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 Information Filtering via Biased Random Walk on Coupled Social Network

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Da-Cheng Nie ◽  
Zi-Ke Zhang ◽  
Qiang Dong ◽  
Chongjing Sun ◽  
Yan Fu
Keyword(s):  
Random Walk ◽  
Recommender Systems ◽  
Information Filtering ◽  
Real Data ◽  
Data Sets ◽  
Biased Random Walk ◽  
The Social ◽  
Social Interests ◽  
Purchase Process ◽  
Walk Algorithm

The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users’ purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users’ preference. Numerical analyses on two real data sets,EpinionsandFriendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods.

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.

Improving Collaborative Filtering Algorithms

2020 ◽  
pp. 638-657
Author(s):  
Firas Ben Kharrat ◽  
Aymen Elkhleifi ◽  
Rim Faiz
Keyword(s):  
Social Network ◽  
Web Sites ◽  
Opinion Mining ◽  
Semantic Analysis ◽  
Real Data ◽  
Data Sets ◽  
Suggested Approach ◽  
Opinion Score ◽  
Positive Results ◽  
Cold Start Problem

This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approach means that posts are not merely characterized by an opinion score, but receive an opinion grade notion in the post instead. In general, the authors' approach has been implemented with Java and Lenskit framework. The study resulted in two real data sets, namely MovieLens and TripAdvisor, in which the authors have shown positive results. They compared their algorithm to SVD and Slope One algorithms. They have fulfilled an amelioration of 10% in precision and recall along with an improvement of 12% in RMSE and nDCG.

Improving Collaborative Filtering Algorithms

2016 ◽  
Vol 7 (3) ◽  
pp. 99-118 ◽  
Author(s):  
Firas Ben Kharrat ◽  
Aymen Elkhleifi ◽  
Rim Faiz
Keyword(s):  
Social Network ◽  
Web Sites ◽  
Opinion Mining ◽  
Semantic Analysis ◽  
Real Data ◽  
Data Sets ◽  
Recommendation Algorithm ◽  
Suggested Approach ◽  
Opinion Score ◽  
Positive Results

This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approach means that posts are not merely characterized by an opinion score, but receive an opinion grade notion in the post instead. In general, the authors' approach has been implemented with Java and Lenskit framework. The study resulted in two real data sets, namely MovieLens and TripAdvisor, in which the authors have shown positive results. They compared their algorithm to SVD and Slope One algorithms. They have fulfilled an amelioration of 10% in precision and recall along with an improvement of 12% in RMSE and nDCG.

scholarly journals Inferring Ties in Social IoT Using Location-Based Networks and Identification of Hidden Suspicious Ties

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Nauman Ali Khan ◽  
Sihai Zhang ◽  
Wuyang Zhou ◽  
Ahmad Almogren ◽  
Ikram Ud Din ◽  
...  
Keyword(s):  
Social Networks ◽  
Internet Of Things ◽  
Social Ties ◽  
Social Activity ◽  
Geographical Location ◽  
Real Data ◽  
Time Frame ◽  
Data Sets ◽  
The Social ◽  
Time Frames

Stochastic Internet of Things (IoT)-based communication behavior of the progressing world is tremendously impacting social networks. The growth of social networks helps to quantify the effect on the Social Internet of Things (SIoT). Multiple existences of two persons at several geographical locations in different time frames hint to predict the social connection. We investigate the extent to which social ties between people can be inferred by critically reviewing the social networks. Our study used Chinese telecommunication-based anonymized caller data records (CDRs) and two openly available location-based social network data sets, Brightkite and Gowalla. Our research identified social ties based on mobile communication data and further exploits communication reasons based on geographical location. This paper presents an inference framework that predicts the missing ties as suspicious social connections using pipe and filter architecture-based inference framework. It highlights the secret relationship of users, which does not exist in real data. The proposed framework consists of two major parts. Firstly, users’ cooccurrence based on the mutual location in a specific time frame is computed and inferred as social ties. Results are investigated based upon the cooccurrence count, the gap time threshold values, and mutual friend count values. Secondly, the detail about direct connections is collected and cross-related to the inferred results using Precision and Recall evaluation measures. In the later part of the research, we examine the false-positive results methodically by studying the human cooccurrence patterns to identify hidden relationships using a social activity. The outcomes indicate that the proposed approach achieves comprehensive results that further support the theory of suspicious ties.

scholarly journals A fusion fuzzy model for detecting phony accounts in social networks

2018 ◽  
Vol 7 (4) ◽  
pp. 2738
Author(s):  
P. Srinivas Rao ◽  
Jayadev Gyani ◽  
G. Narsimha
Keyword(s):  
Social Network ◽  
Fuzzy Model ◽  
Machine Learning Techniques ◽  
Personal Attributes ◽  
Data Sets ◽  
Learning Techniques ◽  
The Social ◽  
Using Data ◽  
Removal Technique ◽  
Normal Rank

In online social network’s phony account detection is one of the major task among the ability of genuine user from forged user account. The fundamental objective of detection of phony account framework is to detect fake account and removal technique in Social network user sites. This work concentrates on detection of phony account in which it depends on normal basis framework, transformative Algorithms and fuzzy technique. Initially, the most essential attributes including personal attributes, comparability techniques and various real user review, tweets, or comments are extricated. A direct blend of these attributes demonstrates the significance of each reviews tweets comments etc. To compute closeness measure, a consolidated strategy in view of artificial honey bee state Algorithm and fuzzy technique are utilized. Second approach is proposed to alter the best weights of the normal user attributes utilizing the social network activities/transaction and inherited Algorithm. Finally, a normal rank rationale framework is utilized to calculate the final scoring of normal user activities. The decision making of proposed approach to find phony account are variation with existing techniques user behavioral analysis using data sets and machine learning techniques such as crowdflower_sample and genuine_accounts_sample dataset of facebook and Twitter. The outcomes demonstrate that proposed strategy overcomes the previously mentioned strategies. 

scholarly journals An Efficient and Intelligent Recommender System for Mobile Platform

2018 ◽  
Vol 37 (4) ◽  
pp. 463-480
Author(s):  
Muhammad Jabbar ◽  
Qaisar Javaid ◽  
Muhammad Arif ◽  
Asim Munir ◽  
Ali Javed
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Past History ◽  
Subjective Evaluation ◽  
Mobile Platform ◽  
Value Addition ◽  
Hybrid Scheme ◽  
Hybrid Approaches ◽  
User Ratings ◽  
Hybrid Recommendation

Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.

Marketing based on user behavior on Facebook social network through recommender system design

2015 ◽  
Vol 3 ◽  
pp. 27 ◽  
Author(s):  
Bahareh Shadi Shams Zamenjani
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommender Systems ◽  
Recommender System ◽  
User Behavior ◽  
Real Data ◽  
User Interests ◽  
Second Stage ◽  
Commercial Use ◽  
High Level

t— the influence of social networks among people and at the same time inevitable spread of commercial use of them. Accordingly, in order to sell products, recommender systems designed based on user behavior on social networks, providing a variety of commercial offers tailored to the user. The accuracy of recommender systems that make recommendations to users, and how many of the proposals are accepted by the users is important. In this paper, a recommender system is designed based on user behavior in social network Facebook in two acts and suggests that users purchase their favorite products. The first step is to examine user behavior based on user interests will be given an offer to buy products. In the second stage recommender system uses data mining techniques and suggestions to the user that is associated with their previous purchases. This is real data and the real results of it and it is valid, as well as the results show a high level of accuracy recommender system is designed to offer suggestions to users.

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