Yuchun Guo

Can user privacy and recommendation performance be preserved simultaneously

Abstract In online systems of videos, music or books, users’ behaviors are disclosed to the recommender systems to learn their interests. Such a disclosure raises a serious concern in the public for the leak of users’ privacy. Meanwhile, some algorithms are proposed to obfuscate users’ historical behavior records to protect users’ privacy, at the cost of degradation of recommendation accuracy. It is a common belief that such tradeoff is inevitable. In this paper, however, we break this pessimistic belief based on the fact that peoples interests are not necessarily limited to items which are geared to a certain gender, age, or profession. Based on this idea, we propose a recommendation-friendly privacy-preserving framework by introducing a privacy-preserving module between a recommender system and user side. For instance, to obfuscate a female users gender information, the privacy-preserving module adds a set of extra factitious ratings of movies not watched by the given user. These added movies are selected to be those mostly watched by male viewers but interesting the given female user. Extensive experiments show that our algorithm obfuscates users’ privacy information, e.g., gender, efficiently, but also maintains or even improves recommendation accuracy.

A differential private collaborative filtering framework based on privacy-relevance of topics

Some recent work proposed differential private collaborative filtering (DPCF) recommender systems to protect user privacy from indirect access attacks, e.g., KNN attacks. As the cost of such protection, the MAE of recommendation was retained with insignificant increase but the more focused metrics, i.e., the precision and recall of top-k recommendations, degraded unacceptable. To address this problem, we propose a DPCF framework based on privacy-relevance of topics, named DPCFT. Firstly, DPCFT works on topic-preference level which highly aggregates user behaviors and keeps the precision and recall of top-k differential private recommendations acceptable. More importantly, considering the unnoticed fact that the information leakage of some special topics worries users much more than that of other ones in terms of privacy concerns, DPCFT introduces the topic privacy-relevance level in the similarity computation and neighbor selection to impose stronger privacy protection on higher privacy-relevance topics with overall differential privacy and recommendation performance reserved. Finally, to reduce the recommendation performance cost for differential privacy, DPCFT selects the top-k recommendation items at user side further with personal topic-preference without risk of data expose. Experimental results on the MovieLens dataset verify that the proposed framework DPCFT preserves differential privacy and top-k recommendation performance simultaneously.

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.

Accurate inference of user popularity preference in a large-scale online video streaming system

With the fast growth of online video services, the service providers pursue to satisfy users’ personal preferences. Most of them have noticed the diversity of users’ preferences on video content but not that on video popularity. Only Goel et.al. [1] proved in other domains that users have different popularity preferences (PPs) and Oh et.al. [2] used the statistics of users’ PPs to improve recommendation performances. However, the statistical method to obtain users’ PPs is biased when the available historical records are so limited as that in an online video recommendation system. In this article, we characterize users’ PPs in a largescale online video streaming system from China and propose two collaborative filtering (CF) [3] based algorithms to infer users’ PPs. Compared with the statistical method, our proposed algorithms largely enhance the PP accuracy, and the enhancement gets larger with the fewer training data. Our work is beneficial for providing better personalized services. Dataset. We base our study on a a large-scale dataset from the client of PPTV, one of the largest typical online video streaming systems in China. In the dataset, we filter out the sessions shorter than 30 s where users might not be purposeful watching out of interest, and filter out the users with less than 20 records to ensure that we have enough data to evaluate the accuracy of our inference algorithm. The resulted dataset collected from March 23rd to 28th in 2011 including more than 20 thousands of movie videos, 90 thousands of users and more than 2 million of sessions. Characterization. We assign each user a PP sequence whose elements are the ordered popularity rankings of each video one has watched yet. We characterize an individual user’s PP sequence with the respective of three statistical terms: central tendency (measured by Median), dispersion tendency (by coefficient of variation (CV)) and skewness (by a normalized metric defined to be (Mean−Median)/Standard Deviation). These three characteristics above complement each other. Any single one, such as only the central tendency examined in literature [1], would be not enough to characterize the users’ PPs. To examine whether the users’ PPs are homogenous, we compare the distributions of the three PP characteristics in the real dataset and those in a null model which assumes that the users select the videos at a probability proportional to the video’s popularity homogeneously. We find the observations as below. (i) Most real users in PPTV prefer the popular videos averagely but not as significantly as that assumed in the null model, as shown in Figure 1(a). Such a gap is different in different systems. For example, the majority of users in Netflix (a movie rental system), as shown in Figure 5(a) in the literature [1], averagely prefers more popu-

A novel user behavioral aggregation method based on synonym groups in online video systems

创新点在线视频服务系统的激增蕴藏着巨大的商业利益。用户个人偏好,性别,年龄等信息对于个性化服务及广告推荐至关重要。针对在线视频系统中,用户行为数据的严重稀疏性及用户偏好的最大化保留问题,本文提出了一种新的基于同义词组的用户行为汇聚方法,并利用汇聚结果对用户进行了性别预测。与现有的方法相比,本文方法有效地降低了数据的稀疏性,极大地减少了用户偏好信息损失,并进一步提高了性别预测的准确性。