ◽  
High Prediction ◽  
Optimization Mechanism

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

scholarly journals Towards an Inclusive Social Welfare Institution for Disabilities: The Case of Indonesia

2020 ◽  
Vol 5 (1) ◽  
pp. 18-28
Author(s):  
Enung Huripah
Keyword(s):  
Social Welfare ◽  
People With Disabilities ◽  
Secondary Data ◽  
Public Infrastructure ◽  
Data Utilization ◽  
The People ◽  
The Social ◽  
Current Context ◽  
Welfare Institution ◽  
Collection Methods

People with disabilities are vulnerable to experience exclusion and discrimination in society. Likewise in Indonesia, a common condition for people with disabilities is the low level of participation in various sectors, such as the economy, education, healthcare, and public infrastructure. However, Indonesia has committed and started efforts to improve the equality of people with disabilities’ access. One institution that plays an important role in this regard is the social welfare institution. On a related note, this study discusses the dynamic of the social welfare institution’s roles in Indonesia in providing welfare for people with disabilities. Furthermore, the roles are explored based on the current context of society, which over the last few years, has been changing rapidly due to technological advancements, information acceleration, and big data utilization. This study argues building an inclusive social welfare institution is fundamental to fulfill the people with disabilities’ welfare. This study uses a qualitative approach with literature review and secondary data analysis as data collection methods.

scholarly journals A study of neighbour selection strategies for POI recommendation in LBSNs

2018 ◽  
Vol 44 (6) ◽  
pp. 802-817 ◽  
Author(s):  
Carlos Rios ◽  
Silvia Schiaffino ◽  
Daniela Godoy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Location Based Services ◽  
Geographical Information ◽  
Good Prediction ◽  
General Terms ◽  
The Impact ◽  
Filtering Approach

Location-based recommender systems (LBRSs) are gaining importance with the proliferation of location-based services provided by mobile devices as well as user-generated content in social networks. Collaborative approaches for recommendation rely on the opinions of like-minded people, so-called neighbours, for prediction. Thus, an adequate selection of such neighbours becomes essential for achieving good prediction results. The aim of this work is to explore different strategies to select neighbours in the context of a collaborative filtering–based recommender system for POI (places of interest) recommendations. Whereas standard methods are based on user similarity to delimit a neighbourhood, in this work several strategies are proposed based on direct social relationships and geographical information extracted from location-based social networks (LBSNs). The impact of the different strategies proposed has been evaluated and compared against the traditional collaborative filtering approach using a dataset from a popular network as Foursquare. In general terms, the proposed strategies for selecting neighbours based on the different elements available in a LBSN achieve better results than the traditional collaborative filtering approach. Our findings can be helpful both to researchers in the recommender systems area and to recommender system developers in the context of LBSNs, since they can take into account our results to design and provide more effective services considering the huge amount of knowledge produced in LBSNs.

scholarly journals Optimizing Parallel Collaborative Filtering Approaches for Improving Recommendation Systems Performance

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 155 ◽  
Author(s):  
Christos Sardianos ◽  
Grigorios Ballas Papadatos ◽  
Iraklis Varlamis
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Information Filtering ◽  
Prediction Performance ◽  
Research Interest ◽  
Product Review ◽  
Time Efficiency ◽  
The Past ◽  
Product Rating

Recommender systems are one of the fields of information filtering systems that have attracted great research interest during the past several decades and have been utilized in a large variety of applications, from commercial e-shops to social networks and product review sites. Since the applicability of these applications is constantly increasing, the size of the graphs that represent their users and support their functionality increases too. Over the last several years, different approaches have been proposed to deal with the problem of scalability of recommender systems’ algorithms, especially of the group of Collaborative Filtering (CF) algorithms. This article studies the problem of CF algorithms’ parallelization under the prism of graph sparsity, and proposes solutions that may improve the prediction performance of parallel implementations without strongly affecting their time efficiency. We evaluated the proposed approach on a bipartite product-rating network using an implementation on Apache Spark.

Toward Social-Semantic Recommender Systems

2016 ◽  
Vol 7 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Dalia Sulieman ◽  
Maria Malek ◽  
Hubert Kadima ◽  
Dominique Laurent
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Semantic Information ◽  
Basic Problem ◽  
Real Data ◽  
Data Sets ◽  
The Social ◽  
Recommendation Algorithms ◽  
Hybrid Recommendation

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

scholarly journals PENGARUH PENGGUNAAN FACEBOOK BAGI KEHIDUPAN ROHANI MAHASISWA STKIP WIDYA YUWANA

2019 ◽  
Vol 19 (2) ◽  
pp. 28-43
Author(s):  
Anastasia Dwilestari ◽  
Agustinus Wisnu Dewantara
Keyword(s):  
Social Networks ◽  
Qualitative Method ◽  
Spiritual Life ◽  
The People ◽  
Or Groups ◽  
The Social ◽  
Problem Formulations ◽  
The Times ◽  
And Behavior ◽  
The Church

The church is always determined to serve the people of its time, and also to keep abreast of the times with its ways. The development of the technological age that is seen, one of them is the internet that provides various kinds of social networks. Facebook is one of the social networks used in everyday life and influences the user. Based on the background above, the researcher can formulate a number of problem formulations as follows: What is meant by Facebook? What is meant by spiritual life? What is the influence using of Facebook on the spiritual life of students in STKIP Widya Yuwana Madiun? This study aims to describe the meaning of Facebook; describe the meaning of spiritual life, describe the influence using of Facebook on spiritual life of students in STKIP Widya Yuwana. This study used a qualitative method by collecting data through interviews with 8 respondents. Qualitative research is an open interview as an effort to examine and understand the attitudes, views, feelings and behavior of individuals or groups of people on a problem. Qualitative methods are as a form of research that is more focused on efforts to see, understand attitudes, feelings, views and behaviors both individually and in groups regarding an event.

scholarly journals User models for multi-context-aware music recommendation

2020 ◽  
Author(s):  
Martin Pichl ◽  
Eva Zangerle
Keyword(s):  
Recommender Systems ◽  
User Model ◽  
Context Aware ◽  
Music Recommendation ◽  
Music Consumption ◽  
Current Context ◽  
Music Recommender Systems ◽  
Context Features ◽  
Content Information ◽  
Music Streaming

Abstract In the last decade, music consumption has changed dramatically as humans have increasingly started to use music streaming platforms. While such platforms provide access to millions of songs, the sheer volume of choices available renders it hard for users to find songs they like. Consequently, the task of finding music the user likes is often mitigated by music recommender systems, which aim to provide recommendations that match the user’s current context. Particularly in the field of music recommendation, adapting recommendations to the user’s current context is critical as, throughout the day, users listen to different music in numerous different contexts and situations. Therefore, we propose a multi-context-aware user model and track recommender system that jointly exploit information about the current situation and musical preferences of users. Our proposed system clusters users based on their situational context features and similarly, clusters music tracks based on their content features. By conducting a series of offline experiments, we show that by relying on Factorization Machines for the computation of recommendations, the proposed multi-context-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems.

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