Reducing seed noise in personalized PageRank

Personalized PageRank (PPR) based measures of node proximity have been shown to be highly effective in many prediction and recommendation applications. The use of personalized PageRank for large graphs, however, is difficult due to its high computation cost. In this paper, we propose a Locality-sensitive, Re-use promoting, approximate personalized PageRank (LR-PPR) algorithm for efficiently computing the PPR values relying on the localities of the given seed nodes on the graph: (a) The LR-PPR algorithm is locality sensitive in the sense that it reduces the computational cost of the PPR computation process by focusing on the local neighborhoods of the seed nodes. (b) LR-PPR is re-use promoting in that instead of performing a monolithic computation for the given seed node set using the entire graph, LR-PPR divides the work into localities of the seeds and caches the intermediary results obtained during the computation. These cached results are then reused for future queries sharing seed nodes. Experiment results for different data sets and under different scenarios show that LR-PPR algorithm is highly-efficient and accurate.

Personalized PageRank (PPR) is an important variation of PageRank, which is a widely applied popularity measure for Web search. Unlike the original PageRank, PPR is a node proximity measure that represents the degree of closeness among multiple nodes within a graph. It is also widely applied to diverse domains, such as information retrieval, recommendations, and knowledge discovery, due to its theoretical simplicity and flexibility. However, computing PPR in large graphs using naïve algorithms such as iterative matrix multiplication and matrix inversion is not fast enough for many of these applications. Therefore, devising efficient PPR algorithms has been one of the most important subjects in large-scale graph processing. In this paper, we review the algorithms for efficient PPR computations, organizing them into five categories based on their core ideas. Along with detailed explanations including recent advances and their applications, we provide a multifaceted comparison of the algorithms.

With the wide spread of movie platforms and the diversity in the quantity and quality of movies shown, it is difficult for Movie audiences to find related information about the movies that they may be interested in. And have contributed to finding a way to provide a list of movies that suits the user’s desire and personalization. In this study, we present an enhancement to the recommendation system by exploiting the personalized PageRank algorithm to find the most personalized candidate list of movies for the target user. PageRank Algorithm is a useful method for the recommendation task. PageRank algorithm is used to improve the representation of movies and users in the graph network in addition to ranking movies for a target user in the recommendation activity. User preferences have been calculated based on movie genres as a part of building the user profile from his history of rating (content analysis), and for each user, this preference was used to produce a dynamic initial rank value for each movie instead of the traditional static initial rank value. In addition to personalizing the target user, a new way has been presented to personalize each user with different weights according to their rating on movies, by supporting the proposed recommender system with new personalization parameters for each user. To evaluate the performance of the proposed recommender system and measure the accuracy of recommendations, precision, and recall metrics have been used. The experimental results show that there is a significant improvement in the recommendations process, which means the hybrid recommender system using personalized PageRank (HRS-PPR) is better than the traditional recommender system using personalized PageRank. The experimental results show that the proposed approach enhances the accuracy of the recommendation regarding precision and recall measures.

Suppose that we wish to know a group of proteins responsible for a certain cellular biological process. Here we propose to infer such a protein complex from a protein-protein interaction network by using a class of algorithm, which has originally been developed to achieve web page ranking that reflects user’s personal interest or context. The inference of proteins responsible for a given biological process, namely, personalized ranking of proteins is whereby performed in analogy with personalized ranking of web pages. Searching for the best approach to personalized protein ranking, we carry out a series of experiments to compare the performance between two major personalized ranking methods: the personalized PageRank algorithm and the continuous-attractor dynamics algorithm, both applied to a yeast protein-protein interaction network. Results of these comparison experiments suggest that the continuous-attractor dynamics algorithm is the most efficient for personalized protein ranking.KeywordsBioinformaticsInteractomeProtein-protein interaction networkPersonalized PageRank algorithmContinuous-attractor dynamics

Recommendation systems need to employ efficient reality mining module to find out whether social relationships extracted from a collected dataset are really meaningful in real world. Also, the recommendation should be generated by taking current contexts (e.g., location and time) account into. In this paper, we want to propose an ontology-based framework to discover real social networks from a large amount of dataset. This framework can provide a semiautomatic approach to build meaningful social networks by repeating interactions with human experts. We assume that given a dataset, we have to discover the hidden social networks which express the contextual dependencies between people. The personal context of a certain person is interrelated with those of other people, and investigated how to take into account his neighborpsilas contexts, which possibly have an important influence on his personal context. Particularly, in this research project, the target dataset in this work is collected from user activities (about over two millions of people) in telecommunication industry. We have applied the proposed system to discover the social networks among the mobile subscribers to provide recommendation to mobile devices.

Workflow model has been successfully applied to the traditional computing environment. Currently, there are many studies trying to apply workflow to ubiquitous computing environment. In ubiquitous computing environment, a service must be based on the user's context information to be executed. Now, however, there is no workflow system has a pervasive computing environment to support context-aware services. We intend to establish a pervasive computing environment which can be used in smart home workflow model. The system model based on the user's current context information to provide services for the user's current context, and use the Petri net model tools for workflow modeling. Thus parallel to the workflow model and to visualize formal verification correctness. Workflow using Petri net model which is a graphical and mathematical sets in one, is an ideal tool for formal modeling. We intend to apply the system model to a specific smart home, to validate the correctness of our proposed system.

We analyze preferences and the reading flow of users of a popular Austrian online newspaper. Unlike traditional news filtering approaches, we postulate that a user's preference for particular articles depends not only on the topic and on propositional contents, but also on the user's current context and on more subtle attributes. Our assumption is motivated by the observation that many people read newspapers because they actually enjoy the process. Such sentiments depend on a complex variety of factors. The present study is part of an ongoing effort to bring more advanced personalization to online media. Towards this end, we present a systematic evaluation of the merit of contextual and non-propositional features based on real-life clickstream and postings data. Furthermore, we assess the impact of different recommendation strategies on the learning performance of our system.

In recent years, numerous sensing devices and wireless networks are immersed into our living environments, creating the Internet of Things (IoT) integrating the cyber and physical objects. Searching for objects in IoT is a challenging problem because the context relationships among IoT objects are various and complex. The traditional web search approaches cannot work well in the IoT search domain because they miss the critical characteristics of the context relationships. In addition, a user's dynamic and changing context affects the user's information needs, and an IoT search system should exploit context relationship in IoT for retrieving relevant information suitable for the user's current context. In this paper, we present a context-aware search system for IoT, which aims to search objects and related information with more suitable results. We construct a hierarchical context model based on ontology to represent the IoT objects and their contextual relationships. Then, searches are executed with consideration of users' context that is recognized by a context-aware hidden Markov model. Experimental results confirmed that users could obtain more suitable and reasonable search results than with a typical web or map search system.

With continuous increase of available online spatial data, requirements for the adaptation of spatial content to user's context becomes increasingly important. Location-based services (LBS) deliver to the user information specific to a given location preferably using mobile devices. Personalizing the information provided to the user to take into account her/his information preferences and current location, the problem of inundating the user with irrelevant information is avoided. The research presented in this paper introduces an approach which implicitly monitors the user's activity and generates a user profile reflecting her/his information preferences based on the interactions of the user with the system, and her/his physical location and movements. The system recognizes the user profile and adapts accordingly both to provide suitable information, and to prioritize the functionality appropriate to the user's current context.

The vision of pervasive environments is being realized more than ever with the proliferation of services and computing resources located in our surrounding environments. Identifying those services that deserve the attention of the user is becoming an increasingly-challenging task. In this paper, we present an adaptive multi-criteria decision making mechanism for recommending relevant services to the mobile user. In this context, “Relevance” is determined based on a user-centric approach that combines both the reputation of the service, the user's current context, the user's profile, as well as a record of the history of recommendations. Our decision making mechanism is adaptive in the sense that it is able to cope with users' contexts that are changing and drifts in the users' interests, while it simultaneously can track the reputations of services, and suppress repetitive notifications based on the history of the recommendations. The paper also includes some brief but comprehensive results concerning the task of tracking service reputations by analyzing and comprehending Word-of-Mouth communications, as well as by suppressing repetitive notifications. We believe that our architecture presents a significant contribution towards realizing intelligent and personalized service provisioning in pervasive environments.

Context-aware recommender systems have been widely investigated in both academia and industry because they can make recommendations based on a user's current context (e.g., location, time). However...

BackgroundThere has been a surge in the development of apps that aim to improve health, physical activity (PA), and well-being through behavior change. These apps often focus on creating a long-term and sustainable impact on the user. Just-in-time adaptive interventions (JITAIs) that are based on passive sensing of the user’s current context (eg, via smartphones and wearables) have been devised to enhance the effectiveness of these apps and foster PA. JITAIs aim to provide personalized support and interventions such as encouraging messages in a context-aware manner. However, the limited range of passive sensing capabilities often make it challenging to determine the timing and context for delivering well-accepted and effective interventions. Ecological momentary assessment (EMA) can provide personal context by directly capturing user assessments (eg, moods and emotions). Thus, EMA might be a useful complement to passive sensing in determining when JITAIs are triggered. However, extensive EMA schedules need to be scrutinized, as they can increase user burden.ObjectiveThe aim of the study was to use machine learning to balance the feature set size of EMA questions with the prediction accuracy regarding of enacting PA.MethodsA total of 43 healthy participants (aged 19‐67 years) completed 4 EMA surveys daily over 3 weeks. These surveys prospectively assessed various states, including both motivational and volitional variables related to PA preparation (eg, intrinsic motivation, self-efficacy, and perceived barriers) alongside stress and mood or emotions. PA enactment was assessed retrospectively via EMA and served as the outcome variable.ResultsThe best-performing machine learning models predicted PA engagement with a mean area under the curve score of 0.87 (SD 0.02) in 5-fold cross-validation and 0.87 on the test set. Particularly strong predictors included self-efficacy, stress, planning, and perceived barriers, indicating that a small set of EMA predictors can yield accurate PA prediction for these participants.ConclusionsA small set of EMA-based features like self-efficacy, stress, planning, and perceived barriers can be enough to predict PA reasonably well and can thus be used to meaningfully tailor JITAIs such as sending well-timed and context-aware support messages.

This paper considers a context similarity algorithm based on ontology semantic which recommends commodities to users by the similarity between current user's context and source context recorded. First, we divide the context ontology tree into many subtrees with subtree cutting algorithm. According to calculating the similarity of subtrees with the weight sum, we obtain the similarity between the source context and target context. Meanwhile, during calculating the similarity of subtrees, this paper allows for ontology semantic similarity, and adds the structure similarity degree algorithm on the basis of the old ontology similarity degree algorithm, which makes the result more accord with actual, and finally the experiment is verified.

In this demo we present a robust system for delivering real-time news recommendation to the user based on the user's history of the past visits to the site, current user's context and popularity of stories. Our system is running live providing real-time recommendations of news articles. The system handles overspecializing as we recommend categories as opposed to items, it implicitly uses collaboration by taking into account user context and popular items and, it can handle new users by using context information. A unique characteristic of our system is that it prefers freshness over relevance, which is important for recommending news articles in real-world setting as addressed here. We experimentally compare the proposed approach as implemented in our system against several state-of-the-art alternatives and show that it significantly outperforms them.

With the increasing number of mobile devices and the development of all kinds of mobile technology, the volume of data is getting larger and larger. People have to face huge amounts of data. At the same time, as the continuous development of various mobile devices providing more data acquisition technology, the dimension of data have a bigger growth too. It's not enough for the existing technology in the treatment of huge amounts and high dimension data. User's interests in different time have been taken into equal consideration with the method being used, which leads to lack of effectiveness in the given period of time. In order to solve this problem, personalized recommendation is faced with great challenge. What's more, timeliness which is getting more and more urgent in people's need is very important. Towards the problem of recommendation based on context-aware, this paper propose a personalized recommendation algorithm based on user context clustering, with the consideration of timeliness. This algorithm uses the idea of contextual pre-filtering. A method of fuzzy clustering is used on the users' context in history data set first to construct the user set which is similar with current user context, and then combined with user-based collaborative filtering algorithm for personalized recommendation.