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

Heterogeneous Data Fusion via Matrix Factorization for Augmenting Item, Group and Friend Recommendations

ABSTRACT Up to now, more and more social media sites have started to allow their users to build the social relationships. Take the Last.fm for example (which is a popular music-sharing site), users can not only add each other as friends, but also join interest groups that include people with common tastes. Therefore, in this environment, users might be interested in not only receiving item recommendations, but also friend recommendations whom they might consider putting in the contact list, and group recommendations that they may consider joining in. To support such needs, in this paper, we propose a generalized framework that provides three different types of recommendation in a single system: recommending items, recommending groups and recommending friends. For each type of recommendation, we in depth investigated the algorithm impact of fusing other two information resources (e.g., user-item preferences and friendship to be fused for recommending groups), along with their combined effect. The experiment reveals the ideal fusion mechanism for this multi-output recommender, and validates the benefit of factorization model for fusing bipartite data (such as membership and user-item preferences) and the benefit of regularization model for fusing one mode data (such as friendship). Moreover, the positive effect of integrating similarity measure into the regularization model is identified via the experiment

Robust Multivariate Autoregression for Anomaly Detection in Dynamic Product Ratings

ABSTRACT User provided rating data about products and services is one key feature of websites such as Amazon, TripAdvisor, or Yelp. Since these ratings are rather static but might change over time, a temporal analysis of rating distributions provides deeper insights into the evolution of a products' quality. Given a time-series of rating distributions, in this work, we answer the following questions: (1) How to detect the base behavior of users regarding a product's evaluation over time? (2) How to detect points in time where the rating distribution differs from this base behavior, e.g., due to attacks or spontaneous changes in the product's quality? To achieve these goals, we model the base behavior of users regarding a product as a latent multivariate autoregressive process. This latent behavior is mixed with a sparse anomaly signal finally leading to the observed data. We propose an efficient algorithm solving our objective and we present interesting findings on various real world datasets

HHMF: hidden hierarchical matrix factorization for recommender systems

Abstract(#br)Matrix factorization (MF) is one of the most powerful techniques used in recommender systems. MF models the (user, item) interactions behind historical explicit or implicit ratings. Standard MF does not capture the hierarchical structural correlations, such as publisher and advertiser in advertisement recommender systems, or the taxonomy (e.g., tracks, albums, artists, genres) in music recommender systems. There are a few hierarchical MF approaches, but they require the hierarchical structures to be known beforehand. In this paper, we propose a Hidden Hierarchical Matrix Factorization (HHMF) technique, which learns the hidden hierarchical structure from the user-item rating records. HHMF does not require the prior knowledge of hierarchical structure; hence, as opposed to..