Topic-enhanced memory networks for personalised point-of-interest recommendation

Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry. Despite their value, however, they still suffer from the challenges of capturing complicated user preferences and fine-grained user-POI relationship for spatio-temporal sensitive POI recommendation. Existing recommendation algorithms, including both shallow and deep approaches, usually embed the visiting records of a user into a single latent vector to model user preferences: this has limited power of representation and interpretability. In this paper, we propose a novel topic-enhanced memory network (TEMN), a deep architecture to integrate the topic model and memory network capitalising on the strengths of both the global structure of latent patterns and local neighbourhood-based features in a nonlinear fashion. We further incorporate a geographical module to exploit user-specific spatial preference and POI-specific spatial influence to enhance recommendations. The proposed unified hybrid model is widely applicable to various POI recommendation scenarios. Extensive experiments on real-world WeChat datasets demonstrate its effectiveness (improvement ratio of 3.25% and 29.95% for context-aware and sequential recommendation, respectively). Also, qualitative analysis of the attention weights and topic modeling provides insight into the model's recommendation process and results.China Scholarship Council and Cambridge Trus

A study on labeling network hostile behavior with Intelligent Interactive tools

Labeling a real network dataset is specially expensive in computersecurity, as an expert has to ponder several factors before assigningeach label. This paper describes an interactive intelligent systemto support the task of identifying hostile behaviors in network logs.The RiskID application uses visualizations to graphically encodefeatures of network connections and promote visual comparison. Inthe background, two algorithms are used to actively organize con-nections and predict potential labels: a recommendation algorithmand a semi-supervised learning strategy. These algorithms togetherwith interactive adaptions to the user interface constitute a behaviorrecommendation. A study is carried out to analyze how the algo-rithms for recommendation and prediction influence the workflowof labeling a dataset. The results of a study with 16 participantsindicate that the behaviour recommendation significantly improvesthe quality of labels. Analyzing interaction patterns, we identify amore intuitive workflow used when behaviour recommendation isavailable.Fil: Guerra Torres, Jorge Luis. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Veas, Eduardo Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo; ArgentinaFil: Catania, Carlos Adrian. Universidad Nacional de Cuyo; Argentina2019 IEEE Symposium on Visualization for Cyber SecurityVancouverCanadáInstitute of Electrical and Electronics Engineer

Exploring heterogeneous social information networks for recommendation

University of Technology Sydney. Faculty of Engineering and Information Technology.A basic premise behind our study of heterogeneous social information networks for recommendation is that a complex network structure leads to a large volume of implicit but valuable information which can significantly enhance recommendation performance. In our work, we combine the global popularity and personalized features of travel destinations and also integrate temporal sensitive patterns to form spatial-temporal wise trajectory recommendation. We then develop a model to identify representative areas of interest (AOIs) for travellers based on a large scale dataset consisting of geo-tagged images and check-ins. In addition, we introduce active time frame analysis to determine the most suitable time to visit an AOI during the day. The outcome of this work can suggest relevant personalized travel recommendations to assist people who are arriving in new cities. Another important part of our research is to study how “local” and “global” social influences exert their impact on user preferences or purchasing decisions. We first simulate the social influence diffusion in the network to find the global and local influence nodes. We then embed these two different kinds of influence data, as regularization terms, into a traditional recommendation model to improve its accuracy. We find that “Community Stars” and “Web Celebrities”, represent “local” and “global” influence nodes respectively, a phenomenon which does exist and can help us to generate significantly better recommendation results. A central topic of our thesis is also to utilize a large heterogeneous social information network to identify the collective market hyping behaviours. Combating malicious user attacks is also a key task in the recommendation research field. In our study, we investigate the evolving spam strategies which can escape from most of the traditional detection methods. Based on the investigation of the advanced spam technique, we define three kinds of heterogeneous information networks to model the patterns in such spam activities and we then propose an unsupervised learning model which combines the three networks in an attempt to discover collective hyping activities. Overall, we utilize the heterogeneous social information network to enhance recommendation quality, not only by improving the user experience and recommendation accuracy, but also by ensuring that quality and genuine information is not overwhelmed by advanced hyping activities

Recommendation Networks and the Long Tail of Electronic Commerce

It has been conjectured that the peer-based recommendations associated with electronic commerce lead to a redistribution of demand from popular products or 'blockbusters' to less popular or 'niche' products, and that electronic markets will therefore be characterized by a 'long tail' of demand and revenue. In this paper, we develop a novel method to test this conjecture and we report on results contrasting the demand distributions of books in over 200 distinct categories on Amazon.com. Viewing each product as having a unique position in a hyperlinked network of recommendations between products that is analogous to shelf position in traditional commerce, we quantify the extent to which a product is in uenced by its recommendation network position by using a variant of Google's PageRank measure of centrality. We then associate the average level of network influence on each category with the inequality in the distribution of its demand and revenue, quantifying this inequality using the Gini coefficient derived from the category's Lorenz curve. We establish that categories whose products are influenced more by recommendations have significantly flatter demand distributions, even after controlling for variations in average category demand, the category's size and measures of price dispersion. Our empirical findings indicate that doubling the average influence of recommendations on a category is associated with an average increase in the relative demand for the least popular 20% of products by about 50%, and a average reduction in the relative demand for the most popular 20% by about 12%. We also show that this e¤ect is enhanced when there is assortative mixing in the recommendation network, and in categories whose products are more evenly influenced by recommendations. The direction of these results persist across time, across both demand and revenue distributions, and across both daily and weekly demand aggregations. Our work offers new ideas for assessing the influence of networks on demand and revenue patterns in electronic commerce, and provides new empirical evidence supporting the impact of visible recommendations on the long tail of electronic commerce

The Dynamics of Viral Marketing

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a 'long tail' where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product and pricing categories for which viral marketing seems to be very effective