Mutual information based clustering of market basket data for profiling users

Attraction and commercial success of web sites depend heavily on the additional values visitors may find. Here, individual, automatically obtained and maintained user profiles are the key for user satisfaction. This contribution shows for the example of a cooking information site how user profiles might be obtained using category information provided by cooking recipes. It is shown that metrical distance functions and standard clustering procedures lead to erroneous results. Instead, we propose a new mutual information based clustering approach and outline its implications for the example of user profiling

Profiling user activities with minimal traffic traces

Understanding user behavior is essential to personalize and enrich a user's online experience. While there are significant benefits to be accrued from the pursuit of personalized services based on a fine-grained behavioral analysis, care must be taken to address user privacy concerns. In this paper, we consider the use of web traces with truncated URLs - each URL is trimmed to only contain the web domain - for this purpose. While such truncation removes the fine-grained sensitive information, it also strips the data of many features that are crucial to the profiling of user activity. We show how to overcome the severe handicap of lack of crucial features for the purpose of filtering out the URLs representing a user activity from the noisy network traffic trace (including advertisement, spam, analytics, webscripts) with high accuracy. This activity profiling with truncated URLs enables the network operators to provide personalized services while mitigating privacy concerns by storing and sharing only truncated traffic traces. In order to offset the accuracy loss due to truncation, our statistical methodology leverages specialized features extracted from a group of consecutive URLs that represent a micro user action like web click, chat reply, etc., which we call bursts. These bursts, in turn, are detected by a novel algorithm which is based on our observed characteristics of the inter-arrival time of HTTP records. We present an extensive experimental evaluation on a real dataset of mobile web traces, consisting of more than 130 million records, representing the browsing activities of 10,000 users over a period of 30 days. Our results show that the proposed methodology achieves around 90% accuracy in segregating URLs representing user activities from non-representative URLs

Hybrid Profiling in Information Retrieval

Abstract-One of the main challenges in search engine quality of service is how to satisfy the needs and the interests of individual users. This raises the fundamental issue of how to identify and select the information that is relevant to a specific user. This concern over generic provision and the lack of search precision have provided the impetus for the research into Web Search personalisation. In this paper a hybrid user profiling system is proposed -a combination of explicit and implicit user profiles for improving the web search effectiveness in terms of precision and recall. The proposed system is content-based and implements the Vector Space Model. Experimental results, supported by significance tests, indicate that the system offers better precision and recall in comparison to traditional search engines

The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Machine Learning of User Profiles: Representational Issues

As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction. The research described here focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (IR) problem.Comment: 6 page

WebPUM : a web-based recommendation system to predict user future movements.

Web usage mining has become the subject of exhaustive research, as its potential for Web-based personalized services, prediction of user near future intentions, adaptive Web sites, and customer profiling are recognized. Recently, a variety of recommendation systems to predict user future movements through Web usage mining have been proposed. However, the quality of recommendations in the current systems to predict user future requests in a particular Web site is below satisfaction. To effectively provide online prediction, we have developed a recommendation system called WebPUM, an action using Web usage mining system and propose a novel approach online prediction for classifying user navigation patterns to predict users’ future intentions. The approach is based on the new graph partitioning algorithm to model user navigation patterns for the navigation patterns mining phase. Furthermore, longest common subsequence algorithm is used for classifying current user activities to predict user next movement. The proposed system has been tested on CTI and MSNBC datasets. The results show an improvement in the quality of recommendations. Furthermore, experiments on scalability prove that the size of dataset and the number of the users in dataset do not significantly contribute to the percentage of accuracy

A recommender system approach for classifying user navigation patterns using longest common subsequence algorithm.

Prediction of user future movements and intentions based on the users’ clickstream data is a main challenging problem in Web based recommendation systems. Web usage mining based on the users’ clickstream data has become the subject of exhaustive research, as its potential for web based personalized services, predicting user near future intentions, adaptive Web sites and customer profiling is recognized. A variety of the recommender systems for online personalization through web usage mining have been proposed. However, the quality of the recommendations in the current systems to predict users’ future intentions systems cannot still satisfy users in the particular huge web sites. In this paper, to provide online predicting effectively, we develop a model for online predicting through web usage mining system and propose a novel approach for classifying user navigation patterns to predict users’ future intentions. The approach is based on the using longest common subsequence algorithm to classify current user activities to predict user next movement. We have tested our proposed model on the CTI datasets. The results indicate that the approach can improve the quality of the system for the predictions

A Novel Framework For User Customizable Privacy Preserving Search

The objective of the Personalized web search (PWS) is to provide an effective and efficient search results, which are tailor mode for individual user needs. we build user profiles based on user preference and these profiles are then used to re-rank the search results and rank the order of user-examined results.User privacy can be protected without affecting the personalized search quality. However, users are troubled, with exposing personal preference information to search engines has become a major limitation for profile based personalized web search.The Privacy-preserving personalized web search framework is called UPS framework which can generalize profiles for each query according to user-specific privacy requirements. .In general, there is a tradeoff between the search quality and the level of privacy protection achieved from generalization. Effective generalization algorithms namely GreedyDP and GreedyIL are used to support the runtime profiling. Experiments are conducted on real web search data show that the algorithms are effective in enhancing the stability of the search quality and avoids the unnecessary exposure of the user profile. DOI: 10.17762/ijritcc2321-8169.150313

An effective approach for personalized web search based on community-cluster analysis

The concept of Personalized Web Search is commonly used for improving the quality of web search results by identifying and facilitating different users' search needs. There are several techniques such as user profiling, content analysis, hyperlink analysis and biased PageRank algorithm that are used to achieve web personalization. User Profiling is one of the widely used techniques for personalizing web search at large scale. But it contains several technical and ethical issues such as privacy violations, inefficient use of computing resources as well. Collaborative web search is also a kind of a relatively "new concept which defines the way of optimizing/personalizing search results by using details of group of people and contributing the knowledge of all of them about web search. This paper presents the details of an alternative approach for personalizing web results by using user profiling technique with community cluster analysis of collaborative web search by adapting concept of reusability 'among web results