Towards Privacy Compliant and Anytime Recommender Systems

The original publication is available at www.springerlink.comInternational audienceRecommendation technologies have traditionally been used in domains such as E-commerce and Web navigation to recommend resources to customers so as to help them to get the pertinent resources. Among the possible approaches is collaborative filtering that does not take into account the content of the resources: only the traces of usage of the resources are considered. State of the art models, such as sequential association-rules and Markov models, that can be used in the frame of privacy concerns, are usually studied in terms of performance, state space complexity and time complexity. Many of them have a large time complexity and require a long time to compute recommendations. However, there are domains of application of the models where recommendations may be required quickly. This paper focuses on the study of how these state of the art models can be adapted so as to be anytime. In that case recommendations can be proposed to the user whatever is the computation time available, the quality of the recommendations increases according to the computation time. We show that such models can be adapted so as to be anytime and we propose several strategies to compute recommendations iteratively. We also show that the computation time needed by these new models is not increased compared to classical ones; even so, it sometimes decreases

Analysis of Users' Behavior in Structured e-Commerce Websites

Online shopping is becoming more and more common in our daily lives. Understanding users'' interests and behavior is essential to adapt e-commerce websites to customers'' requirements. The information about users'' behavior is stored in the Web server logs. The analysis of such information has focused on applying data mining techniques, where a rather static characterization is used to model users'' behavior, and the sequence of the actions performed by them is not usually considered. Therefore, incorporating a view of the process followed by users during a session can be of great interest to identify more complex behavioral patterns. To address this issue, this paper proposes a linear-temporal logic model checking approach for the analysis of structured e-commerce Web logs. By defining a common way of mapping log records according to the e-commerce structure, Web logs can be easily converted into event logs where the behavior of users is captured. Then, different predefined queries can be performed to identify different behavioral patterns that consider the different actions performed by a user during a session. Finally, the usefulness of the proposed approach has been studied by applying it to a real case study of a Spanish e-commerce website. The results have identified interesting findings that have made possible to propose some improvements in the website design with the aim of increasing its efficiency

대규모 TV 시청로그 클러스터링을 통한 시청행위 및 시청가구 유형 분석 연구

학위논문 (석사)-- 서울대학교 융합과학기술대학원 융합과학부, 2017. 8. 서봉원.최근에는 더 이상 과거처럼 같은 시간에 온 가족이 모여 앉아 소위 본방사수를 하는 행동만으로는 TV 시청을 이해할 수 없을 만큼 TV 시청 행태가 매우 복잡해졌다. 다양한 매체와 콘텐츠 공급 서비스들과 상호작용하며 서로 얽히고설킨 복잡한 시청 행동을 보이고 있는 것이다. TV 시청 환경은 콘텐츠 플랫폼 및 디바이스 환경 변화로 인해 과거와 달리 훨씬 예측하기 어려운 복잡한 환경으로 변모하게 되었다. TV를 둘러싼 환경이 더욱 복잡해지는 상황에서도 TV 시청에 대한 이해는 여전히 중요하게 여겨진다. N-스크린 시청 환경이 보편화되면서 TV에 대한 비중이 하락하고는 있으나, 아직까지는 TV 시청에 많은 시간을 보내고 있고 일상 생활에서의 중요도도 높은 만큼, TV는 여전히 콘텐츠 소비에 있어 중요한 역할을 하고 있기 때문이다. 달라진 환경 속에서도 TV 시청은 여전히 건재한 여가 활동이라 할 수 있으며, 복잡한 환경 속에서 달라진 TV 시청자와 시청 행태에 대한 이해가 더욱 필요한 상황이라는 점을 시사한다. 본 연구에서는 행동을 중심으로 TV 시청을 이해하고자 했던 기존 연구의 확장성 등의 한계점을 극복하고 궁극적으로는 전통적 관점에서 벗어나 다변화된 TV 시청 환경에서의 TV 시청에 대한 이해의 폭을 넓히고자, 디지털 케이블 TV 셋톱박스 로그를 통해 획득한 대규모 TV 시청 로그를 바탕으로 TV 시청 패턴을 행동 중심으로 유형화하고, 이를 다시 사용자 중심으로 조합하여 해석하는 프레임워크를 제시하였다. 이를 위해 기존의 웹 사용 마이닝 분야에서 사용되었던 세션 클러스터링 기반 유형화 분석 기법을 TV 시청 로그에 적용하였다. 또한 유형화 된 시청 행동과 서비스 해지율 간의 상관관계를 살펴봄으로써 본 연구의 접근 방식이 유효함을 입증하고자 하였다. 제안된 분석 프레임워크를 통해, 본 연구에서는 총 7개의 시청 행동 유형과 이를 통해 조합된 8개의 시청 가구 유형을 도출하였다. 또한 각 시청 가구 유형 그룹 내의 서비스 해지율과 시청 행동 유형 구성비 간의 상관관계 도출을 통해, 본 연구에서 도출한 시청 행동 유형이 서비스 해지를 의미 있게 설명할 수 있다는 것을 확인할 수 있었다. 본 연구를 통해 제안된 분석 프레임워크를 활용하여 행동을 기반으로 시청 패턴을 분석함으로써, 기존의 거시적 맥락에서 이루어진 선행 연구를 확장하여 현재 미디어 환경에서의 TV 시청 행위에 대해 더욱 풍부한 이해를 도울 수 있을 것으로 기대한다.제 1 장 서 론 1 제 1 절 연구의 배경 1 제 2 절 연구의 목표 10 제 2 장 선행연구 13 제 1 절 이론적 배경 13 제 2 절 기술적 배경 24 제 3 장 연구 문제 30 제 1 절 연구 문제 30 제 4 장 연구 방법 33 제 1 절 데이터 개요 및 전처리 34 제 2 절 세션 유형화 38 제 3 절 시청 가구 유형화 46 제 4 절 세션 유형과 서비스 해지의 연관성 48 제 5 장 연구 결과 49 제 1 절 세션 유형 49 제 2 절 시청 가구 유형 60 제 3 절 세션 유형과 서비스 해지의 연관성 77 제 6 장 결 론 85 제 1 절 연구 요약 85 제 2 절 연구의 시사점 87 제 3 절 연구의 한계 90 참고문헌 92Maste

Understanding, Analyzing and Predicting Online User Behavior

abstract: Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources.Dissertation/ThesisDoctoral Dissertation Business Administration 201

Adaptive Resource Management Schemes for Web Services

Web cluster systems provide cost-effective solutions when scalable and reliable web services are required. However, as the number of servers in web cluster systems increase, web cluster systems incur long and unpredictable delays to manage servers. This study presents the efficient management schemes for web cluster systems. First of all, we propose an efficient request distribution scheme in web cluster systems. Distributor-based systems forward user requests to a balanced set of waiting servers in complete transparency to the users. The policy employed in forwarding requests from the frontend distributor to the backend servers plays an important role in the overall system performance. In this study, we present a proactive request distribution (ProRD) to provide an intelligent distribution at the distributor. Second, we propose the heuristic memory management schemes through a web prefetching scheme. For this study, we design a Double Prediction-by-Partial-Match Scheme (DPS) that can be adapted to the modern web frameworks. In addition, we present an Adaptive Rate Controller (ARC) to determine the prefetch rate depending on the memory status dynamically. For evaluating the prefetch gain in a server node, we implement an Apache module. Lastly, we design an adaptive web streaming system in wireless networks. The rapid growth of new wireless and mobile devices accessing the internet has contributed to a whole new level of heterogeneity in web streaming systems. Particularly, in-home networks have also increased in heterogeneity by using various devices such as laptops, cell phone and PDAs. In our study, a set-top box(STB) is the access pointer between the internet and a home network. We design an ActiveSTB which has a capability of buffering and quality adaptation based on the estimation for the available bandwidth in the wireless LAN

Mining Significant Usage Patterns from Clickstream Data

Abstract. Discovery of usage patterns from Web data is one of the primary purposes for Web Usage Mining. In this paper, a technique to generate Significant Usage Patterns (SUP) is proposed and used to acquire significant “user preferred navigational trails”. The technique uses pipelined processing phases including sub-abstraction of sessionized Web clickstreams, clustering of the abstracted Web sessions, concept-based abstraction of the clustered sessions, and SUP generation. Using this technique, valuable customer behavior information can be extracted by Web site practitioners. Experiments conducted using Web log data provided by J.C.Penney demonstrate that SUPs of different types of customers are distinguishable and interpretable. This technique is particularly suited for analysis of dynamic websites.

Combating Attacks and Abuse in Large Online Communities

Internet users today are connected more widely and ubiquitously than ever before. As a result, various online communities are formed, ranging from online social networks (Facebook, Twitter), to mobile communities (Foursquare, Waze), to content/interests based networks (Wikipedia, Yelp, Quora). While users are benefiting from the ease of access to information and social interactions, there is a growing concern for users' security and privacy against various attacks such as spam, phishing, malware infection and identity theft. Combating attacks and abuse in online communities is challenging. First, today’s online communities are increasingly dependent on users and user-generated content. Securing online systems demands a deep understanding of the complex and often unpredictable human behaviors. Second, online communities can easily have millions or even billions of users, which requires the corresponding security mechanisms to be highly scalable. Finally, cybercriminals are constantly evolving to launch new types of attacks. This further demands high robustness of security defenses. In this thesis, we take concrete steps towards measuring, understanding, and defending against attacks and abuse in online communities. We begin with a series of empirical measurements to understand user behaviors in different online services and the uniquesecurity and privacy challenges that users are facing with. This effort covers a broad set of popular online services including social networks for question and answering (Quora), anonymous social networks (Whisper), and crowdsourced mobile communities (Waze). Despite the differences of specific online communities, our study provides a first look at their user activity patterns based on empirical data, and reveals the need for reliable mechanisms to curate user content, protect privacy, and defend against emerging attacks. Next, we turn our attention to attacks targeting online communities, with focus on spam campaigns. While traditional spam is mostly generated by automated software, attackers today start to introduce "human intelligence" to implement attacks. This is maliciouscrowdsourcing (or crowdturfing) where a large group of real-users are organized to carry out malicious campaigns, such as writing fake reviews or spreading rumors on social media. Using collective human efforts, attackers can easily bypass many existing defenses (e.g.,CAPTCHA). To understand the ecosystem of crowdturfing, we first use measurements to examine their detailed campaign organization, workers and revenue. Based on insights from empirical data, we develop effective machine learning classifiers to detect crowdturfingactivities. In the meantime, considering the adversarial nature of crowdturfing, we also build practical adversarial models to simulate how attackers can evade or disrupt machine learning based defenses. To aid in this effort, we next explore using user behavior models to detect a wider range of attacks. Instead of making assumptions about attacker behavior, our idea is to model normal user behaviors and capture (malicious) behaviors that are deviated from norm. In this way, we can detect previously unknown attacks. Our behavior model is based on detailed clickstream data, which are sequences of click events generated by users when using the service. We build a similarity graph where each user is a node and the edges are weightedby clickstream similarity. By partitioning this graph, we obtain "clusters" of users with similar behaviors. We then use a small set of known good users to "color" these clusters to differentiate the malicious ones. This technique has been adopted by real-world social networks (Renren and LinkedIn), and already detected unexpected attacks. Finally, we extend clickstream model to understanding more-grained behaviors of attackers (and real users), and tracking how user behavior changes over time. In summary, this thesis illustrates a data-driven approach to understanding and defending against attacks and abuse in online communities. Our measurements have revealed new insights about how attackers are evolving to bypass existing security defenses today. Inaddition, our data-driven systems provide new solutions for online services to gain a deep understanding of their users, and defend them from emerging attacks and abuse