scholarly journals Estratégias para Aprimorar a Diversidade Categórica e Geográfica de Sistemas de Recomendação de POIs

2020 ◽  
Author(s):  
Rodrigo Carvalho ◽  
Leonardo Rocha
Keyword(s):  
Social Networks ◽  
User Satisfaction ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Data Sets ◽  
Processing Strategy ◽  
Post Processing ◽  
Practical Utility ◽  
Points Of Interest ◽  
Location Based Social Networks

Currently, so-called Recommendation Systems (SRs) have been used to assist users in discovering relevant Points of Interest (POIs) on Location-Based Social Networks (LBSN), such as FourSquare and Yelp. Given the main challenges of data-sparse and geographic influence in this scenario, most of the work on POI recommendations has focused only on improving the effectiveness (i.e. accuracy) of the systems. However, there is a growing consensus that just effectiveness is not sufficient to assess the practical utility of these systems. In real scenarios, categorical and geographic diversities were identified as the main complementary dimensions for assessing user satisfaction and the usefulness of recommendations. The works in the literature are concentrated on only one of these concepts. In this work, we propose a new post-processing strategy, which combines these concepts in order to improve the user’s interest in POIs. Our experimental results in the Yelp data sets show that our strategy can improve user satisfaction, considering different SRs and multiple diversification metrics. Our method is capable of improving diversity by up to 120 % without significant losses in terms of effectiveness.

Fine-Grained User Location Prediction using Meta-Path Context with Attention Mechanism

2021 ◽  
Author(s):  
Zhixiao Wang ◽  
Wenyao Yan ◽  
Ang Gao
Keyword(s):  
Social Networks ◽  
Positive Sample ◽  
Data Sets ◽  
Suggested Approach ◽  
Heterogeneous Information ◽  
Fine Grained ◽  
High Efficient ◽  
Meta Path ◽  
Location Based Social Networks ◽  
User Location

The prevalence of Location-Based Social Networks (LBSNs) significantly improves the location-aware capability of services by providing Geo-tagged information. Relied on a great number of user check-in data in the location-based social networks, their essential mobility modes are able to be comprehensively studied, which is basic for forecasting the next venue where a specific user is going to visit considering his relevant historical check-in data. Since there exist different kinds of nodes and interactions between nodes, these information could be look upon as a network that is made up of heterogeneous information. In this network a few of different semantic meta paths could be obtained. Enlightened from the competitive advantage of embedding method relied upon meta-path contexts in the heterogeneous information network, we study a joint deep learning scheme exploring different meta-path context information to forecast fine-grained location. In order to capture different semantics in a user-location interaction, we adopt a simple but high-efficient attention method to learn the meta-path importance or weights. In the terms of model optimization, considering we have only positive sample data and there exists intrinsically latent feedback in check-in information, herein a pairwise learning method is utilized for maximizing the margin between visited and invisible venues. Experiment in different data-sets validate the competitive performance of the suggested approach under different assessment criterion.

scholarly journals A Study on Recommendation Systems in Location Based Social Networking

2017 ◽  
Vol 41 (2) ◽  
pp. 213-229
Author(s):  
Lakshmi Shree Kullappa ◽  
Rajeshwari Kullappa
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Recommendation Systems ◽  
Smart Devices ◽  
Media Content ◽  
Location Data ◽  
The Social ◽  
Comprehensive Survey ◽  
Location Based Social Networks ◽  
Meaningful Relationships

Smart devices in the hands of people are revolutionizing the social lifestyle of one's self. Everyone across the world are using smart devices linked to their social networking activities one such activity is to share location data by uploading the tagged media content like photos, videos. The data is of surroundings, events attended/attending and travel experiences. Users share their experiences at a given location through localization techniques.  Using such data from social networks an attempt is made to analyse tagged media content to acquire information on user context, individual’s interests, tastes, behaviours and derive meaningful relationships amongst them are referred to as Location Based Social Networks (LBSNs). The resulting information can be used to market a product and to improve business, as well recommend a travel and plan an itinerary. This paper presents a comprehensive survey of recommended systems for LBSNs covering the concepts of LBSNs, terminologies of LBSN and various recommendation systems.

scholarly journals Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation

2018 ◽  
Author(s):  
Hao Wang ◽  
Huawei Shen ◽  
Wentao Ouyang ◽  
Xueqi Cheng
Keyword(s):  
Social Networks ◽  
Real World ◽  
Fundamental Problem ◽  
Physical Distance ◽  
Experimental Results ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Movie Recommendation ◽  
Real World Datasets ◽  
Location Based Social Networks

Point-of-interest (POI) recommendation, i.e., recommending unvisited POIs for users, is a fundamental problem for location-based social networks. POI recommendation distinguishes itself from traditional item recommendation, e.g., movie recommendation, via geographical influence among POIs. Existing methods model the geographical influence between two POIs as the probability or propensity that the two POIs are co-visited by the same user given their physical distance. These methods assume that geographical influence between POIs is determined by their physical distance, failing to capture the asymmetry of geographical influence and the high variation of geographical influence across POIs. In this paper, we exploit POI-specific geographical influence to improve POI recommendation. We model the geographical influence between two POIs using three factors: the geo-influence of POI, the geo-susceptibility of POI, and their physical distance. Geo-influence captures POI?s capacity at exerting geographical influence to other POIs, and geo-susceptibility reflects POI?s propensity of being geographically influenced by other POIs. Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation.

Social-Spatial Group Queries with Keywords

2022 ◽  
Vol 8 (1) ◽  
pp. 1-32
Author(s):  
Sajid Hasan Apon ◽  
Mohammed Eunus Ali ◽  
Bishwamittra Ghosh ◽  
Timos Sellis
Keyword(s):  
Social Networks ◽  
Social Groups ◽  
Real Life ◽  
Real Data ◽  
Data Sets ◽  
Interesting Insight ◽  
Points Of Interest ◽  
The Social ◽  
Textual Data ◽  
Aware Group

Social networks with location enabling technologies, also known as geo-social networks, allow users to share their location-specific activities and preferences through check-ins. A user in such a geo-social network can be attributed to an associated location (spatial), her preferences as keywords (textual), and the connectivity (social) with her friends. The fusion of social, spatial, and textual data of a large number of users in these networks provide an interesting insight for finding meaningful geo-social groups of users supporting many real-life applications, including activity planning and recommendation systems. In this article, we introduce a novel query, namely, Top- k Flexible Socio-Spatial Keyword-aware Group Query (SSKGQ), which finds the best k groups of varying sizes around different points of interest (POIs), where the groups are ranked based on the social and textual cohesiveness among members and spatial closeness with the corresponding POI and the number of members in the group. We develop an efficient approach to solve the SSKGQ problem based on our theoretical upper bounds on distance, social connectivity, and textual similarity. We prove that the SSKGQ problem is NP-Hard and provide an approximate solution based on our derived relaxed bounds, which run much faster than the exact approach by sacrificing the group quality slightly. Our extensive experiments on real data sets show the effectiveness of our approaches in different real-life settings.

scholarly journals An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks

2019 ◽  
Vol 8 (11) ◽  
pp. 487 ◽  
Author(s):  
Zou ◽  
He ◽  
Zhu
Keyword(s):  
New York ◽  
Social Networks ◽  
Semantic Information ◽  
Automatic Annotation ◽  
Baseline Method ◽  
Points Of Interest ◽  
The Social ◽  
Rich Information ◽  
Location Based Social Networks ◽  
Geographical Locations

Location-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides benefits for location recommendations. However, current methods still have some limitations, such as a long annotating time and a low annotating accuracy. In this study, we develop a hybrid method to annotate POIs with meaningful information from LBSNs. The method integrates three patterns: temporal, spatial, and text patterns. Firstly, we present an approach for preprocessing data based on temporal patterns. Secondly, we describe a way to discover POIs through spatial patterns. Thirdly, we build a keyword dictionary for discovering the categories of POIs to be annotated via mining the text patterns. Finally, we integrate these three patterns to label each POI. Taking New York and London as the target areas, we accomplish automatic POI annotation by using Precision, Recall, and F-values to evaluate the effectiveness. The results show that our F-value is 78%, which is superior to that of the baseline method (Falcone's method) at 73% and this suggests that our method is effective in extracting POIs and assigning them categories.

scholarly journals An Effective Hotel Recommendation System through Processing Heterogeneous Data

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1920
Author(s):  
Md. Shafiul Alam Forhad ◽  
Mohammad Shamsul Arefin ◽  
A. S. M. Kayes ◽  
Khandakar Ahmed ◽  
Mohammad Jabed Morshed Chowdhury ◽  
...  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Heterogeneous Data ◽  
Experimental Results ◽  
User Reviews ◽  
Customer Reviews ◽  
Points Of Interest ◽  
Hotel Booking

Recommendation systems have recently gained a lot of popularity in various industries such as entertainment and tourism. They can act as filters of information by providing relevant suggestions to the users through processing heterogeneous data from different networks. Many travelers and tourists routinely rely on textual reviews, numerical ratings, and points of interest to select hotels in cities worldwide. To attract more customers, online hotel booking systems typically rank their hotels based on the recommendations from their customers. In this paper, we present a framework that can rank hotels by analyzing hotels’ customer reviews and nearby amenities. In addition, a framework is presented that combines the scores generated from user reviews and surrounding facilities. We perform experiments using datasets from online hotel booking platforms such as TripAdvisor and Booking to evaluate the effectiveness and applicability of the proposed framework. We first store the keywords extracted from reviews and assign weights to each considered unigram and bigram keywords and, then, we give a numerical score to each considered keyword. Finally, our proposed system aggregates the scores generated from the reviews and surrounding environments from different categories of the facilities. Experimental results confirm the effectiveness of the proposed recommendation framework.

scholarly journals An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation Systems ◽  
Network Services ◽  
The Past ◽  
User Behaviors ◽  
User Influence ◽  
User Activity ◽  
Information Recommendation ◽  
Social Network Services

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.

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