Recommender Systems for Online and Mobile Social Networks: A survey

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area

An efficient approach to generating location-sensitive recommendations in ad-hoc social network environments

Social recommendation has been popular and successful in various urban sustainable applications such as online sharing, products recommendation and shopping services. These applications allow users to form several implicit social networks through their daily social interactions. The users in such social networks can rate some interesting items and give comments. The majority of the existing studies have investigated the rating prediction and recommendation of items based on user-item bipartite graph and user-user social graph, so called social recommendation. However, the spatial factor was not considered in their recommendation mechanisms. With the rapid development of the service of location-based social networks, the spatial information gradually affects the quality and correlation of rating and recommendation of items. This paper proposes spatial social union (SSU), an approach of similarity measurement between two users that integrates the interconnection among users, items and locations. The SSU-aware location-sensitive recommendation algorithm is then devised. We evaluate and compare the proposed approach with the existing rating prediction and item recommendation algorithms subject to a real-life data set. Experimental results show that the proposed SSU-aware recommendation algorithm is more effective in recommending items with the better consideration of user's preference and location.This work was supported by the National Natural Science Foundation of China under Grant 61372187. G. Min’s work was partly supported by the EU FP7 CLIMBER project under Grant Agreement No. PIRSES-GA-2012-318939. L. T. Yang is the corresponding author

CoSoLoRec: Joint factor model with content, social, location for heterogeneous point-of-interest recommendation

© Springer International Publishing AG 2016. The pervasive use of Location-based Social Networks calls for more precise Point-of-Interest recommendation. The probability of a user’s visit to a target place is influenced by multiple factors. Though there are several fusion models in such fields, heterogeneous information are not considered comprehensively. To this end, we propose a novel probabilistic latent factor model by jointly considering the social correlation, geographical influence and users’ preference. To be specific, a variant of Latent Dirichlet Allocation is leveraged to extract the topics of both user and POI from reviews which is denoted as explicit interest. Then, Probabilistic Latent Factor Model is introduced to depict the implicit interest. Moreover, Kernel Density Estimation and friend-based Collaborative Filtering are leveraged to model user’s geographic allocation and social correlation respectively. Thus, we propose CoSoLoRec, a fusion framework, to ameliorate the recommendation. Experiments on two real-word datasets show the superiority of our approach over the state-of-the-art methods

Top-N Recommendation on Graphs

Recommender systems play an increasingly important role in online applications to help users find what they need or prefer. Collaborative filtering algorithms that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. To alleviate this problem, this paper proposes a simple recommendation algorithm that fully exploits the similarity information among users and items and intrinsic structural information of the user-item matrix. The proposed method constructs a new representation which preserves affinity and structure information in the user-item rating matrix and then performs recommendation task. To capture proximity information about users and items, two graphs are constructed. Manifold learning idea is used to constrain the new representation to be smooth on these graphs, so as to enforce users and item proximities. Our model is formulated as a convex optimization problem, for which we need to solve the well-known Sylvester equation only. We carry out extensive empirical evaluations on six benchmark datasets to show the effectiveness of this approach.Comment: CIKM 201

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502