scholarly journals Determining Trust Based Examination on Social Networks for Hotel Recommendation

2019 ◽  
Vol 8 (9S4) ◽  
pp. 218-221 ◽  
Keyword(s):  
Social Networks ◽  
Social Trust ◽  
Research Work ◽  
Cold Start ◽  
Data Sparsity ◽  
Sparsity Problem ◽  
Implicit And Explicit ◽  
Fake Reviews ◽  
Trust Information

The objective of this research work is to enhance the performance of a novel recommendation site for tavern by mining the data about all kinds of hotel in websites. When we are about to view the rating of a hotel given by other people, we can’t rely accurately on it as there are many fake reviews. So we can overcome the problem by viewing only the ratings given by our friends and friends of friends in our trusted network. TrustSVD algorithm considered for ensuring this. By considering both the implicit and explicit opinions of ratings the predictions are made. The proposed technique is used to merge with social trust information thus from that we can get the trusted network. Thus, by using the trusted network reviews we can avoid the fake reviews. In case of cold start and data sparsity problem the friends of friends list is considered.

An effective social recommendation method based on user reputation model and rating profile enhancement

2018 ◽  
Vol 45 (5) ◽  
pp. 607-642 ◽  
Author(s):  
Sajad Ahmadian ◽  
Mohsen Afsharchi ◽  
Majid Meghdadi
Keyword(s):  
Recommender Systems ◽  
Cold Start ◽  
Reliability Measures ◽  
Data Sparsity ◽  
Reliability Measure ◽  
Trust Networks ◽  
Reputation Model ◽  
Real World Datasets ◽  
User Reputation ◽  
Trust Information

Trust-aware recommender systems are advanced approaches which have been developed based on social information to provide relevant suggestions to users. These systems can alleviate cold start and data sparsity problems in recommendation methods through trust relations. However, the lack of sufficient trust information can reduce the efficiency of these methods. Moreover, diversity and novelty are important measures for providing more attractive suggestions to users. In this article, a reputation-based approach is proposed to improve trust-aware recommender systems by enhancing rating profiles of the users who have insufficient ratings and trust information. In particular, we use a user reliability measure to determine the effectiveness of the rating profiles and trust networks of users in predicting unseen items. Then, a novel user reputation model is introduced based on the combination of the rating profiles and trust networks. The main idea of the proposed method is to enhance the rating profiles of the users who have low user reliability measure by adding a number of virtual ratings. To this end, the proposed user reputation model is used to predict the virtual ratings. In addition, the diversity, novelty and reliability measures of items are considered in the proposed rating profile enhancement mechanism. Therefore, the proposed method can improve the recommender systems about the cold start and data sparsity problems and also the diversity, novelty and reliability measures. Experimental results based on three real-world datasets show that the proposed method achieves higher performance than other recommendation methods.

scholarly journals Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

2021 ◽  
Vol 4 ◽  
Author(s):  
Zheni Zeng ◽  
Chaojun Xiao ◽  
Yuan Yao ◽  
Ruobing Xie ◽  
Zhiyuan Liu ◽  
...  
Keyword(s):  
Knowledge Transfer ◽  
Recommender Systems ◽  
Real World ◽  
Source Code ◽  
Cold Start ◽  
Future Research ◽  
Data Sparsity ◽  
Sparsity Problem

Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems. In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments. Finally, we discuss several promising directions for future research of recommender systems with pre-training. The source code of our experiments will be available to facilitate future research.

scholarly journals Tensor Decomposition for Spatial—Temporal Traffic Flow Prediction with Sparse Data

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6046
Author(s):  
Funing Yang ◽  
Guoliang Liu ◽  
Liping Huang ◽  
Cheng Siong Chin
Keyword(s):  
Traffic Flow ◽  
Prediction Method ◽  
Sparse Data ◽  
Time Of Day ◽  
Traffic Prediction ◽  
Traffic Surveillance ◽  
Data Sparsity ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Sparsity Problem

Urban transport traffic surveillance is of great importance for public traffic control and personal travel path planning. Effective and efficient traffic flow prediction is helpful to optimize these real applications. The main challenge of traffic flow prediction is the data sparsity problem, meaning that traffic flow on some roads or of certain periods cannot be monitored. This paper presents a transport traffic prediction method that leverages the spatial and temporal correlation of transportation traffic to tackle this problem. We first propose to model the traffic flow using a fourth-order tensor, which incorporates the location, the time of day, the day of the week, and the week of the month. Based on the constructed traffic flow tensor, we either propose a model to estimate the correlation in each dimension of the tensor. Furthermore, we utilize the gradient descent strategy to design a traffic flow prediction algorithm that is capable of tackling the data sparsity problem from the spatial and temporal perspectives of the traffic pattern. To validate the proposed traffic prediction method, case studies using real-work datasets are constructed, and the results demonstrate that the prediction accuracy of our proposed method outperforms the baselines. The accuracy decreases the least with the percentage of missing data increasing, including the situation of data being missing on neighboring roads in one or continuous multi-days. This certifies that the proposed prediction method can be utilized for sparse data-based transportation traffic surveillance.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

scholarly journals Regulating the new information intermediaries as gatekeepers of information diversity

Info ◽  
2015 ◽  
Vol 17 (6) ◽  
pp. 50-71 ◽  
Author(s):  
Natali Helberger ◽  
Katharina Kleinen-von Königslöw ◽  
Rob van der Noll
Keyword(s):  
Social Networks ◽  
Search Engines ◽  
Research Work ◽  
Societal Implications ◽  
Content Type ◽  
Media Diversity ◽  
Dynamic Relationship ◽  
Information Intermediaries ◽  
New Information ◽  
Desk Research

Purpose – The purposes of this paper are to deal with the questions: because search engines, social networks and app-stores are often referred to as gatekeepers to diverse information access, what is the evidence to substantiate these gatekeeper concerns, and to what extent are existing regulatory solutions to control gatekeeper control suitable at all to address new diversity concerns? It will also map the different gatekeeper concerns about media diversity as evidenced in existing research before the background of network gatekeeping theory critically analyses some of the currently discussed regulatory approaches and develops the contours of a more user-centric approach towards approaching gatekeeper control and media diversity. Design/methodology/approach – This is a conceptual research work based on desk research into the relevant and communications science, economic and legal academic literature and the relevant laws and public policy documents. Based on the existing evidence as well as on applying the insights from network gatekeeping theory, this paper then critically reviews the existing legal/policy discourse and identifies elements for an alternative approach. Findings – This paper finds that when looking at search engines, social networks and app stores, many concerns about the influence of the new information intermediaries on media diversity have not so much their source in the control over critical resources or access to information, as the traditional gatekeepers do. Instead, the real bottleneck is access to the user, and the way the relationship between social network, search engine or app platforms and users is given form. Based on this observation, the paper concludes that regulatory initiatives in this area would need to pay more attention to the dynamic relationship between gatekeeper and gated. Research limitations/implications – Because this is a conceptual piece based on desk-research, meaning that our assumptions and conclusions have not been validated by own empirical research. Also, although the authors have conducted to their best knowledge the literature review as broad and as concise as possible, seeing the breadth of the issue and the diversity of research outlets, it cannot be excluded that we have overlooked one or the other publication. Practical implications – This paper makes a number of very concrete suggestions of how to approach potential challenges from the new information intermediaries to media diversity. Social implications – The societal implications of search engines, social networks and app stores for media diversity cannot be overestimated. And yet, it is the position of users, and their exposure to diverse information that is often neglected in the current dialogue. By drawing attention to the dynamic relationship between gatekeeper and gated, this paper highlights the importance of this relationship for diverse exposure to information. Originality/value – While there is currently much discussion about the possible challenges from search engines, social networks and app-stores for media diversity, a comprehensive overview in the scholarly literature on the evidence that actually exists is still lacking. And while most of the regulatory solutions still depart from a more pre-networked, static understanding of “gatekeeper”, we develop our analysis on the basis for a more dynamic approach that takes into account the fluid and interactive relationship between the roles of “gatekeepers” and “gated”. Seen from this perspective, the regulatory solutions discussed so far appear in a very different light.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

2021 ◽  
Vol 14 (1) ◽  
pp. 387-399
Author(s):  
Noor Ifada ◽  
◽  
Richi Nayak ◽  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Approach ◽  
Tensor Model ◽  
Learning Approaches ◽  
Data Sparsity ◽  
Tensor Models ◽  
Sparsity Problem ◽  
Weighted Rank

The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.

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