Point-of-Interest (POI) recommendation is one of the important services of location-based social networks (LBSNs), which has become an important way to help users discover interesting places and increase the potential income of related companies. Although human movement presents a sequential pattern in the LBSN. There still are the following problems: (1) when modeling the sequence data, most of the existing works assume that the check-in time depends on the location transformation in the location sequence. In particular, these works emphasize the equivalent transition probabilities between locations for all users to capture the check-in sequential pattern, whereas they ignore the spatial and temporal information of personalized context in some actual personal check-in scenarios; (2) most of the existing POI recommendation algorithms fail to utilize the social information related to modeling users to improve the final recommendation performance.To tackle the above challenges, we propose a new personalized successive POI recommendation model called Spatiotemporal Sequential and Social Embedding Rank model, named SSSER. First, we use a hybrid deep learning model based on the convolution filter and multilayer perceptron model to mine the sequence pattern among the users’ checked-in locations. Then, we use the method of metric learning to model the social relationship among users. Finally, we propose a unified framework to recommend POIs combining the users’ personal interests, the check-in sequential influence and social information simultaneously for the successive POI recommendation. And the BPR standard is used to optimize the loss function to fit the user’s partial order of POIs. The experimental results on the real datasets show that our proposed POI recommendation algorithm outperforms the other state-of-the-art POI recommendation algorithms.

Introduction

With the rapid development of Web2.0, wireless communication and location collection technology have promoted many location-based social networks (LBSNs), such as Foursquare, Yelp, Facebook and so on. Via these LBSNs, users can establish social connections with other users, explore the surrounding environment, and share their life experiences by checking in points-of-interest (POIs) such as restaurants, shopping centers, and tourist attractions. In addition to providing an interactive platform for users, LBSN contains rich data (check-in data, social relationships, comment information, etc.), which can be applied to predict users’ preferences and recommend some unvisited POIs that may be of interest to users. The recommendation, by means of LBSN, of a geographical location that a user may be interested in is referred to as the recommendation of POI. The POI recommendation, on the one hand, satisfies the individualized needs of users to explore new geographical areas and discover new POIs and at the same time alleviates the problem of information overload faced by users. On the other hand, the recommendation of POI helps LBSN service providers to play a pivotal role in realizing intelligent location services. Therefore, how to provide users with accurate POI recommendation has drawn the attention of more and more researchers in recent years [1].

Introduction

The Web allows people accessing to a huge amount of information. However, the users skills to cope with all the available information are limited, which leads to select suboptimal alternatives. This problem is known as information overload. Recommender Systems (RSs) are tools to help individuals to overcome such information overload problem personalizing access to information (Adomavicius & Tuzhilin, 2005; Ekstrand, Riedl, & Konstan, 2011). However, some items tend to be consumed by groups of users, such as tourist attractions (Garcia, Pajares, Sebastia, & Onaindia, 2012) or television programmes (Said, Berkovsky, & De Luca, 2011). With this purpose in mind, Group Recommender Systems (GRSs) (Masthoff, 2015) help groups of users to find suitable items according to their preferences and needs. Several techniques have been used to improve individual recommendation, such as neighborhood-based collaborative filtering (Sarwar, Karypis, Konstan, & Riedl, 2001), matrix factorisation (Koren, Bell, & Volinsky, 2009), or approaches that consider temporal dynamics (Koren, 2010; Rafailidis, Kefalas, & Manolopoulos, 2017). In the case of group recommendation, there are approaches to aggregate individual information (Masthoff, 2015), to consider consensus among members (Castro, Quesada, Palomares, & Martínez, 2015), or matrix factorisation models for groups (Ortega, Hernando, Bobadilla, & Kang, 2016). A decade ago, it was pointed out that explicitly stated user preferences may not be error free (O’Mahony, Hurley, & Silvestre, 2006). More recently, other recent works (Bellogín, Said, & de Vries, 2014; Centeno, Hermoso, & Fasli, 2015; Guo & Dunson, 2015; Zhang, Zhao, & Lui, 2017) have also pointed out that a person’s ratings are noisy, inconsistent, and biased. Li, Chen, Zhu, and Zhang (2013) determined that too many noisy ratings can distort users’ preference profiles, which result in unlike-minded neighbors that imply a quality loss in recommendations. Kluver, Nguyen, Ekstrand, Sen, and Riedl (2012) have also suggested that user ratings are imperfect and noisy, and such noise limits the predictive power of any RS. Therefore, in addition to improving recommendations through new recommendation approaches, researchers should also focus on improving the quality of the rating database (Amatriain, Pujol, Tintarev, & Oliver, 2009c). In RSs, there are two kinds of noise in the database (O’Mahony et al., 2006): (i) malicious noise, that consists of erroneous data deliberately inserted in the system to influence recommendations, and (ii) natural noise, that appears when users unpurposely introduce erroneous data due to human errors or external factors during the rating process. This paper focuses on the latter. Natural noise biases recommendations, therefore, its management is a key factor to improve them. There are several Natural Noise Management (NNM) approaches for individual RSs databases. While some NNM approaches need additional information (Amatriain, Lathia, Pujol, Kwak, & Oliver, 2009a; Pham & Jung, 2013), others detect and correct the natural noise using information already contained in the database (Yera, Castro, & Martínez, 2016; Yera Toledo, Caballero Mota, & Martínez, 2015). GRSs also rely on databases with explicit users’ preferences (Masthoff, 2015), therefore, they are affected by natural noise. Castro, Yera, and Martínez (2017) propose a NNM approach for GRSs to manage ratings and noise using crisp values. This is the only work focused on NNM in GRSs.

Abstract

Within the emergence of social web (web 2.0), new platform in technology named social networks, brought in to being. Social networks (SN) become more crowded and their rapidly growth caused scientists to search for methods analyzing the data which is implicated in social networks. Social network analysis with special attention to SN’s graph is a method that helps data extraction. These data could be used in targeted advertisements (Ad) which could impress users more. In the field of e-advertisements, presenting ads and sales are combined together using hypertexts or hypermedia to the nearest retailer or e-shops. So, targeted advertisement could be mentioned as an effective solution in the field of marketing on the web. Scientists have been focused on various variables and features that could be considered to target users in an appropriate way. While mentioning them, some new features are added. In this article, a framework has been proposed which facilitate targeted advertisements in social networks’ platform; using social networks information, previous advertisements and their status to have more precise information for recommender systems. Recommender system is used as a tool to target each user according to its preferences and interests. The main goal is to show the most effective advertisements in sidebar and attract users to share word of mouth (WOM) advertisements with each other. Considering user’s type through their activity in a social network and omitting repetitive advertisements ease our aim.

Introduction

Marketing is listed as a primary activity in Michel porter’s value chain model and it has a critical role in each business. Lots of researches have been performed for finding suitable methods of targeted advertising and making the most impression possible on people. This paper’s main goal is not only to fascinate customers, but also to make an improvement in the field of advertising. Reaching this aim would result in paying attention to new media such as social networks. Analyzing social networks would help to have exact information from any user through its profile, users’ interactions with others and generally their behavior. Social Networks (SN) users are considered as potential customers and having exact and precise information about each user, would guide us to recommend useful goods or services to them according to their own interests and tastes. This is a good solution to overcome data’s redundancies, which is the problem of information century.