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 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.

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.

scholarly journals Enhanced Hybrid Recommender System using Social Friend Network

2014 ◽  
Vol 10 (4) ◽  
pp. 2023-2031
Author(s):  
Shalmali A. Patil ◽  
Reena Pagare
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Recommender System ◽  
Social Relationship ◽  
Information Overload ◽  
Great Success ◽  
Traditional System ◽  
Use Of Data ◽  
The Social ◽  
Hybrid Recommender

Lots of people employ recommender systems to diminish the information overload over the internet. This leads the user in a personalized manner to hit upon interesting or helpful objects in a huge space of possible options. Amongst different techniques, Collaborative filtering recommender system has pulled off great success. But this technique pays no heed towards the social relationship of the users. This problem gave birth to the Social recommender system technology which possesses the capability to recognize users likings and preferences and their social relationships. In this paper, we present novel method where we combine collaborative filtering recommender system with social friend network to use social relationships. For this, we have made use of data related to users which provides their interests as well as their social relationship. Our method helps to find the friends with dissimilar tastes and determine the close friends amongst direct friends of targeted user which has more similar tastes. This proposed approach resulted in more precise and realistic results than traditional system.

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.

The Staged Nature of Decision Making among Senior Tourists

2019 ◽  
Vol 59 (4) ◽  
pp. 602-613 ◽  
Author(s):  
Juan Luis Nicolau ◽  
Nieves Losada ◽  
Elisa Alén ◽  
Trinidad Domínguez
Keyword(s):  
Decision Making ◽  
Decision Making Process ◽  
Social Character ◽  
Two Stage ◽  
Decision Stage ◽  
Proposed Model ◽  
The Social ◽  
The Individual ◽  
The Way

This article builds on the idea that senior tourists’ decision making is a staged process in which the different choices are sequential, interrelated, and interdependent. These decisions are “whether to take a vacation," “whether to opt for an international trip," “whether to use an organized tour," and “whether to use publicly subsidized travel.” Considering the social character of many trips offered to seniors, the fourth decision of the proposed process makes it unique. No research has empirically considered using a staged decision making in the context of senior travelers, and the proposed model quantifies the effect of each variable based on the decision the individual is dealing with; also, the way a variable changes its effect even within the same decision stage depending on the individual is analyzed by including heterogeneity into the modeling. The results find that senior tourists follow the proposed four-staged decision-making process rather than the basic two-stage decision-making process.

The social aspects of consumption as predictors of consumer loyalty: online vs. offline services

2016 ◽  
Vol 27 (2) ◽  
Author(s):  
Rodoula H Tsiotsou
Keyword(s):  
Social Relationships ◽  
Design Methodology ◽  
Social Aspects ◽  
Online Services ◽  
Service Setting ◽  
Proposed Model ◽  
The Social ◽  
Service Brands ◽  
Soccer Fans

Purpose Nowadays, companies are seeking to create meaningful and long-term relationships with their customers. Therefore, the purpose of this study is to examine the role of parasocial and social aspects of consumption in building trustworthy and loyal relationships in both offline and online services. Design/methodology/approach Two studies were conducted using the survey research method. The first study collected data from 285 soccer fans, and the second study collected data from 298 Facebook consumers. Findings The study confirms the proposed model and suggests that parasocial and social relationships act as significant antecedents of service brand loyalty in both offline and online services.. Originality/value This is the first study that examines parasocial and social relationships in tandem and their role in developing loyal relationships with service brands. It also confirms that social relationships in a service setting play a significant role in predicting brand trust and loyalty.

Rethinking Social Relationships in Adulthood: The Differential Investment of Resources Model

2022 ◽  
pp. 108886832110670
Author(s):  
Oliver Huxhold ◽  
Katherine L. Fiori ◽  
Tim Windsor
Keyword(s):  
Social Relationships ◽  
Individual Characteristics ◽  
Theoretical Development ◽  
Future Research ◽  
Living Situation ◽  
Social Exchanges ◽  
Proposed Model ◽  
The Social ◽  
Different Types ◽  
Time And Energy

Empirical evidence about the development of social relationships across adulthood into late life continues to accumulate, but theoretical development has lagged behind. The Differential Investment of Resources (DIRe) model integrates these empirical advances. The model defines the investment of time and energy into social ties varying in terms of emotional closeness and kinship as the core mechanism explaining the formation and maintenance of social networks. Individual characteristics, acting as capacities, motivations, and skills, determine the amount, direction, and efficacy of the investment. The context (e.g., the living situation) affects the social opportunity structure, the amount of time and energy available, and individual characteristics. Finally, the model describes two feedback loops: (a) social capital affecting the individual’s living situation and (b) different types of ties impacting individual characteristics via social exchanges, social influences, and social evaluations. The proposed model will provide a theoretical basis for future research and hypothesis testing.

The Role of Social Capital on the Searching Behaviours of a Job Seeker in the Multiple Labour Markets

2021 ◽  
pp. 232102222110514
Author(s):  
Hiep Truong Thanh ◽  
Hong Nguyen Thi Bich
Keyword(s):  
Social Capital ◽  
Theoretical Model ◽  
Labour Markets ◽  
Two Stage ◽  
Reservation Wages ◽  
Proposed Model ◽  
The Social ◽  
Jel Classifications

This study aims to build a two-stage theoretical model to analyse the role of social capital on the searching behaviours of a job seeker in two different markets. As the advantage of the social capital in either market triggers the reservation wages in both two markets equally, the job seeker should prioritize his or her resources enhancing a larger amount of the social capital in a particular market. Consequently, the job seeker tends to search more intensively in the market where she or he has a higher level of social capital. That is the seeker can shorten the expected searching time. The proposed model also explains why the job seeker sometimes chooses the 2nd highest wage offer instead of the highest one. JEL Classifications: C02, D83, J64

scholarly journals Measuring user’s influence in the Yelp recommender system

2017 ◽  
Vol 1 (2) ◽  
pp. 91-104 ◽  
Author(s):  
Andres Bejarano ◽  
Agrima Jindal ◽  
Bharat Bhargava
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Design Methodology ◽  
User Reviews ◽  
Content Type ◽  
Attribute Weights ◽  
User Influence ◽  
Proposed Model ◽  
The Social

Purpose Recommender systems collect information about users and businesses and how they are related. Such relation is given in terms of reviews and votes on reviews. User reviews gather opinions, rating scores and review influence. The latter component is crucial for determining which users are more relevant in a recommender system, that is, the users whose reviews are more popular than the average user’s reviews. Design/methodology/approach A model of measure of user influence is proposed based on review and social attributes of the user. User influence is also used for determining how influenced has been a business being based on popular reviews. Findings Results indicate there is a connection between social attributes and user influence. Such results are relevant for marketing, credibility estimation and Sybil detections, among others. Originality/value The proposed model allows search parameterization based on the social attribute weights of users, reviews and businesses. Such weights defines the relevance on each attribute, which can be adjusted according to the search needs. Popularity results are then a function of weight preferences on user, reviews and businesses data join.

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