scholarly journals Experimental Analysis of Friend-And-Native Based Location Awareness for Accurate Collaborative Filtering

2021 ◽  
Vol 11 (6) ◽  
pp. 2510
Author(s):  
Aaron Ling Chi Yi ◽  
Dae-Ki Kang
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Real World ◽  
Experimental Analysis ◽  
Location Awareness ◽  
Research Communities ◽  
Current Location ◽  
Real World Datasets ◽  
Filtering Approach

Location-based recommender systems have gained a lot of attention in both commercial domains and research communities where there are various approaches that have shown great potential for further studies. However, there has been little attention in previous research on location-based recommender systems for generating recommendations considering the locations of target users. Such recommender systems sometimes recommend places that are far from the target user’s current location. In this paper, we explore the issues of generating location recommendations for users who are traveling overseas by taking into account the user’s social influence and also the native or local expert’s knowledge. Accordingly, we have proposed a collaborative filtering recommendation framework called the Friend-And-Native-Aware Approach for Collaborative Filtering (FANA-CF), to generate reasonable location recommendations for users. We have validated our approach by systematic and extensive experiments using real-world datasets collected from Foursquare TM. By comparing algorithms such as the collaborative filtering approach (item-based collaborative filtering and user-based collaborative filtering) and the personalized mean approach, we have shown that our proposed approach has slightly outperformed the conventional collaborative filtering approach and personalized mean approach.

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 Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 19-26 ◽  
Author(s):  
Chong Chen ◽  
Min Zhang ◽  
Yongfeng Zhang ◽  
Weizhi Ma ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Model Parameters ◽  
Online Systems ◽  
Practical Applications ◽  
Real World Datasets ◽  
Primary Type

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural 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.

Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging Systems

2020 ◽  
Vol 31 (4) ◽  
pp. 24-45
Author(s):  
Mengmeng Shen ◽  
Jun Wang ◽  
Ou Liu ◽  
Haiying Wang
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Real World ◽  
Agglomerative Hierarchical Clustering ◽  
Collaborative Tagging ◽  
Excellent Performance ◽  
Recommendation Algorithm ◽  
Proposed Model ◽  
Target User ◽  
Real World Datasets

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

scholarly journals From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

2011 ◽  
Vol 2011 ◽  
pp. 1-19
Author(s):  
Armelle Brun ◽  
Sylvain Castagnos ◽  
Anne Boyer
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Community Detection ◽  
Local Community ◽  
State Of The Art ◽  
New Approach ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
The Web ◽  
Filtering Approach

The number of items that users can now access when navigating on the Web is so huge that these might feel lost. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that relies on the preferences of items expressed by users, usually under the form of ratings. In the absence of ratings, classical collaborative filtering techniques cannot be applied. Fortunately, the behavior of users, such as their consultations, can be collected. In this paper, we present a new approach to perform collaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentors of a given user. We adapt one state-of-the-art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted show that the precision achieved is higher then the baseline that does not perform any mentor selection. In addition, our model almost offsets the absence of ratings by exploiting a reduced set of mentors.

SOCIAL INTEREST FOR USER SELECTING ITEMS IN RECOMMENDER SYSTEMS

2013 ◽  
Vol 24 (04) ◽  
pp. 1350022 ◽  
Author(s):  
DA-CHENG NIE ◽  
MING-JING DING ◽  
YAN FU ◽  
JUN-LIN ZHOU ◽  
ZI-KE ZHANG
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Social Interest ◽  
Experimental Results ◽  
Simple Method ◽  
The Social ◽  
Social Interests ◽  
Similarity Computation ◽  
Real World Datasets

Recommender systems have developed rapidly and successfully. The system aims to help users find relevant items from a potentially overwhelming set of choices. However, most of the existing recommender algorithms focused on the traditional user-item similarity computation, other than incorporating the social interests into the recommender systems. As we know, each user has their own preference field, they may influence their friends' preference in their expert field when considering the social interest on their friends' item collecting. In order to model this social interest, in this paper, we proposed a simple method to compute users' social interest on the specific items in the recommender systems, and then integrate this social interest with similarity preference. The experimental results on two real-world datasets Epinions and Friendfeed show that this method can significantly improve not only the algorithmic precision-accuracy but also the diversity-accuracy.

scholarly journals Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data

2021 ◽  
Author(s):  
Yatong Sun ◽  
Bin Wang ◽  
Zhu Sun ◽  
Xiaochun Yang
Keyword(s):  
Recommender Systems ◽  
Gaussian Distribution ◽  
Real World ◽  
Sequential Pattern ◽  
High Loss ◽  
Real World Datasets

Most sequential recommender systems (SRSs) predict next-item as target for each user given its preceding items as input, assuming that each input is related to its target. However, users may unintentionally click on items that are inconsistent with their preference. We empirically verify that SRSs can be misguided with such unreliable instances (i.e. targets mismatch inputs). This inspires us to design a novel SRS By Eliminating unReliable Data (BERD) guided with two observations: (1) unreliable instances generally have high training loss; and (2) high-loss instances are not necessarily unreliable but uncertain ones caused by blurry sequential pattern. Accordingly, BERD models both loss and uncertainty of each instance via a Gaussian distribution to better distinguish unreliable instances; meanwhile an uncertainty-aware graph convolution network is exploited to assist in mining unreliable instances by lowering uncertainty. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed BERD.

EXPERIMENTAL ANALYSIS OF DESIGN CHOICES IN MULTIATTRIBUTE UTILITY COLLABORATIVE FILTERING

2007 ◽  
Vol 21 (02) ◽  
pp. 311-331 ◽  
Author(s):  
NIKOS MANOUSELIS ◽  
CONSTANTINA COSTOPOULOU
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Experimental Analysis ◽  
Real Life ◽  
Multiattribute Utility ◽  
Numerous Application ◽  
Data Set ◽  
Recommendation Algorithms ◽  
Actual Operation ◽  
Design Options

Recommender systems have already been engaging multiple criteria for the production of recommendations. Such systems, referred to as multicriteria recommenders, demonstrated early the potential of applying Multi-Criteria Decision Making (MCDM) methods to facilitate recommendation in numerous application domains. On the other hand, systematic implementation and testing of multicriteria recommender systems in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined the importance of carrying out careful testing and parameterization of a recommender system, before it is actually deployed in a real setting. In this paper, the experimental analysis of several design options for three proposed multiattribute utility collaborative filtering algorithms is presented for a particular application context (recommendation of e-markets to online customers), under conditions similar to the ones expected during actual operation. The results of this study indicate that the performance of recommendation algorithms depends on the characteristics of the application context, as these are reflected on the properties of evaluations' data set. Therefore, it is judged important to experimentally analyze various design choices for multicriteria recommender systems, before their actual deployment.

Sign in / Sign up

Export Citation Format

Share Document