scholarly journals Trajectory Based Location Prediction and Enriched Ontological User Profiles for Efficient Website Recommendation

2020 ◽  
Vol 9 (3) ◽  
pp. 250-257
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
User Profile ◽  
User Profiling ◽  
Location Prediction ◽  
Semantic Features ◽  
User Profiles ◽  
Search Results ◽  
Dynamic Location ◽  
Prediction Approach

The spread over of huge amount of information in the vast area of internet makes difficult for the users to obtain the search items that are relevant to them. The adoption of web usage mining helps to discover the accurate search results that satisfy their requirements. To fulfill their need, it is necessary to know their preferences of search at various contexts. In general, the user profiles are used to determine the taste of the users. The traditional method of user profiling does not provide a complete detail regarding their search. In addition, the search preference of the individuals varies in accordance with time and location. The user profiles do not update the dynamic location changes of the users. The traditional location based recommendation systems suggest the search results based on their location to compensate the dynamic preferences of the users. The drawbacks of the conventional systems are resolved by the Location and User Profile (LUP) based recommendation system. To attain a higher user satisfaction by providing accurate search results, a trajectory based location prediction and enriched ontological user profiles to recommend the appropriate websites to the users is proposed in this paper. In this article, we suggest a novel method for predicting the location of a user's profile using Semantic Trajectory Pattern (STP), based on both the place and semantic features of user trajectories. Our prediction model 's central concept is based on a novel cluster-based prediction approach that evaluates the location of user search data based on the regular activities of related users in the same cluster, calculated by evaluating the typical behavior of users in semantic trajectories. The combination of location information along with enriched ontological user profiles improves the efficiency of the proposed web recommendation system. The experimental results are evaluated using recall, precision and F-measure metrics.

Feature weighted clustering for user profiling

2017 ◽  
Vol 08 (04) ◽  
pp. 1750056 ◽  
Author(s):  
Ayse Cufoglu ◽  
Mahi Lohi ◽  
Colin Everiss
Keyword(s):  
Clustering Algorithm ◽  
Personal Information ◽  
Clustering Algorithms ◽  
User Profile ◽  
User Profiling ◽  
User Profiles ◽  
Related Information ◽  
Weighted Clustering ◽  
Feature Weight ◽  
Feature Values

Personalization is the adaptation of the services to fit the user’s interests, characteristics and needs. The key to effective personalization is user profiling. Apart from traditional collaborative and content-based approaches, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, they are not able to achieve accurate user profiles. In this paper, we present a new clustering algorithm, namely Multi-Dimensional Clustering (MDC), to determine user profiling. The MDC is a version of the Instance-Based Learner (IBL) algorithm that assigns weights to feature values and considers these weights for the clustering. Three feature weight methods are proposed for the MDC and, all three, have been tested and evaluated. Simulations were conducted with using two sets of user profile datasets, which are the training (includes 10,000 instances) and test (includes 1000 instances) datasets. These datasets reflect each user’s personal information, preferences and interests. Additional simulations and comparisons with existing weighted and non-weighted instance-based algorithms were carried out in order to demonstrate the performance of proposed algorithm. Experimental results using the user profile datasets demonstrate that the proposed algorithm has better clustering accuracy performance compared to other algorithms. This work is based on the doctoral thesis of the corresponding author.

scholarly journals User profiling for web personalization using multi agent and DBSCAN based approach

2018 ◽  
Vol 7 (2) ◽  
pp. 849
Author(s):  
Sipra Sahoo ◽  
Bikram Kesari Ratha
Keyword(s):  
User Profile ◽  
Storage Management ◽  
User Profiling ◽  
Web Pages ◽  
Multi Agent System ◽  
User Profiles ◽  
Web Page ◽  
Web Personalization ◽  
Reference Ontology ◽  
Multi Agent

The user experience is enhanced by the Web Personalization System (WPS), which depends on the User's Interests (UI) and references are stored in the User Profile (UP). The profiles should be able to adapt and reproduce the change of user’s behavior for such system. Existing web page Recommendation Systems (RS) are still limited by several problems, some of which are the problem of recommending web pages to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and the problem of over-specialization. In this paper, the UI has been tracked and Dynamic User Profiles have been maintained by introducing a method called Density-Based Spa-tial Clustering of Applications with Noise-User Profiling (DBSCAN-UP). The mapping web pages, construct the ontological concepts, which represent the UI, and the interests of users are learned by the reference ontology, which are used to map the visited web pages. The process of storage, management and adaptation of UI is facilitated by multi-agent system. The different user browsing behaviors learning and adapting capability is built in the proposed system and the efficiency of the DBSCAN-UP model is evaluated by the series of experi-ments. The accuracy of the DBSCAN-UP was achieved up to 5% compared to the existing methods.

Web Application for User Profiling

2016 ◽  
Vol 8 (2) ◽  
pp. 44-56
Author(s):  
Taous Iggui ◽  
Hassina Nacer ◽  
Youcef Sklab ◽  
Taklit Ait Radi
Keyword(s):  
Natural Language Processing ◽  
Information Extraction ◽  
Language Processing ◽  
Web Application ◽  
User Profile ◽  
User Profiling ◽  
Good Precision ◽  
User Profiles ◽  
Extraction Techniques ◽  
Novel Approach

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).

scholarly journals User tag weight assessment based on fuzzy theory in mobile social networks

2021 ◽  
Author(s):  
Min Gao ◽  
Li Xu ◽  
Xiaoding Wang ◽  
Xinxin Zhang
Keyword(s):  
Recommendation System ◽  
Fuzzy Theory ◽  
Single Point ◽  
User Profile ◽  
Business Value ◽  
Personalized Recommendation ◽  
User Profiles ◽  
Mobile Social Networks ◽  
Mobile Social Network ◽  
Initial Weight

Abstract Mobile social network supports mobile communication and asynchronous social networking. For enterprises, how to provide better services and create greater business value through the data and information provided by users is crucial. For example, enterprises need to build user profiles to achieve personalized recommendation and precision marketing. In view of the data modeling stage of user profile, we propose a method to evaluate user tag weight, which includes two steps. Specifically, we introduce fuzzy theory to get the initial weight interval. Then, genetic algorithm with single point crossover is used to optimize user tag weight. Experiment results show that the proposed method has better performance than other three methods applied to recommendation system.

scholarly journals THE RESEARCH TO THE ROBUSTNESS OF RECOMMENDATION SYSTEMS WITH COLLABORATIVE FILTERING TO INFORMATION ATTACKS

2019 ◽  
pp. 95-104
Author(s):  
Yelyzaveta Meleshko ◽  
Vitaliy Khokh ◽  
Oleksandr Ulichev
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
User Profile ◽  
User Profiles ◽  
Injection Attacks ◽  
User Reputation ◽  
General Method

In this article research to the robustness of recommendation systems with collaborative filtering to information attacks, which are aimed at raising or lowering the ratings of target objects in a system. The vulnerabilities of collaborative filtering methods to information attacks, as well as the main types of attacks on recommendation systems - profile-injection attacks are explored. Ways to evaluate the robustness of recommendation systems to profile-injection attacks using metrics such as rating deviation from mean agreement and hit ratio are researched. The general method of testing the robustness of recommendation systems is described. The classification of collaborative filtration methods and comparisons of their robustness to information attacks are presented. Collaborative filtering model-based methods have been found to be more robust than memorybased methods, and item-based methods more resistant to attack than user-based methods. Methods of identifying information attacks on recommendation systems based on the classification of user-profiles are explored. Metrics for identify both individual bot profiles in a system and a group of bots are researched. Ways to evaluate the quality of user profile classifiers, including calculating metrics such as precision, recall, negative predictive value, and specificity are described. The method of increasing the robustness of recommendation systems by entering the user reputation parameter as well as methods for obtaining the numerical value of the user reputation parameter is considered. The results of these researches will in the future be directed to the development of a program model of a recommendation system for testing the robustness of various algorithms for collaborative filtering to known information attacks.

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

scholarly journals Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Sen Zhang ◽  
Qiang Fu ◽  
Wendong Xiao
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Historical Data ◽  
User Profile ◽  
Imbalanced Learning ◽  
Sample Distribution ◽  
The Real ◽  
Adaboost Algorithm ◽  
Click Through Rate ◽  
Prediction Approach

Accurate click-through rate (CTR) prediction can not only improve the advertisement company’s reputation and revenue, but also help the advertisers to optimize the advertising performance. There are two main unsolved problems of the CTR prediction: low prediction accuracy due to the imbalanced distribution of the advertising data and the lack of the real-time advertisement bidding implementation. In this paper, we will develop a novel online CTR prediction approach by incorporating the real-time bidding (RTB) advertising by the following strategies: user profile system is constructed from the historical data of the RTB advertising to describe the user features, the historical CTR features, the ID features, and the other numerical features. A novel CTR prediction approach is presented to address the imbalanced learning sample distribution by integrating the Weighted-ELM (WELM) and the Adaboost algorithm. Compared to the commonly used algorithms, the proposed approach can improve the CTR significantly.

User logistics profiling for terminal distribution based on adaptive large adjacent search

2021 ◽  
pp. 1-13
Author(s):  
Qinghua Li ◽  
Yisong Li
Keyword(s):  
Interaction Design ◽  
Search Algorithm ◽  
Volume Growth ◽  
Route Planning ◽  
User Profiling ◽  
Accurate Information ◽  
User Profiles ◽  
Delivery Costs ◽  
Delivery Route ◽  
User Centric

With the volume growth of delivery business, terminal distribution plays a more and more important role in logistics as it faces consumers directly. User Profiling as an important tool to realize user-centric interaction design can provide more accurate information for terminal distribution. By user profiling, the design team can better understand and satisfy users and their demands for the product and service. This paper studies the problem of terminal delivery route planning considering user logistic profiles. It mainly generates user profiles from two aspects: consumers’ preference for self-pickup services and consumers’ complaint tendencies. Based on the results of user profiles, an Adaptive Large Adjacent Search algorithm is established to design the delivery route of terminal distribution and determine the appropriate delivery strategy to reduce delivery costs and improve customer satisfaction.

Characterising Usage of a Multicomponent Digital Intervention to Predict Treatment Outcomes in First-Episode Psychosis: A Cluster Analysis (Preprint)

2021 ◽  
Author(s):  
Shaunagh O'Sullivan ◽  
Lianne Schmaal ◽  
Simon D'Alfonso ◽  
Yara J Toenders ◽  
Lee Valentine ◽  
...  
Keyword(s):  
Young People ◽  
Treatment Outcomes ◽  
Social Functioning ◽  
Negative Symptoms ◽  
User Profile ◽  
First Episode Psychosis ◽  
First Episode ◽  
User Profiles ◽  
Episode Psychosis ◽  
Social Components

BACKGROUND Multicomponent digital interventions offer the potential for tailored and flexible interventions that aim to address high attrition rates and increase engagement, an area of concern in digital mental health. However, increased flexibility in usage makes it difficult to determine which components lead to improved treatment outcomes. OBJECTIVE This study aimed to identify user profiles on Horyzons, an 18-month digital relapse prevention intervention that incorporates therapeutic content and social networking, along with clinical, vocational and peer support, and to examine the predictive value of these user profiles for treatment outcomes. A secondary objective was to compare each user profile with young people receiving treatment as usual (TAU). METHODS Participants comprised 82 young people (16-27 years of age) with access to Horyzons and 84 receiving TAU, recovering from first-episode psychosis. Six-month usage data from the therapy and social networking components of Horyzons were used as features for K-means clustering for joint trajectories to identify user profiles. Social functioning, psychotic symptoms, depression and anxiety were assessed at baseline and six-month follow-up. General linear mixed models were used to examine the predictive value of user profiles for treatment outcomes, and between each user profile with TAU. RESULTS Three user profiles were identified based on system usage metrics including: (a) low usage; (b) maintained usage of social components; and (c) maintained usage of both therapy and social components. The maintained therapy and social group showed improvements in social functioning (F (2, 51) = 3.58; P = .04), negative symptoms (F (2, 51) = 4.45; P = .02) and overall psychiatric symptom severity (F (2, 50) = 3.23; P = .048) compared to the other user profiles. This group also showed improvements in social functioning (F (1, 62) = 4.68; P = .03), negative symptoms (F (1, 62) = 14.61; P = <.001) and overall psychiatric symptom severity (F (1, 63) = 5.66; P = .02) compared to TAU. Conversely, the maintained social group showed increases in anxiety compared to TAU (F (1, 57) = 7.65; P = .01). No differences were found between the low usage group and TAU on treatment outcomes. CONCLUSIONS Continued engagement with both therapy and social components might be key in achieving long-term recovery. Maintained social usage and low usage outcomes were broadly comparable to TAU, emphasizing the importance of maintaining engagement for improved treatment outcomes. Although the social network may be a key ingredient to increase sustained engagement, as users engaged with this more consistently, it should be leveraged as a tool to engage young people with therapeutic content to bring about social and clinical benefits.

A comparative study to recognize fake ratings in recommendation system using classification techniques

2021 ◽  
pp. 1-8
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Decision Tree ◽  
False Positive ◽  
Recommendation System ◽  
Target Item ◽  
User Profile ◽  
Method Performance ◽  
Classification Techniques ◽  
Attack Model ◽  
Unique Method ◽  
Decision Tree Method

The recommendation system is affected with attacks when the users are given liberty to rate the items based on their impression about the product or service. Some malicious user or other competitors’ try to inject fake rating to degrade the item’s graces that are mostly adored by several users. Attacks in the rating matrix are not executed just by a single profile. A group of users profile is injected into rating matrix to decrease the performance. It is highly complex to extract the fake ratings from the mixture of genuine profile as it resides the same pattern. Identifying the attacked profile and the target item of the fake rating is a challenging task in the big data environment. This paper proposes a unique method to identify the attacks in collaborating filtering method. The process of extracting fake rating is carried out in two phases. During the initial phase, doubtful user profile is identified from the rating matrix. In the following phase, the target item is analysed using push attack count to reduce the false positive rates from the doubtful user profile. The proposed model is evaluated with detection rate and false positive rates by considering the filler size and attacks size. The experiment was conducted with 6%, 8% and 10% filler sizes and with different attack sizes that ranges from 0%–100%. Various classification techniques such as decision tree, logistic regression, SVM and random forest methods are used to classify the fake ratings. From the results, it is witnessed that SVM model works better with random and bandwagon attack models at an average of 4% higher accuracy. Similarly the decision tree method performance better at an average of 3% on average attack model.

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