User Ranking Research Articles

Survey on Users Ranking Pattern based Trust Model Regularization in Product Recommendation

Survey on Users Ranking Pattern based Trust Model Regularization in Product Recommendation

WalkRanker: A Unified Pairwise Ranking Model With Multiple Relations for Item Recommendation

Top-N item recommendation techniques, e.g., pairwise models, learn the rank of users' preferred items through separating items into positive samples if user-item interactions exist, and negative samples otherwise. This separation results in an important issue: the extreme imbalance between positive and negative samples, because the number of items with user actions is much less than those without actions. The problem is even worse for "cold-start" users. In addition, existing learning models only consider the observed user-item proximity, while neglecting other useful relations, such as the unobserved but potentially helpful user-item relations, and high-order proximity in user-user, item-item relations. In this paper, we aim at incorporating multiple types of user-item relations into a unified pairwise ranking model towards approximately optimizing ranking metrics mean average precision (MAP), and mean reciprocal rank (MRR). Instead of taking statical separation of positive and negative sets, we employ a random walk approach to dynamically draw positive samples from short random walk sequences, and a rank-aware negative sampling method to draw negative samples for efficiently learning the proposed pairwise ranking model. The proposed method is compared with several state-of-the-art baselines on two large and sparse datasets. Experimental results show that our proposed model outperforms the other baselines with average 4% at different top-N metrics, in particular for cold-start users with 6% on average.

Who should I invite: predicting event participants for a host user

While users can interact with others online, more and more social networking services can help people to organize various offline social events, such as dinner parties and study groups, on the Internet. The hosts can invite friends or strangers to participate in their events in either manual or collaborative manner. However, such invitation manners may cost substantial time. Besides, the invitees may be uninterested or even unexpectedly contain spammers. In this paper, we aim at developing a predictive model to accurate recommend event participants. Specifically, given the host who initializes a social event, along with its event contexts, including the underlying social network, categories, and geolocations, our model will recommend a ranked list of candidate participants with the highest participation potential. We propose a feature-based matrix factorization model that optimizes pairwise errors of user rankings for training events, using six categories of features that represent the tendency of a user to attend the event. Experiments conducted on two event-based social networks Meetup and Plancast and Twitter retweet data exhibit the promising performance of our approach, together with an extensive study to analyze the factors affecting users’ event participation.

Ranking Users in Social Networks With Higher-Order Structures

PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, ignoring higher-order structures captured by motifs, subgraphs consisting of a small number of nodes in complex networks. In this paper, we propose a novel framework, motif-based PageRank (MPR), to incorporate higher-order structures into conventional PageRank computation. We conduct extensive experiments in three real-world networks, i.e., DBLP, Epinions, and Ciao, to show that MPR can significantly improve the effectiveness of PageRank for ranking users in social networks. In addition to numerical results, we also provide detailed analysis for MPR to show how and why incorporating higher-order information works better than PageRank in ranking users in social networks.

RANKuser

PurposeRecently, with a high volume of users and user’s content in Community Question Answering (CQA) sites, the quality of answers provided by users has raised a big concern. Finding the expert users can be a method to address this problem, which aims to find the suitable users (answerers) who can provide high-quality relevant answers. The purpose of this paper is to find the expert users for the newly posted questions of the CQA sites.Design/methodology/approachIn this paper, a new algorithm, RANKuser, is proposed for identifying the expert users of CQA sites. The proposed RANKuser algorithm consists of three major stages. In the first stage, folksonomy relation between users, tags, and queries is established. User profile attributes, namely, reputation, tags, and badges, are also considered in folksonomy. In the second stage, expertise scores of the user are calculated based on reputation, badges, and tags. Finally, in the third stage, the expert users are identified by extracting top N users based on expertise score.FindingsIn this work, with the help of proposed ranking algorithm, expert users are identified for newly posted questions. In this paper, comparison of proposed user ranking algorithm (RANKuser) is also performed with other existing ranking algorithms, namely, ML-KNN, rankSVM, LDA, STM CQARank, and EV-based model using performance parameters such as hamming loss, accuracy, average precision, one error, F-measure, and normalized discounted cumulative gain. The proposed ranking method is also compared to the original ranking of CQA sites using the paired t-test. The experimental results demonstrate the effectiveness of the proposed RANKuser algorithm in comparison with the existing ranking algorithms.Originality/valueThis paper proposes and implements a new algorithm for expert user identification in CQA sites. By utilizing the folksonomy in CQA sites and information of user profile, this algorithm identifies the experts.

User-level incremental conversion ranking without A/B testing

The traditional approach to build incremental conversion prediction model has to rely on A/B test results. However, under certain intense business environment, A/B testing can be limited by technical support, platform, or budget, and become not practically available. In this paper, we propose an algorithm to rank users by incremental conversions resulted from advertising effects, which is based on user’s conversion history and the output from a conversion prediction model. By appropriately defining who an active user is, this algorithm is proven to work well with real data. In case where an A/B test is not available and incremental conversion-based user targeting is desired, this algorithm offers a practical solution.

Effect of the initial configuration for user–object reputation systems

Identifying the user reputation accurately is significant for the online social systems. For different fair rating parameter q, by changing the parameter values α and β of the beta probability distribution (RBPD) for ranking online user reputation, we investigate the effect of the initial configuration of the RBPD method for the online user ranking performance. Experimental results for the Netflix and MovieLens data sets show that when the parameter q equals to 0.8 and 0.9, the accuracy value AUC would increase about 4.5% and 3.5% for the Netflix data set, while the AUC value increases about 1.5% for the MovieLens data set when the parameter q is 0.9. Furthermore, we investigate the evolution characteristics of the AUC value for different α and β, and find that as the rating records increase, the AUC value increases about 0.2 and 0.16 for the Netflix and MovieLens data sets, indicating that online users’ reputations will increase as they rate more and more objects.

Enhanced User-driven Ranking System with Splay Tree

E-learning is one of the information and communication technology products used for teaching and learning process [35]. An efficient and effective way to construct trust relationship among peer users in e-learning environment is ranking. User-driven ranking systems are based only on the feedback or rating provided by the users. In [46-48] the authors provide a variety of trust and reputation methods. Certified Belief in Strength (CBS) [45] is a novel trust measurement method based on reputation and strength. In [38] author presents a recommendation system based on the relevant feedback review to predict the user's interests, that are ranked based on the recommendations history they provide previously. Users with higher rating obtain high reputation compared to less scored users. In question answering websites like StackOverflow, new or low scored users are ignored by the community. This discourage them and their involvement with the community reduces further down, as power law states, alleged low users are pushed to the bottom of the ranking list. Avoid this condition by encouraging less reputed users and prevent them from moving further down in ranking level. Thus, low reputed users are provided with few more chances to participate actively in the e-learning environments. A splay tree is a Binary Search Tree with self-balancing skill. The splay tree brings the recently accessed item to the top of the tree, thus active users are always on the top of the tree. A splay tree is used to represent user's ranks, and to semi-splay low ranked users again in the tree thus preventing them from further drowning in the ranking list. The focus of this research work is to find and enhance low reputed users in reputation system by providing few more chances to take part actively in the e-learning environment using the splay tree. Normalized discounted cumulative gain (NDCG) acts as a decision part for identifying drowning users.

Social-Aware Movie Recommendation via Multimodal Network Learning

With the rapid development of Internet movie industry social-aware movie recommendation systems (SMRs) have become a popular online web service that provide relevant movie recommendations to users. In this effort many existing movie recommendation approaches learn a user ranking model from user feedback with respect to the movie's content. Unfortunately this approach suffers from the sparsity problem inherent in SMR data. In the present work we address the sparsity problem by learning a multimodal network representation for ranking movie recommendations. We develop a heterogeneous SMR network for movie recommendation that exploits the textual description and movie-poster image of each movie as well as user ratings and social relationships. With this multimodal data we then present a heterogeneous information network learning framework called SMR-multimodal network representation learning (MNRL) for movie recommendation. To learn a ranking metric from the heterogeneous information network we also developed a multimodal neural network model. We evaluated this model on a large-scale dataset from a real world SMR Web site and we find that SMR-MNRL achieves better performance than other state-of-the-art solutions to the problem.

Uloga finansijskih i marketing pokazatelja u funkciji rangiranja ključnih kupaca

This paper describes the importance of developing key accounts as important business customers in organizations in Serbia. The paper presents the theoretical development of the emergence of the concept to key account in organizations that emerged due to dynamic changes in the market. Financial and marketing indicators are especially explained. A survey of 15 companies was realized within the sales department in the Serbian market. Respondents were employed at two levels: key account managers and sales managers. The results of the survey show the financial and marketing indicators in the key customer department that employees consider being the most important for business results. The most important are financial indicators: annual values of delivered goods, average value of delivered goods, profitability, and settlement of liabilities. Marketing indicators for ranking key customers include: market share, number of years of cooperation, growth potential and satisfaction with business relationships. The paper summarizes how the ranking of customers in several categories is important for the company and provides guidelines in creating a strategy for different key account groups.

Supporting the selection of value-driven performance measures in maintenance services - comprehensive value assessment

Maintenance measures, such as key performance indicators should align with the strategic objectives. However, this is often not the case, as mainly the cost dimension of services is measured. The field of maintenance has evolved due to maintenance outsourcing and the rising interest of equipment providers to join the service business. There is a need for methods that improve communication between maintenance service customers and service providers to ensure that other value dimensions are recognised and measured. This paper proposes a preliminary framework for integrating comprehensive value assessment into strategic decision-making in maintenance service relationships. The empirical findings based on an online-survey emphasise the fact that several elements contribute to the value of maintenance services, but these elements are in many cases not measured. Based on the ranking of customers, a number of elements such as environmental safety, safety at work, operator knowledge, reliability, and reputation of the service provider should be included in the value assessments.

Scalable Twitter user clustering approach boosted by Personalized PageRank

Twitter has been the focus of analysis in regard to various interesting and challenging problems, one of them being clustering of its users based on their interests. There are many clustering approaches for graphs that look at either the structure or the contents of the graph. However, when we consider real-world complex data such as Twitter data, structural approaches may produce many different user clusters with similar interests. Moreover, content-based clustering approaches on Twitter data also produce inferior results because tweets have a limited number of characters and lots of garbled data. Hence, for practical applications, these clustering approaches cannot be directly used on Twitter data. In the study reported in this paper, we clustered Twitter users on the basis of their interests, looking at both the structure of the graph generated from Twitter data and the contents of the Tweets. In short, we clustered Twitter users by using an unsupervised structural approach, merging similar clusters using a content-based approach, expanding the graph and ranking users with Personalized PageRank, and determining the topic to which a cluster belongs in accordance with the hashtag frequency. The results of combining these approaches were better than those of the existing techniques and befit practical applications.

Relevance and Diversity of Images by Using Tags

Now a day, there is a growth of digital images and video archives. Some of these are very important from user point of view. The collection of these digital images may be for personal use and may be for public use. For searching images in the database, there is the need of secure, fast and efficient techniques. Tag-based image search is an effective technique to find images in the databases and in the social websites. In this paper a reranking system for image retrieval with the consideration of images relevance and diversity is proposed. Reranking of the images have done according to their visual features and semantic features. Visual features are used for detecting duplicate images in proposed method. Each user maintains several images. First sort these images by same group user ranking system. Users that have higher contribution to the given query rank higher. Then an implementation to all-user reranking on the ranked users image set and only the most relevant image from users image set is selected. These selected images compose the final retrieval results. We build an inverted structure for the image dataset to speed up the searching process.

An adapted incremental graded multi-label classification model for recommendation systems

Graded multi-label classification (GMLC) is the task of assigning to each data a set of relevant labels with corresponding membership grades. This paper is interested in GMLC for large and evolving datasets where data are collected from a possibly infinite stream. Many commercial and non-commercial websites acquire such data by giving users the opportunity to rank items any time using an ordinal scale like one-to-five star rating. Typically these collected data are sparse because users rank only a small subset of items. Websites rely on recommender systems to dynamically adapt the recommended item set for each user. Hence, the applied recommender system should remain scalable and efficient when dealing with sparse data. State-of-the-art methods related to GMLC were tested only in batch mode. Their performance in an incremental mode is not investigated, especially in presence of sparse data and concept drifts. This paper presents our proposed incremental GMLC method which answers the above challenges and can be applied to build a recommender system. This method is tested on the well-known MovieLens and Jester datasets, and it is able to adapt to concept drifts and maintain the Hamming loss at a low level.

Social influence determination on big data streams in an online social network

Social networks have become a good place to promote products and also to campaign for causes. Maximizing the spread of information in an online social network at a least cost has attracted the attention of publicist's. In general, influence user ranking methods are derived either by a network's topological features or by user features but not both. Existing Influence Maximization Problem (IMP) operates as a modification of greedy algorithms that cannot scale streaming data. Which are time consuming and cannot handle large networks because it requires heavy Monte-Carlo simulation. This is also an NP hard problem in both linear threshold and independent cascade models. Our proposed work aims to address IMP through a Rank-based sampling approach in the Map-Reduce environment. This novel technique combines user and topological features of the network enabling it to handle real-time streaming data. Our experiment of influenced rank-based sampling approach to influence maximization is compared to the greedy approach with and without sampling that exhibits an accuracy of 82%. Performance analysis in terms of running time is reduced from O(n3) to O(kn). Where `k' is the size of the sample dataset and `n' is the number of user's.

Identification of influential users by neighbors in online social networks

Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors’ role for each user, a two-level method is proposed for both detecting users’ influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects.

CUSTOMER RELATIONSHIP MANAGEMENT SCALE FOR THE B2C MARKET: A CROSS-CULTURAL COMPARISON

ABSTRACT Purpose: The objectives of this study were to validate the Customer Relationship Management Scale (CRMS) in France, and to compare the French model to both Brazilian and American ones. Originality/gap/relevance/implications: Based on the premise that scientific measurement instruments may be used to reflect customers' perception about the organization actions and effectiveness, it is important to validate a scale within a multidimensional cultural context. Therefore, the applicability of the instrument shall be possible in different contexts, longitudinally, with diverse subjects, thus providing external validity and generalization. Key methodological aspects: This is a descriptive, instrumental, quantitative, cross-sectional survey where we used the Customer Relationship Management Scale (CRMS). The sampling method was non-probabilistic convenience and the total of answered questionnaires added up to 454. We carried out a quantitative research through Exploratory and Confirmatory Factor Analysis. Summary of key results: The results obtained in the analyses allow us to conclude that the relation between clients and companies is really two-dimensional and it involves two distinct factors, namely Loyalty and Customer Service. The scale validated in Brazil and in the United States remained stable, in terms of validity (quality of items) and reliability, when validated in a distinct context, that is, France. This makes its application in French organizations possible, improving its external validity and generalization. Key considerations/conclusions: The main objective of this study was reached and an instrument to assess what aspects French customers rank as relevant regarding CRM was produced showing theoretical consistency, reliability and construct validity as well.

User Vitality Ranking and Prediction in Social Networking Services: A Dynamic Network Perspective

Social networking services have been prevalent at many online communities such as Twitter.com and Weibo.com, where millions of users keep interacting with each other every day. One interesting and important problem in the social networking services is to rank users based on their vitality in a timely fashion. An accurate ranking list of user vitality could benefit many parties in social network services such as the ads providers and site operators. Although it is very promising to obtain a vitality-based ranking list of users, there are many technical challenges due to the large scale and dynamics of social networking data. In this paper, we propose a unique perspective to achieve this goal, which is quantifying user vitality by analyzing the dynamic interactions among users on social networks. Examples of social network include but are not limited to social networks in microblog sites and academical collaboration networks. Intuitively, if a user has many interactions with his friends within a time period and most of his friends do not have many interactions with their friends simultaneously, it is very likely that this user has high vitality. Based on this idea, we develop quantitative measurements for user vitality and propose our first algorithm for ranking users based vitality. Also, we further consider the mutual influence between users while computing the vitality measurements and propose the second ranking algorithm, which computes user vitality in an iterative way. Other than user vitality ranking, we also introduce a vitality prediction problem, which is also of great importance for many applications in social networking services. Along this line, we develop a customized prediction model to solve the vitality prediction problem. To evaluate the performance of our algorithms, we collect two dynamic social network data sets. The experimental results with both data sets clearly demonstrate the advantage of our ranking and prediction methods.

Analyzing social choice and group ranking of online games for product mix innovation

With the increasing populations of the smartphones and mobile devices these years, the majority of people have played the mobile or online games on their smartphones. As such users have rapidly increased, this research is to propose an innovative service that can automatically recommend the users the appropriate online games as their optional references. In order for market competition, the most online games’ enterprises need to develop and sell the newer online game products or versions continuously for the customers who like to play online games. Now that the types and markets of online games have had the more diverse along with the sustainable development of online game products over time. This research is to propose an integrative analysis from a large amount of data -users’ preference sequences. Firstly, the research utilizes a sequence recommendation technology that can be used to analyze their preference sequences of online game’s types based on other similar users’ sequence preference data. In other words, these recommended online game’s types that can be generated according to the relationships between the query user’s preferences and users themselves. All sequences from numerous data are accumulated to be the inferences of the query users’ preferences. Secondly, the research also utilizes data mining methodology to explore the possibilities of different product mix of game types from numerous user data. For the main methodology, this research uses a ‘partial user ranking algorithm’ to analyze group ranking of online games based on similar users. The purpose is to explore how the players can be recommended based on the other users’ priorities, as well as the product mix of game types which can be the potential business trends come to markets. Furthermore, this study contributes to a research implication for online game industry, and the results also could be the business references of game combinations and product design.

How developers micro-optimize Android apps

Abstract Optimizing mobile apps early on in the development cycle is supposed to be a key strategy for obtaining higher user rankings, more downloads, and higher retention. In fact, mobile platform designers publish specific guidelines, and tools aimed at optimizing apps. However, little research has been done with respect to identifying and understanding actual optimization practices performed by developers. In this paper, we present the results of three empirical studies aimed at investigating practices of Android developers towards improving the performance of their apps, by means of micro-optimizations. We mined change histories of 3513 apps to identify the most frequent micro-optimization opportunities in 297K+ snapshots and to understand if (and when) developers implement these optimizations. Then, we performed an in-depth analysis into whether implementing micro-optimizations can help reduce memory/CPU usage. Finally, we conducted a survey with 389 open-source developers to understand how they use micro-optimizations to improve the performance of Android apps. Surprisingly, our results indicate that developers rarely implement micro-optimizations. Also, the impact of the analyzed micro-optimization on CPU/memory consumption is negligible in most of the cases. Finally, the results from the survey shed some light into why this happens as well as upon which practices developers rely upon.