arXiv subjects

Michael R. Lyu

Publications and source records attributed to Michael R. Lyu.

222 records · Page 13Linked to original sources

arXiv2016

A Survey of Point-of-interest Recommendation in Location-based Social Networks

Point-of-interest (POI) recommendation that suggests new places for users to visit arises with the popularity of location-based social networks (LBSNs). Due to the importance of POI recommendation in LBSNs, it has attracted much academic and industrial interest. In this paper, we offer a systematic review of this field, summarizing the contributions of individual efforts and exploring their relations. We discuss the new properties and challenges in POI recommendation, compared with traditional recommendation problems, e.g., movie recommendation. Then, we present a comprehensive review in three aspects: influential factors for POI recommendation, methodologies employed for POI recommendation, and different tasks in POI recommendation. Specifically, we propose three taxonomies to classify POI recommendation systems. First, we categorize the systems by the influential factors check-in characteristics, including the geographical information, social relationship, temporal influence, and content indications. Second, we categorize the systems by the methodology, including systems modeled by fused methods and joint methods. Third, we categorize the systems as general POI recommendation and successive POI recommendation by subtle differences in the recommendation task whether to be bias to the recent check-in. For each category, we summarize the contributions and system features, and highlight the representative work. Moreover, we discuss the available data sets and the popular metrics. Finally, we point out the possible future directions in this area and conclude this survey.

cs.IR↗

arXiv2016

GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation

Point-of-interest (POI) recommendation is an important application in location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. However, previous POI recommendation systems model check-in sequences based on either tensor factorization or Markov chain model, which cannot capture contextual check-in information in sequences. The contextual check-in information implies the complementary functions among POIs that compose an individual's daily check-in sequence. In this paper, we exploit the embedding learning technique to capture the contextual check-in information and further propose the \textit{\textbf{SE}}quential \textit{\textbf{E}}mbedding \textit{\textbf{R}}ank (\textit{SEER}) model for POI recommendation. In particular, the \textit{SEER} model learns user preferences via a pairwise ranking model under the sequential constraint modeled by the POI embedding learning method. Furthermore, we incorporate two important factors, i.e., temporal influence and geographical influence, into the \textit{SEER} model to enhance the POI recommendation system. Due to the temporal variance of sequences on different days, we propose a temporal POI embedding model and incorporate the temporal POI representations into a temporal preference ranking model to establish the \textit{T}emporal \textit{SEER} (\textit{T-SEER}) model. In addition, We incorporate the geographical influence into the \textit{T-SEER} model and develop the \textit{\textbf{Geo-Temporal}} \textit{\textbf{SEER}} (\textit{GT-SEER}) model.

cs.IR↗

arXiv2015

PersisDroid: Android Performance Diagnosis via Anatomizing Asynchronous Executions

Android applications (apps) grow dramatically in recent years. Apps are user interface (UI) centric typically. Rapid UI responsiveness is key consideration to app developers. However, we still lack a handy tool for profiling app performance so as to diagnose performance problems. This paper presents PersisDroid, a tool specifically designed for this task. The key notion of PersisDroid is that the UI-triggered asynchronous executions also contribute to the UI performance, and hence its performance should be properly captured to facilitate performance diagnosis. However, Android allows tremendous ways to start the asynchronous executions, posing a great challenge to profiling such execution. This paper finds that they can be grouped into six categories. As a result, they can be tracked and profiled according to the specifics of each category with a dynamic instrumentation approach carefully tailored for Android. PersisDroid can then properly profile the asynchronous executions in task granularity, which equips it with low-overhead and high compatibility merits. Most importantly, the profiling data can greatly help the developers in detecting and locating performance anomalies. We code and open-source release PersisDroid. The tool is applied in diagnosing 20 open-source apps, and we find 11 of them contain potential performance problems, which shows its effectiveness in performance diagnosis for Android apps.

cs.SE↗

arXiv2015

N-Version Obfuscation: Impeding Software Tampering Replication with Program Diversity

Tamper-resistance is a fundamental software security research area. Many approaches have been proposed to thwart specific procedures of tampering, e.g., obfuscation and self-checksumming. However, to our best knowledge, none of them can achieve theoretically tamper-resistance. Our idea is to impede the replication of tampering via program diversification, and thus increasing the complexity to break the whole software system. To this end, we propose to deliver same featured, but functionally nonequivalent software copies to different machines. We formally define the problem as N-version obfuscation, and provide a viable means to solve the problem. Our evaluation result shows that the time required for breaking a software system is linearly increased with the number of software versions, which is O(n) complexity.

cs.CR↗

arXiv2015

A Privacy-Preserving QoS Prediction Framework for Web Service Recommendation

QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To facilitate such recommendations, existing studies suggest the use of collaborative filtering techniques for personalized QoS prediction. These approaches, by leveraging partially observed QoS values from users, can achieve high accuracy of QoS predictions on the unobserved ones. However, the requirement to collect users' QoS data likely puts user privacy at risk, thus making them unwilling to contribute their usage data to a Web service recommender system. As a result, privacy becomes a critical challenge in developing practical Web service recommender systems. In this paper, we make the first attempt to cope with the privacy concerns for Web service recommendation. Specifically, we propose a simple yet effective privacy-preserving framework by applying data obfuscation techniques, and further develop two representative privacy-preserving QoS prediction approaches under this framework. Evaluation results from a publicly-available QoS dataset of real-world Web services demonstrate the feasibility and effectiveness of our privacy-preserving QoS prediction approaches. We believe our work can serve as a good starting point to inspire more research efforts on privacy-preserving Web service recommendation.

cs.CR↗

arXiv2012

Response Aware Model-Based Collaborative Filtering

Previous work on recommender systems mainly focus on fitting the ratings provided by users. However, the response patterns, i.e., some items are rated while others not, are generally ignored. We argue that failing to observe such response patterns can lead to biased parameter estimation and sub-optimal model performance. Although several pieces of work have tried to model users' response patterns, they miss the effectiveness and interpretability of the successful matrix factorization collaborative filtering approaches. To bridge the gap, in this paper, we unify explicit response models and PMF to establish the Response Aware Probabilistic Matrix Factorization (RAPMF) framework. We show that RAPMF subsumes PMF as a special case. Empirically we demonstrate the merits of RAPMF from various aspects.

cs.LG↗