Build User Profiles Research Articles

Learning-Based Models for Building User Profiles for Personalized Information Access

Learning-Based Models for Building User Profiles for Personalized Information Access

Hybrid recommendation by incorporating the sentiment of product reviews

Hybrid recommender systems utilize advanced algorithms capable of learning heterogeneous sources of data and generating personalized recommendations for users. The data can range from user preferences (e.g., ratings or reviews) to item content (e.g., description or category).Prior studies in the field of recommender systems have primarily relied on “ratings” as the user feedback, when building user profiles or evaluating the quality of the recommendation. While ratings are informative, they may still fail to represent a comprehensive picture of actual user preferences. In contrast, there are other types of feedback data that differently or complementarily represent users and their preferences, including the reviews and the sentiments encapsulated within them. Such data can reveal important parts of a user’s profile that are not necessarily correlated with user ratings, and hence, they potentially reflect a different side of the user’s profile.In this paper, we propose a novel form of hybrid recommender system, capable of analyzing the reviews and extracting their sentiments that are incorporated into the recommendation process. We used advanced algorithms to generate recommendations for users capable of incorporating additional data, such as the review sentiment. We conducted analyses and showed that sentiments of user reviews are not always highly correlated with the ratings (e.g., in music domain). This might mean that sentiment can be indicative of a different aspect of user preferences and can be used as an alternative signal of user feedback. Hence, we have used both ratings and sentiments of reviews when evaluating our proposed hybrid recommender system. We selected two common datasets for the evaluation, Amazon Digital Music and Amazon Video Games, and showed the superior performance of the proposed hybrid recommender system compared to different baselines. The comparison were made in two evaluation scenarios, namely, when the ratings were considered the user feedback and when sentiments of the review were considered the user feedback.

A pro-government disinformation campaign on Indonesian Papua

This research identifies an Indonesian-language Twitter disinformation campaign posting pro-government materials on Indonesian governance in Papua, site of a protracted ethno-nationalist, pro-independence insurgency. Curiously, the campaign does not employ common disinformation tactics such as hashtag flooding or the posting of clickbait with high engagement potential, nor does it seek to build user profiles that would make the accounts posting this material appear as important participants in a debate over Papua’s status. The campaign simply employs synchronous, duplicate posts by ostensibly distinct authors to ensure that a significant proportion of posts mentioning contentious special autonomy arrangements are pro-government. Despite lacking sophistication, the scale of this information campaign in overall Twitter discussion of special autonomy adds to concerns about the ability of pro-government actors to employ disinformation to constrict political discourse in Southeast Asia.

Towards addressing unauthorized sharing of subscriptions.

Subscription-based business is booming in recent years, especially in the entertainment sector such as video and music streaming. Usually one subscription account can be shared among family members for the convenience of subscribers. However, account sharing also creates challenges for service provider, as many account owners share their subscriptions outside of the household. The widely spread practice of unauthorized sharing causes huge revenue loss for service providers. However, service providers are very cautious to pursue violators because identifying unauthorized shared accounts is a challenging task. First, the sheer volume of unstructured and noisy data makes it prohibitive to manually process the data. Moreover, it is legitimate for family members to share an account from any location and use many devices as they want. It is tricky to differentiate between unauthorized and legitimate sharing. In this paper, we propose an efficient solution to address the account sharing problem. Based on usage log data, our solution builds user profiles by accumulating and representing geolocation and device usage information. Then we estimate the risk of unauthorized sharing by analyzing the usage pattern of each account. The proposed solution can identify a large number of shared accounts and help service providers to recoup a significant amount of lost revenue.

Combining Cluster-Based Profiling Based on Social Media Features and Association Rule Mining for Personalised Recommendations of Touristic Activities

Tourists who visit a city for the first time may find it difficult to decide on places to visit, as the amount of information in the Web about cultural and leisure activities may be large. Recommender systems address this problem by suggesting the points of interest that fit better with the user’s preferences. This paper presents a novel recommender system that leverages tweets to build user profiles, taking into account not only their personal preferences but also their travel habits. Association rules, which are mined from the previous visits of users documented on Twitter, are used to make the final recommendations of places to visit. The system has been applied to data of the city of Barcelona, and the results show that the use of the social media-based clustering procedure increases its performance according to several relevant metrics.

Demographic profile building for cold start in recommender system: A social media fusion approach

New user recommendation is a big challenge for social networks site like reddit, where user profile is not so much rich. The aim of this paper is to enhance recommendation process that reddit can used for its users. We know that twitter and Facebook generate rich user information for social media behavior analysis and research. reddit recommendation system usually implement generic recommendation for its users in order to recommend subreddit, other side of coin, Like LinkedIn, amazon have very specialized user and item recommendation system based on user detail and create effective user profile for future reference. In this work fusion twitter and Facebook dataset user profile building by using tweets and post posted on Facebook by authentic unique user, and after that we extract user profile feature and parallelly perform subreddit listing for specific user feature generated by profile building. As per the reddit rank it is the 5th most visited site of USA and in world it holds 13th rank of most visited site. Twitter has 330 million active users with rich user profile like user demography. In this paper we analyze user timeline and identify user interest using machine learning and extract keywords using TF-IDF. Given the output with this machine learning used as input for a web based reddit subreddit listing application.

Blockchain-Based Smart Advertising Network With Privacy-Preserving Accountability

In a smart advertising network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAN</i> ), a broker builds user profiles from its wealth of user data, manages advertisements for retailers, and disseminates the advertisements through multiple channels. However, the broker sometimes provides insufficient transparency explanations of advertising activities, which may result in the increasing popularity of ad-blocking software and lower advertising investments from retailers. In this paper, we propose a blockchain-based Smart Advertising Network with Privacy-preserving Accountability ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> ). Specifically, we design a composite Succinct Non-interactive Argument ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SNARG</i> ) system, that commits advertising policies as cryptographic authenticators in a smart contract. By doing so, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> is compatible with the existing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAN</i> without posing prohibitive implementation cost over the blockchain architecture. Users or retailers can require explanations of an advertising activity by sending a challenge to the smart contract. With the succinctness and privacy preservation of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SNARG</i> system, the smart contract can efficiently verify whether the challenged advertising activity follows committed advertising policies without exposing user profile privacy. If any misconduct is identified, the contract enforces public accountability on the misbehaving party by confiscating its cryptocurrency deposits. We conduct extensive experiments to provide both on-chain and off-chain benchmarks, which demonstrates the application feasibility of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SANPA</i> .

Alarm Systems in Buildings - Investigation of Effectiveness Depending on Building Type and User Profile

Alarm systems are used to warn passengers and employees in railway stations, airports, logistics facilities and administrative buildings of hazards; in this way, they are instructed to rescue themselves. A large online survey conducted in Germany in 2018 has shown that often sounding the alarm does not have the desired effect. The research presented in this article investigates dependence of an alarm effectiveness on type of building, user profile and type of the alarm system.&#x0D; In this paper, general and building-specific measures, using which the effective sounding of the alarm and optimal behaviour of the building users in the case of danger can be achieved, are presented.

Building user profiles based on sequences for content and collaborative filtering

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited.

Retrieving people: Identifying potential answerers in Community Question‐Answering

Community Question‐Answering (CQA) sites have become popular venues where people can ask questions, seek information, or share knowledge with a user community. Although responses on CQA sites are obviously slower than information retrieved by a search engine, one of the most frustrating aspects of CQAs occurs when an asker's posted question does not receive a reasonable answer or remains unanswered. CQA sites could improve users' experience by identifying potential answerers and routing appropriate questions to them. In this paper, we predict the potential answerers based on question content and user profiles. Our approach builds user profiles based on past activity. When a new question is posted, the proposed method computes scores between the question and all user profiles to find the potential answerers. We conduct extensive experimental evaluations on two popular CQA sites ‐ Yahoo! Answers and Stack Overflow ‐ to show the effectiveness of our algorithm. The results show that our technique is able to predict a small group of 1000 users from which at least one user will answer the question with a probability higher than 50% in both CQA sites. Further analysis indicates that topic interest and activity level can improve the correctness of our approach.

User Profiling Approaches, Modeling, and Personalization

The growth of the available information on the internet and the huge diversity of users give high priority to personalization. User profiling is the crucial issue for both information and service personalization. Building an automated user profile is the main challenge in developing an adaptive personalized application. Recent research works are involved in developing systems with personalizing user’s interaction. This paper examines what information is needed to be modeled for presenting various user models, how this information is collected, how the user model is represented and maintained, and finally how the user model is exploited to deliver personalized services. The paper investigates the current studies on user profiling and modeling so far. A novel classification schema is proposed for the study efforts in user profile building and usage. The proposed classification schema includes three main classes: data collection, profile representation and construction, and personalization. Finally, we debate the current research gaps found in this research area.

Recreation Use and Spatial Distribution of Use by Washington Households on the Outer Coast of Washington

Natural resources along the Outer Coast of Washington provide a variety of economic, social, and cultural benefits to the state’s residents, including tourism and recreation. Knowledge of the intensity and spatial distribution of recreation use can help inform marine spatial planning (MSP) and management of parks and marine protected areas (MPAs). A survey was funded by the State of Washington to support its MSP process and addressed visitation to the Outer Coast with emphasis on outdoor recreation activities. In 2013 and 2014, Point97 and the Surfrider Foundation conducted an Internet survey using a panel from Knowledge Networks (KN), a marketing research firm. The panel included a random sample of households in the State of Washington. In 2014, the survey was expanded to address management plan objectives of Olympic Coast National Marine Sanctuary (OCNMS). The survey covered user visitation to the Outer Coast over the past 12 months. It also solicited information on detailed recreational activities participated in by respondents over the 12-month period and on the last trip. Information on the respondent’s last trip to the Outer Coast was collected for two important reasons. First, respondents provided trip expenditure information so that expenditure profiles of visitors and their economic contributions to the local economy could be estimated. Second, respondents provided information on where, spatially, they engaged in particular types of activities during their last trip. This spatial information was used to estimate the spatial intensity of use by types of recreational activities along the Outer Coast (OC) and to estimate use in OCNMS under different spatial definitions of the OCNMS. Demographic information of the users was also collected to build user profiles, and to help understand how population changes may impact use and economic impacts. Subscribe to JPRA

Obfuscated image classification for secure image-centric friend recommendation

Image sharing is one of the most popular activities of smart citizens in recent time. People tend to frequently upload images through smart devices to express different aspects of their life with connected peers using the image sharing feature facilitated by different social media services such as Facebook, Flickr, Pinterest, and Instagram, etc. The service providers of such web services are utilizing the image features and high availability of cloud storage at low cost for providing friend recommendation service to build online smart community. However, after reports of citizens surveillance by government agencies and the celebrity photo leakage incident in iCloud, users have become concerned about their photo privacy. In addition to that, the cloud services are vulnerable to security threats and an adversary capable of breaching the security would be able to access the images residing in the cloud storage. Obfuscating images before sharing can be considered as a potential solution; however, de-obfuscating and then classifying millions of images at the service provider's end does not provide privacy guarantee and it is computationally expensive. Motivated by this scenario, we propose a practical privacy-preserving image-centric friend recommendation framework compatible with smart devices which protects the privacy of images through obfuscation and classifies obfuscated images using the deep neural network to build user profiles for friend recommendation.

A novel framework to facilitate personalized web search in a dual mode

Now days, web search engines provide good services in terms of retrieval and presentation of the information to the user. A foremost difficulty in the modern and ever growing web is the lack of user interest adaption in the process of web search. All users are presented with the same set of search engine result pages (SERPs) for a given input query string, since it follows the keyword based search. The limitation of keyword based search is (i) uncertain user needs and (ii) improper query selection. If the programmer is searching for a query “switch”, it refers to the switch statement of a programming language and for an electrical engineer, the context of search is the physical house hold switch component. In addition to that a user may fall short in choosing the proper query for search that best articulate their information need. Hence, it is evident that keyword searches have tough time to distinguish the user context over the query. A typical approach to focus on this challenge is a personalized web search strategy where the results are retrieved based on the user interest and preferences. The three different major search modules are: (i) building user profiles (ii) re-ranking the SERPs in personal mode and (iii) re-ranking the SERPs in group mode. The proposed work stands for contributing in the field of user profile construction and personalized page ranking. A new method of user model representation termed as Preference Network is constructed. The proposed system can work in both initialization and maintenance mode to build a new or update an existing model. Both the short term and long term interest are utilized to rank the SERPs. The user interest score and group interest score are computed dynamically.

Retweet Prediction in Sina Weibo Based on Entity-Level Sentiment Analysis

Sina Weibo and other microblogs have rapidly become significant means by which people communicate with the world and each other. There has been a large number of studies surrounding retweet behavior, focusing on a variety of features that may explain why people retweet certain weibo content or certain user’s weibo. However, they didn’t consider the sentiment aspect or simply use the sentencelevel sentiment analysis which is not accurate enough. In this paper, we use Wikipedia and syntactic infomation to build user profile with entity-level sentiment analysis, then use this profile to predict user retweet behavior and with other common features we try to figure out what role entitylevel emotion plays in user’s retweet bahavior. Experiments on real weibo dataset shows our approach’s effectivceness and emotion is an important factor in retweet behavior.

Web Application for User Profiling

User's profiles play an important role when information systems try to meet their needs. This work presents a novel approach to build user profiles. It is based on information extraction techniques and proceeds by iterative steps. The use of different statistic metrics, Natural Language Processing (NLP) techniques and semantic descriptions (ontologies) in the authors' approach, has provided it with a good precision degree when extracting information from texts. This has been demonstrated by an application prototype which is an automatic user profile constructor, using the texts of emails job applications (E recruitment field).

Purchase prediction using Tmall‐specific features

SummaryHistorical user activity, such as online shopping recommendations, content personalization, and advertising clicking rates, is a critical component of building user profiles to predict purchases and preferences. Alongside the rapid development of e‐commerce, purchase prediction has become an increasingly important consideration for a wide variety of retail platforms. This paper proposes a framework which combines machine learning methods with a threshold‐moving approach to predict sets of pairs (user id and brand id,) in terms of whether a certain brand is purchased by a specified user according to his or her historical activity records. Three specific feature groups are extracted: click features, purchase features, and collect‐and‐cart features using a dataset from Tmall, a Chinese business‐to‐consumer online retail platform. Next, seven user purchase prediction experiments with different combinations of the three feature groups are conducted, and the purchase prediction performance is observed. Results showed that a combination of all three feature groups, with 27 features in total, provided valuable purchase prediction contributions and performed favorably. Also, three feature groups are proved to be relative independent, and our prediction model identified the most important feature, from the collect‐and‐cart feature group. Detailed result analysis validated the effectiveness of both the extracted features and proposed machine learning methods. Copyright © 2016 John Wiley &amp; Sons, Ltd.

A possible tool for development of information security: SIEM system

The implementation of information security for governmental institutions is regulated by laws, which specify the development of complex regulation environment and implementation of information security solutions. The implementing regulation emphasizes the minimal user access rights principle, which means employees should be provided with necessary and sufficient rights to do their jobs, prescribes the implementation of a control system which limits user activities to the execution of their tasks, requires security analysis of information systems' log files of organizations handling large volume of personal data. Considering that the weakest point is the user, the most important aspect of log file processing is user activity analysis, building user profiles, identifying unusual events based on these and analyse them based on the data available about users for the organization. The paper discusses the goal, role, possibility, the importance and limitations of building user profiles instantly by analysing log files, considering efficiency, and all-inclusiveness with minimization of false alarms and administration tasks based on information available electronically about employees. By the end of the paper a cost-effective model is presented for automated user activity and profile analysis.

Recommender systems based on user reviews: the state of the art

In recent years, a variety of review-based recommender systems have been developed, with the goal of incorporating the valuable information in user-generated textual reviews into the user modeling and recommending process. Advanced text analysis and opinion mining techniques enable the extraction of various types of review elements, such as the discussed topics, the multi-faceted nature of opinions, contextual information, comparative opinions, and reviewers' emotions. In this article, we provide a comprehensive overview of how the review elements have been exploited to improve standard content-based recommending, collaborative filtering, and preference-based product ranking techniques. The review-based recommender system's ability to alleviate the well-known rating sparsity and cold-start problems is emphasized. This survey classifies state-of-the-art studies into two principal branches: review-based user profile building and review-based product profile building. In the user profile sub-branch, the reviews are not only used to create term-based profiles, but also to infer or enhance ratings. Multi-faceted opinions can further be exploited to derive the weight/value preferences that users place on particular features. In another sub-branch, the product profile can be enriched with feature opinions or comparative opinions to better reflect its assessment quality. The merit of each branch of work is discussed in terms of both algorithm development and the way in which the proposed algorithms are evaluated. In addition, we discuss several future trends based on the survey, which may inspire investigators to pursue additional studies in this area.

An analysis of consumer preferences and segmentation for mobile phone service providers in India: a multivariate approach

Consumer preferences and information on product choice behaviour can be of significant value in development processes of services. Mobile phone service providers (MSPs) always look for gaining valuable insight into consumer preferences which help them in designing their service packages accordingly. This paper analyses the applicability of conjoint analysis, a classical technique to identify consumer preferences in multi-attribute decision making, to the design of mobile phone service packages and determination of the relative importance of attributes in consumer choice processes. Further, with the introduction of mobile number portability (MNP), there is an urgency to build user profiles and develop a market plan to retain and target new customers. Thus, the study also identifies segments based on their preference pattern and choices through hierarchical cluster analysis which extracts salient and homogeneous consumer segments from the conjoint analysis output.