scholarly journals Exploring IoT Location Information to Perform Point of Interest Recommendation Engine: Traveling to a New Geographical Region

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 992 ◽  
Author(s):  
Xu Yang ◽  
Billy Zimba ◽  
Tingting Qiao ◽  
Keyan Gao ◽  
Xiaoya Chen
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Real Data ◽  
User Preferences ◽  
Data Sets ◽  
Wireless Internet ◽  
User Reviews ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Geographical Regions

With the development of wireless Internet and the popularity of location sensors in mobile phones, the coupling degree between social networks and location sensor information is increasing. Many studies in the Location-Based Social Network (LBSN) domain have begun to use social media and location sensing information to implement personalized Points-of-interests (POI) recommendations. However, this approach may fall short when a user moves to a new district or city where they have little or no activity history and social network friend information. Thus, a need to reconsider how we model the factors influencing a user’s preferences in new geographical regions in order to make personalized and relevant recommendation. A POI in LBSNs is semantically enriched with annotations such as place categories, tags, tips or user reviews which implies knowledge about the nature of the place as well as a visiting person’s interests. This provides us with opportunities to better understand the patterns in users’ interests and activities by exploiting the annotations which will continue to be useful even when a user moves to unfamiliar places. In this research, we proposed a location-aware POI recommendation system that models user preferences mainly based on user reviews, which shows the nature of activities that a user finds interesting. Using this information from users’ location history, we predict user ratings by harnessing the information present in review text as well as consider social influence from similar user set formed based on matching category preferences and similar reviews. We use real data sets partitioned by city provided by Yelp, to compare the accuracy of our proposed method against some baseline POI recommendation algorithms. Experimental results show that our algorithm achieves a better accuracy.

scholarly journals Hybrid POI Recommendation System for Tourism

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Monishkanna Barathan ◽  
Ershad Sharifahmadian
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Interaction ◽  
User Preferences ◽  
Knowledge Based ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Information Finding ◽  
Hybrid Recommender ◽  
Available Information

Due to the increase in amount of available information, finding places and planning of the activities to be done during a tour can be strenuous. Tourists are looking for information about a place in which they have not been before, which worsen the selection of places that fit better with user’s preferences. Recommendation systems have been fundamentally applicable in tourism, suggest suitable places, and effectively prune large information from different locations, so tourists are directed toward those places where are matched with their needs and preferences. Several techniques have been studied for point-of-interest (POI) recommendation, including content-based which builds based on user preferences, collaborative filtering which exploits the behavior of other users, and different places, knowledge-based method, and several other techniques. These methods are vulnerable to some limitations and shortcomings related to recommendation environment such as scalability, sparsity, first-rater or gray sheep problems. This paper tries to identify the drawbacks that prevent wide spread use of these methodologies in recommendation. To improve performance of recommendation systems, these methods are combined to form hybrid recommenders. This paper proposes a novel hybrid recommender system which suggests tourism destinations to a user with minimal user interaction. Furthermore, we use sentiment analysis of user’s comments to enhance the efficiency of the proposed system.

scholarly journals Temporal Patterns Of User Preferences In The Tasks Of Forming Explanations In The Recommender System

2020 ◽  
Vol 2 (95) ◽  
pp. 21-27
Author(s):  
S. F. Chalyi ◽  
V. O. Leshchynskyi
Keyword(s):  
Recommendation System ◽  
Selection Process ◽  
Temporal Patterns ◽  
User Preferences ◽  
Data Sets ◽  
Goods And Services ◽  
Temporal Relationships ◽  
Sequential Selection ◽  
System User ◽  
Alternative Choice

The problem of taking into account changes in the user’s behavior of the recommendation system whenconstructing explanations for recommendations is considered. This problem occurs as a result of cyclical changes in userrequirements. Its solution is associated with the construction of an explanation comparing the alternative choices of theuser of the recommendation system. The developed models of temporal patterns consist of a set of temporal relationshipsbetween the events of users’ choice of goods and services. The first pattern contains an alternative in the form of sequential selection in time of several objects or the selection of only a pair - the first and the last object. The second pattern,sequential-alternative choice, consists of a sequence of choices over time, which ends with the first pattern. The proposedapproach to the formation of patterns is based on the construction of data sets containing temporal dependencies betweena group of user choices for a given level of time detail. The temporal dataset is used to construct a temporal graph of therecommender system user selection process. The latter includes a set of temporal patterns with an indication of the timeof their beginning and end, which makes it possible to determine the duration of the implementation of these patterns.On the basis of the patterns, subsets of temporal relationships are formed to build explanations for the recommendedlist of goods and services. Experimental verification of the developed approach using the “Online Retail” sales data sethas shown the possibility of identifying temporal patterns even on short initial samples.

scholarly journals Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhengyou Xia ◽  
Shengwu Xu ◽  
Ningzhong Liu ◽  
Zhengkang Zhao
Keyword(s):  
Bayesian Model ◽  
Recommendation System ◽  
Research Work ◽  
Joint Probability ◽  
Real Data ◽  
Online News ◽  
City Government ◽  
Data Sets ◽  
News Websites ◽  
News Recommendation

The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results.

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.

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 Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2673
Author(s):  
Chonghuan Xu ◽  
Dongsheng Liu ◽  
Xinyao Mei
Keyword(s):  
Estimation Method ◽  
Geographical Location ◽  
Heterogeneous Data ◽  
User Preferences ◽  
Data Sets ◽  
User Characteristics ◽  
Model Studies ◽  
Poi Recommendation ◽  
Temporal Factors ◽  
The Impact

The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-K POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.

scholarly journals MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation

2021 ◽  
Author(s):  
Huimin Sun ◽  
Jiajie Xu ◽  
Kai Zheng ◽  
Pengpeng Zhao ◽  
Pingfu Chao ◽  
...  
Keyword(s):  
State Of The Art ◽  
Cold Start ◽  
User Preferences ◽  
Location Based Services ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Meta Learning ◽  
Optimized Model ◽  
Real World Datasets ◽  
Cold Start Problem

Next Point-of-Interest (POI) recommendation is of great value for location-based services. Existing solutions mainly rely on extensive observed data and are brittle to users with few interactions. Unfortunately, the problem of few-shot next POI recommendation has not been well studied yet. In this paper, we propose a novel meta-optimized model MFNP, which can rapidly adapt to users with few check-in records. Towards the cold-start problem, it seamlessly integrates carefully designed user-specific and region-specific tasks in meta-learning, such that region-aware user preferences can be captured via a rational fusion of region-independent personal preferences and region-dependent crowd preferences. In modelling region-dependent crowd preferences, a cluster-based adaptive network is adopted to capture shared preferences from similar users for knowledge transfer. Experimental results on two real-world datasets show that our model outperforms the state-of-the-art methods on next POI recommendation for cold-start users.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

Using latent features for building an interpretable recommendation system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziming Zeng ◽  
Yu Shi ◽  
Lavinia Florentina Pieptea ◽  
Junhua Ding
Keyword(s):  
Recommendation System ◽  
User Preferences ◽  
User Preference ◽  
Data Sets ◽  
Historical Records ◽  
Data Link ◽  
Content Type ◽  
Fine Grained ◽  
Public Data ◽  
Latent Features

Purpose Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation. Design/methodology/approach First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system Findings Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system. Originality/value This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.

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